Short-Range Positioning Systems: Fundamentals and Advanced ... · • new context aware applications (e.g., real-time location systems, RTLS) • new market opportunities ($6 Billion

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Short-Range Positioning Systems: Fundamentals and Advanced Research

Results with Case Studies

Davide Dardari and Andrea Conti (*)

University of Bologna, Engineering Faculty, March 5-6, 2009

WiLAB –University of Bologna, ItalyDepartment of Electronics, Computer Science and Systems

(*) also with ENDIF, University of Ferrara, Italy

Short Course for Doctoral Students

([email protected], [email protected])

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D. Dardari, A. Conti, WiLAB, University of Bologna

Summary (1/2)

• Introduction– Motivations

– Localization basics

• First part (D. Dardari): Distance estimation (ranging)– Basic concepts on estimation theory

– Performance limits in Time-of-Arrival (TOA) estimation

– Ultra-wide bandwidth (UWB) signals

– Ranging with UWB signals

– Main sources of error in TOA estimation

– Practical TOA estimators

– The IEEE 802.15.4a standard

– Advanced issues

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Summary (2/2)

• Second part (A. Conti): Localization– Performance limits

– Localization schemes

– Cooperative localization

– Tracking

– Experimental results

• In addition:– Case studies

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Why localization is important

• new context aware applications (e.g., real-time location systems, RTLS)• new market opportunities ($6 Billion in 2017*)

[VICOM Project www.vicom-project.com]

Some examples:• inventory/people tracking (RFID)• surveillance/security• wireless sensor networks (WSN)• virtual immersive and augmented reality applications

* R. Das and P. Harrop ”RFID Forecast, Players and Opportunities 2007-2017”, 2007, http://www.idtechex.com.

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For example in WSNs……

Sensed data without position and time information is often meaningless

For example, in habitat environments monitored sensed events must be ordered both in time and space to permit a correct interpretation.

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In addition…

Positioning and time synchronization are also essential for basic mechanisms composing the network to work efficiently:

•MAC scheduling algorithms can reduce packet collisions•power-saving strategies (wake-up sleeping times)•networking protocols to improve performance of routing algorithms (geo-routing)•enabling interference avoidance techniques in future cognitive radios

In many schemes proper time synchronization is required to achieve high ranging accuracies in positioning techniques

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Some technologies enabling localization

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(c) VICom Project 2002-06(c) VICom Project 2002-06Location Provider

Plu

g IN

Plu

g IN

Plu

g IN

Plu

g IN

…

5-50 m5-10 m0.5 - 5 m5 cm - 50 cm beyond 30 m

GPSWLANMOTECRICKET/RF-ID Cellular

LFIntegration of different technologiesdepending on the environment

In addition, UWB-based localization (now practical)

High nodes’ density requiredHigh nodes’ density required

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• high localization accuracy (<1m) in harsh environments (e.g., indoor)

• low device complexity (low cost, low size)

• standards (e.g., IEEE802.15.4a)

Needs

But in many cases:

• nodes are GPS-denied (e.g., indoor, urban canyon)

• GPS is too expensive (e.g., WSN) and only a small fraction of nodes (anchors) have positioning information due to cost, size and power constraints

• higher accuracy than GPS is required

• no infrastructure available (anchor-free), only relative coordinates are estimated (ah hoc networks)

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Localization basics

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Localization Basics

The purpose of any localization algorithm is, given a set of measurements, to find the locations of target nodes with unknown positions.

Positioning occurs in two main steps:

1. Selected measurements are conducted between nodes2. Measurements are combined to determine the locations

of target nodes (localization algorithm).

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Ingredients for localization-aware applications

AlgorithmsAlgorithms

TechnologiesTechnologies

Position‐Based ApplicationsPosition‐Based Applications

RSSI, TOA, TDOA, AOA, …

Robustw/o memoryRange-, angle-, proximity-basedw/o cooperation

Error sourcesUncertainty

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Position estimation techniques classification (1/2)

•Anchor-based - Using GPS or using predefined coordinates, a subset of nodes in the network know their position a priori (these nodes are typically named anchors or beacons). Nodes with unknown positions (targets) use positioning information from anchors to determine their location.

Ranging between anchors and other nodes can be obtained by:- direct interaction (Single-Hop), - indirectly by means of intermediate nodes (Multi-Hop).

•Anchor-free - No nodes in the network have knowledge of their position a priori. Only relative coordinates can be found in such networks.

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•Range-based - Measurements provide distance information among nodes (*)

•Angle-based - Measurements provide angle information among nodes

•Range-free - Only connectivity information is used (*)

Position estimation techniques classification (2/2)

Other techniques

Interferometric – (low complexity, problems with multipath) (*)•Scene analysis – (e.g., based on receiver power “signature”)•Inertial – (error accumulation)•DC magnetic tracker – (accurate but expensive and low range)•Optical – (laser ranging systems, very accurate but expensive)•Hybrid – (e.g., Range-free and Inertial) (*)

(*) suitable for low cost devices (e.g., WSNs)

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Basic Concepts on

Estimation Theory

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Problem statement

Estimation theory basics

To obtain an estimate θ of the unknown parameter θbased on the observation vector r = [r1, r2, ...., rN ]

T

Note : r could eventually represent a continuos-time function r(t)through a suitable orthonormal base

estimator: θ = θ(r)

The problem is to find the estimator θ(r)which maximizes some performance metric(or minimizes a desired cost function)

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Data model

Data model (observation model)

θ deterministic unknown: p(r; θ) probability density function (PDF)

Classical estimation approach

Bayesian estimation approach

These PDFs are the laws that govern the effect of θ on the observation r

θ random, unknown: p(r, θ) = p(r|θ)p(θ), p(θ) prior information

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Square Error (SE) cost function

“Hit-or-Miss” cost function

C(²) = ²2

where ² = θ(r)− θ is the estimation error

Design Criteria for Bayesian Estimators (1/3)

Selection of a Cost Function

Recall

θ random, unknown: p(r, θ) = p(r|θ)p(θ), p(θ) prior information

C(²) =

½0 |²| ≤ ∆/21 |²| > ∆/2

Our goal is to find an estimate that minimizes

the expected value of the cost

Estimator design

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Minimum Mean Square Error (MMSE) Estimator


Minimizes the MSE

The estimation error has zero mean

Difficult to implement in the non-Gaussian case

where the expectation is with respect to the posteriori PDF

p(θ|r) =p(r|θ)p(θ)Rp(r|θ)p(θ) dθ

=p(r|θ)p(θ)

p(r)

MSE = Eθ,r©²2ª

θ(r) = Eθ|r{θ|r} =Rθ p(θ|r) dθ posteriori mean

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Maximum A Posteriori (MAP) Estimator


Minimizes the “hit-or-miss” cost function

No general formula is available

If r and θ are jointly Gaussian, it is equivalent to the MMSE estimator

θ(r) = argmaxθp(θ|r)

θ θ

p(θ|r)

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Minimum Mean Square Error (MMSE)

Design Criteria for Classical Estimators (1/6)

MSE = Ern(θ(r)− θ)2

o= Var (²) + Er{²}

Similar to the Bayesian case (here the expectation is only over r), but in general not realizable since the estimator depends on theparameter to be estimated!

We need other measures of quality of estimation process

Recall:

θ deterministic unknown: p(r; θ) PDF

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1) Impose zero bias (unbiased estimator)

Minimum Variance (MVU) Estimator

Er{²} = 0

2) Minimize the variance of estimation error

The MVU estimator does not always existWhen exists, no straightforward procedure are available to find the estimator

θ(r) = argminθ(r)

En(θ(r)− θ)2

o= argmin

θ(r)Var (²)

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The Cramer-Rao Lower Bound (CRB)

The CRB provides a minimum bound on the variance of any unbiased estimator:

CRB =³−E

n∂2 log p(r;θ)

∂θ2

o´−1

Def. Efficient estimator: if it attains the CRB for all θ


Var (²) = En(θ(r)− θ)2

o≥ CRB

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Maximum Likelihood (ML) Estimator

Not optimal in general, but straightforward

It is asymptotically efficient, i.e., asymptotically it is the MVU estimatorand the MSE tends to the CRB

If an efficient estimator exists, the MLE will produce it


θ(r) = argmaxθp(r; θ)

θθ

p(r; θ)

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Estimation of DC level in AWGN noise

This is a MVU estimator, the best we can do!

Design Criteria for Classical Estimators: example

rk = θ + nk for k = 1, 2, . . . , N

p(r; θ) = 1√2πσ2

expn−PN

k=1(rk−θ)22σ2

oE©n2kª= σ2

θ(r) = 1N

PNk=1 rk

Var (²) = Var³θ(r)

´= σ2

N

CRB = σ2

N

PDF:

ML estimator:

Performance:

CRLB:

E {²} = 0

Model:

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Least Squares (LS) Estimator

Not optimal in general, but straightforward


r = s(θ) + n

If n is Gaussian, then the LS estimator is equivalent to the ML estimator

Minimizing the LS error does not in general translate into minimizing the estimation error

No probabilistic assumptions are made about the observation

θ(r) = argminθ(r− s(θ))T ((r− s(θ))

Observation model

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Distance estimation:

Ranging

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Distance Estimation: Ranging

• Received Signal Strength (RSS)– direct RSS-distance mapping

– interferometric

• Time-Based– e.m. waves

– ultrasound

• Near field (phase difference between the E & H fields in the near range) (Q trace corporation)

Ranging between two nodes is the technique employed by two nodes in the network to determine the physical distance between them.

Node A Node B

Possible technologies

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Ranging based on RSS measurements

• Theoretical and empirical models are used to translate the difference between the transmitted signal strength and the RSS into a range estimate.

• Propagation effects (refraction, reflection, shadowing, and multipath) cause the attenuation to poorly correlate with distance

Inaccurate distance estimates

Time synch between nodes is not required

PR(dBm)

PR(dBm)

Unrealisticdeterministicchannel

Randomchannel

d d

-100-90-80-70-60-50-40-30-20-10

00,00 5,00 10,00 15,00 20,00 25,00 30,00

Distance (m)

RSS

I (db

m)

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RSS ranging: theoretical bound

•The MSE bound does not depend on signal structure•RSS ranging does not require time synchronization between nodes•No dedicated HW

Received power v.s. distance model (in dBm):

Cramer-Rao bound (CRB) for the distance estimation MSE

Note: the CRB provides a minimum bound on the variance of any unbiased estimator.

Var³d´≥

µln 10

10

σSγd

¶2.

Pr(d) = P0 − 10 γ log10 d+ S

P0 received power (in dBm) at a reference distance of 1 meterγ: path-loss exponent (typiacl values between 2 and 5)S: Gaussian random variable with zero mean and standard deviation σS (shad-owing spread). It accounts for large-scale random fluctuations (shadowing).

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Interferometric Ranging (1/2)

M. Maroti, P. Völgyesi, S. Dora, B. Kusy, A. Nadas, A. Ledeczi, G. Balogh, and K. Molnar, “Radio interferometricgeolocation,” in Proc. ACM Conference on Embedded Networked Sensor Systems, San Diego, USA, Nov. 2005, pp. 1–12.

•Transmitters A and B transmit sinusoids at slightly different frequencies

•The envelope of the received composite signal, after band-pass filtering, will vary slowly over time.

•The phase offset of this envelope can be measured using cheap low-precision RF chips, and it contains information about the difference in distance of the two links.

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Interferometric Ranging (2/2)

•To solve the unknown initial phase of the two transmitted sinusoids (no time synch is present), a similar measurement is made by node D in a different location

•The difference in phase offset measured at the two receivers only depends on the four distances. Hence, given a number of phase-offset-difference measurements, the unknown locations of the two receivers may be inferred.

Comments:

•Sub-meter precision can be achieved in outdoor environments with simple hardware•Problems with severe multi-path (e.g., indoor)

•Infrastructure required.

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Time-Based Ranging

• One-way Time-of Arrival (TOA) ranging

Node BNode A t1 t2

Node A Node B

• Two-way TOA ranging

τf =(t2−t1)+(t4−t3)

2

τf = t2 − t1

The distance information between a pair of nodes A and B (ranging) can beobtained using the measurement of the propagation delay or Time-of-Flight(TOF) τf = d/c, where d is the actual distance between A and B.

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Time-Difference-of-Arrival (TDOA) (1/3)

TDOA scheme A TDOA scheme B

Networks with infrastructure (anchor nodes) and accurate anchorssynchronization (e.g., through cable connections) are required

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A typical approach uses a geometric interpretation to calculate the intersection of two or more hyperbolas: each sensor pair gives a hyperbola which represents the set of points at a constant range difference (time-difference) from two sensors

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Networks with infrastructure (anchor nodes) and accurate anchorssynchronization (e.g., through cable connections) are required

TDOA scheme B suitable for extremely low complexity targets nodes (e.g., TAGs in UWB-RFID systems)

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All time-based ranging techniques require

time-of-arrival (TOA) estimation of the received signal

Hence, accurate TOA estimationand time measurement are required!

For example, τf = 1 ns means a distance d ≈ 30 cm

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Only an estimation of the real time t can be obtained due to node local oscillator frequency drift

Time Measurements (1/2)

Reasonable model:

T(t) = (1 + δ)t+ µ

δ: clock driftrelative to the correct rateµ: clock offset.

The rate of a perfect clock, dT(t)/dt, would equal 1 (i.e., δ = 0).

The clock performance is often expressed in terms of part-per-million (ppm)defined as the maximum number of extra (or missed) clock counts over a totalof 106 counts, i.e., δ · 106.

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Time Measurements (2/2)

Suppose that a node intends to generate a time delay of τd seconds,the effective generated delay τd in the presence of a clock drift δ would be

τd =τd1 + δ

In case a node has to measure a time interval of true durationτ = t2 − t1 seconds, the corresponding estimated value τ would be

τ = T(t2)− T(t1) = τ (1 + δ)

In both cases there is no dependence on the clock offset µ.

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One-Way TOA Ranging: effect of clock drifts

According to nodeA’s local time, the packet is transmitted at time t(A)1 = TA(t1)

(included as a timestamp in the packet) and it is received at node B’s local time

t(B)2 = TB(t2). Node B calculates the estimated propagation delay as

τf = t(B)2 − t

(A)1 = τf · (1 + δA) + t2 · (δB − δA) + µB − µA

clock offsets may be in the order of us, one-way raging tied to network synchronization

One-way ranging requires stringent network synchronization constraints whichare often not feasible in low-cost systems.

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Possible application of one-way TOA ranging

Ultrasound devices•propagation speed of acoustic waves (@340 m/s) is much lower than light-speed •synchronization errors can be several orders of magnitude smaller than the typical propagation delay values

•high positioning accuracy (3 cm)

Ultrasound technology has several disadvantages

•Hybrid technology•Propagation limited by walls (coverage)•Effect of temperature and pressure (calibration)•Power-hungry

A. Harter, A. Hopper, P. Steggles, A. Ward and P. Webster ”The anatomy of a Context-Aware Application”, In Wireless

Networks, Vol. 8, pp. 187-197, Feb. 2002.

Active Bat localization system

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Two-Way TOA Ranging: effect of clock drifts (1/2)

The effective response delay introduced by node B is τd/(1 + δB), whereas theestimated RTT denoted by τRT, according to node A’s time scale, is

τRT = 2 τf(1 + δA) +τd(1 + δA)

(1 + δB)

In absence of other information, node A derives the estimation of the propaga-tion time, τf, by equating the previous equation with the supposed round-triptime 2 τf + τd leading to an error

τf − τf = τf δA +ε τd

2(1 + δA − ε)≈

ε τd2(1 + δA − ε)

where ε , δA − δB .

τf: in the order of nanosecondsτd: in the order of microseconds/milliseconds (includes the time for packetacquisition, sync., channel estimation, etc.)

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Two-Way TOA Ranging: effect of clock drifts (2/2)

10−7

10−6

10−5

10−12

10−11

10−10

10−9

10−8

10−7

ε

Ran

ging

err

or (

s)

τd=1 µs

τd=10 µs

τd=100 µs

τd=1 ms

τd=10 ms

Low response delay must be ensured for high ranging accuracy

The ranging protocol must be implemented at PHY/MAC level

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Mitigation of clock frequency offsets

τf − τf = τf δA

Example:

• each node locally measures the (known) received packet duration and exchange its measure with the other node

• from the 2 measurements node A can evaluate a correction factorwhich applies to its estimated propagation time.

• In this case the error becomes

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Remarks

•Two-way ranging requires less stringent synchronization constraints compared to one-way ranging (relative clock drifts still affect ranging accuracy)

•Suitable for networks without infrastructure (e.g., ad hoc networks)

•Main scheme adopted in the IEEE 802.15.4a standard

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Time-of-Arrival (TOA) estimation

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Time-of-Arrival Estimation (TOA)

Consider the transmission of a single pulse p(t) in AWGN channel in the absence of other sources of error. The received signal is

Problem statement in an ideal scenario

Classical non-linear parameter estimation problem

r(t) =pEp p(t− τ) + n(t)

The problem is to obtain the best estimate for the TOA, τ , based on the receivedsignal r(t) observed over the interval [0, Tob).

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Maximum likelihood (ML) TOA estimator

The ML TOA estimator is asymptotically efficient in AWGN

matchedfilter

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The estimation Mean Square Error (MSE) of any unbiased estimation τ of τcan be bounded by the Cramer-Rao bound (CRB)

Var (τ ) = E©(τ − τ )2

ª≥ CRB

Theoretical limits in AWGN

The CRB is given by

CRB =N0/2

(2π)2Ep β2=

1

8π2 β2 SNR

where− SNR , Ep/N0− β2 represents the second moment of the spectrum P (f) of p(t) defined by

β2 ,R∞−∞ f

2|P (f)|2 dfR∞−∞ |P (f)|

2 df

− β: effective bandwidth

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Bound on ranging MSE

Increase the SNR towards very narrowband systems

To mitigate the effect of multipath in indoor environments, low frequency bands must be used not practical for small size devices due to large antennas

Increase the bandwidth towards ultra wideband (UWB) systems

Higher frequencies bands can be used. Multipath can be resolvable.Technique chosen by the IEEE 802.15.4a standard

Lower bound on ranging MSE

How to improve the accuracy?

Var³d´≥

c2

8π2 β2 SNR.

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Ultra-wide Bandwidth (UWB) Signals

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Definition of a UWB signal

UWB signal is formally defined as any signal that (FCC):

• occupies more than 500 MHz of spectrum or

• has a fractional bandwidth in excess of 20%

H L

H L

f fBF 0.2(f f ) / 2

−= ≥

+

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Brief history of UWB

• The Radio was born an UWB: spark gap transmission designs of Marconi in the late 1890s

• After the Marconi’s “4 seven” patent (1900) the radio became “tuned”

• Time-domain electromagnetics theory in early 1960s

• Short-pulse generators using tunnel diodes in early 1970s

• Applications to radar in 1970-80s

• The term “UWB” originated with the Defense Advanced Research Projects (DARPA) in a radar study undertaken in 1990

• First studies of possible application of UWB to communication systems during 1990s(Win-Scholtz)

• 2/2002 - FCC adopted the First Report and Order (RO) that permits the marketing and operation of certain types of UWB products

• 2/2007 – Implementation of an UWB radio regulatory framework for the EU

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Where do we find such a huge available bandwidth?

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Better to utilize already allocated bands using extremely

low power spectral densities (PSD)

FCC limits ensure that UWB emission levels are exceedingly small• at or below spurious emission limits for all radios• at or below unintentional emitter limits• lowest limits ever applied by FCC to any system

Part 15 limits equate to –41.25 dBm/MHz

For comparison, PSD limits for 2.4 GHz ISM and 5 GHz U-NII bands are +40 dB higher per MHz

Total emissions over several gigahertz of bandwidth are a small fraction of a milliwatt

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FCC masks

Different radiation masks were given for several UWB applications

Yes1.99 to 10.6 GHz Through-wall imaging and surveillance systems

No24 to 29 GHzVehicular radar systems

No3.1 to 10.6 GHzCommunications (indoor & outdoor) and medical systems*

Yes3.1 to 10.6 GHz Ground penetrating radars, wall imaging systems

User RestrictionsFrequency Band for Operation at Part 15 Limits

Application

*Indoor and outdoor communications devices have different out-of-band emission limits

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0.96 1.61

1.99

3.1 10.6

GPS Band

Example of indoor UWB mask

dBm/MHz

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0.96 1.61

1.99

3.1 10.6

GPS Band

Example of outdoor UWB mask (hand-held)

dBm/MHz

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EU mask

W. Hirt “The European UWB Radio Regulatory and Standards Framework: Overview and Implications”, ICUWB 2007. IEEE International Conference on Ultra-Wideband, 2007. Sept. 2007, Singapore.

A maximum mean EIRP density of -41.3 dBm/MHz is allowed in the 3.4 - 4.8 GHz bands provided that a low duty cycle restriction is applied in which the sum of all transmitted signals is less than 5% of the time each second and less than 0.5% of the time each hour, and provided that each transmitted signal does not exceed 5 milliseconds. Limits can be overcome using appropriate mitigation techniques. “10 second” rule introduced.

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0 1 2 3 4 5 6 7 8 9 10 11Frequency [GHz]

−90

−80

−70

−60

−50

−40

EIR

P d

ensi

ty [d

Bm

/MH

z]

FCC maskEU mask

Comparison between FCC and EU masks

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Worldwide regulations

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Why do we need UWB systems?

Motivations Main issues

•High temporal resolution (localization, multipath)

•High material penetration (according to the band used)

•Underlay technology (efficient spectrum usage)

•Multiple access

•Low probability of detection (LPD)

•Keep complexity low

•Large bandwidth antennas

•Interference management (coexistence)

Narrowband (30kHz)

Wideband CDMA (5 MHz)

UWB (several GHz)

Frequency

Part 15 Limit

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How to generate a UWB modulated signal

Conventional techniques over sinusoidal carrier

such as OFDM, DS-CDMA, FH-CDMA

Impulse radio

Base-band transmission of short pulses (monocycles)

Higher complexity

Lower complexity

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UWB Taxonomy

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Impulse Radio - UWB

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Impulse Radio UWB (IR-UWB)

Base-band generation of the signal using short duration pulses

•Low complexity (no RF and IF stages)

•Low consumption

•New antenna design criteria

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• Gaussian nth derivative monocycle

−0.5 −0.4 −0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 0.4 0.5time (ns)

−4

−3

−2

−1

0

1

2

3

4

Am

plitu

de

0 1 2 3 4 5 6 7 8 9 10 11 12Frequency (GHz)

−50

−40

−30

−20

−10

0

10

norm

aliz

ed P

SD

[dB

]

normalized FCC maskpulse spectrum

The Gaussian monocycle

Example of Gaussian

6th derivative monocycle Spectrum

p0(t) = exp (−2π(t2/τ2p )) p(t) = p

(n)0 (t)

r(n−1)!

(2n−1)!πnτ (1−2n)p

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Example of simple impulse generator

From J.S. Lee, C. Nguyen e T. Scullion, IEEE Microwave Theory and Tech. 2001

Bit 0

Bit 0

Bipolar version realized at WiLAB – University of Bologna

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IR-UWB: signaling

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UWB signals are typically modulated pulse trains (low duty cycle)

At each bit several pulses (100-1000) are transmitted according to a certain “randomization” technique such as Time-hopping (TH) or Direct Sequence (DS) to allow multiple access (each user adopts a different “code”)

Modulation techniques include pulse-position modulation (PPM),pulse amplitude modulation (PAM) and others

Tb

δ Tf

IR-UWB: signaling

Randomized time hopping

PPM signaling

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Example: TH-PPM Impulse Radio

Pseudorandom Time Hopping coding (TH)

Tf : frame time

δ : modulation index

Ns: # pulses/symbol

Cj(k): pseudo-random

(PR) sequence(ex:1,3,6,2,…,6)

d: data bits

Tc: chip time

( )s

(k ) (k ) (k )f j c j/ N

js (t) p t jT c T d

∞

⎢ ⎥⎣ ⎦=−∞

= − − − δ∑Transmitted signalK-th user

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UWB propagation

and channel models

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The UWB channel

TxUWB

Rx UWB

Direct path

Multi-path

Multipath propagation

Narrowband channel:

Wideband channel

Ultra wideband channel

Fading large link budget margin

Echoes signal distortion

Single paths are typically not resolvable, i.e., echoes amplitudes exhibit fading

Echoes signal distortion

Single paths may be resolved potential diversity gain

g(t): channel gainh(t): channel impulse responses(t): transmitted signalr(t): received signal

r(t) = g(t) · s(t)

r(t) = h(t)⊗ s(t)

r(t) = h(t)⊗ s(t)

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UWB Propagation Experiment

• Modern building with officesand labs

• Multipath profilesT = 300ns

• 14 rooms 49 equidistant points

• 7 x 7 measurement gridwith 90 cm side

Moe Z. Win, and Robert A. Scholtz, “Characterization of Ultra-Wide Bandwidth Wireless Indoor Channels: A Communication-Theoretic View”, IEEE Journal On Selected Areas In Communications, Vol. 20, No. 9, December 2002

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Room F1

LOS high SNR

Room P

NLOS high SNR

Room H

NLOS medium SNR

Room B

NLOS low SNR

Example of channel impulse responses

In general, dense multipath is present composed of tens or hundreds paths

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Channel model for dense multipath: IEEE 802.15.4a

The IEEE 802.15.4a UWB channel model is based on an extended version of the classical Saleh-Valenzuela (SV) indoor channel model, where multipathcomponents arrive at the receiver in groups (clusters) following the Poisson distribution. According to the SV model, the complex baseband channel impulse response is given as

The Power Delay Profile (PDP) is assumed exponential negative within each cluster

h0(t) =PK

k=0

PLl=0 ak,le

jφk,lδ (t− Tk − τk,l) ,

ak,l: amplitude of the lth path in the kth clusterTk: delay of the kth clusterτk,l: delay of the lth path relative to the kth cluster arrival time Tkφk,l are uniformly distributed in the range [0, 2π)The number of clusters K is assumed to be Poisson distributed.The ray arrival times τk,l are modeled with mixtures of two Poisson processesThe small-scale fading, which characterizes the path amplitudes ak,l, follows aNakagami-m distribution

Λk,l = En|ak,l|

2o∝ exp(−τk,l/²k) ²k is the intra-cluster decay time constant.

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Simplified channel model for lower frequencies

Another widely adopted model is the dense multipath model with a single cluster composed of L independent equally spaced paths and exponential PDP. In this case the real impulse response is given by

Exponential negative PDP

h(t) =PL

l=0 al pl δ (t− τl)

τl = τ1 + (l − 1)∆∆: resolvable time intervalal: path amplitude with Nakagami-m statisticspl is a r.v. which takes, with equal probability, the values {−1,+1}

Λl = En|al|

2o= (e∆/²−1)e−∆(l−1)/²

e∆/²(1−eL∆/²) ²: decay time constant.

A. F. Molisch, D. Cassioli, C.-C. Chong, S. Emami, A. Fort, B. Kannan, J. Karedal, J. Kunisch, H. Schantz, K. Siwiak,and M. Z. Win, “A comprehensive standardized model for ultrawideband propagation channels,” IEEE Trans. AntennasPropag., vol. 54, no. 11, pp. 3151–3166, Nov. 2006, Special Issue on Wireless Communications.

39

77


Ranging using UWB signals

78


Impulse Radio UWB (IR-UWB)

Example of Gaussian

6th derivative monocycle

Base-band generation

of the signal

•Low complexity (no RF and IF stages)

•Low consumption

Spectrum

−0.5 −0.4 −0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 0.4 0.5time (ns)

−4

−3

−2

−1

0

1

2

3

4

Am

plitu

de

0 1 2 3 4 5 6 7 8 9 10 11 12Frequency (GHz)

−50

−40

−30

−20

−10

0

10

norm

aliz

ed P

SD

[dB

]

normalized FCC maskpulse spectrum

40

79


Theoretical Performance Limits

Theoretical performance limits (bounds) serve as useful benchmarks in assessing the performance of newly developed TOA estimation techniques

The performance of the TOA estimator, like all non-linear estimators, is characterized by the presence of distinct SNR regions corresponding to different modes of operation:

- Low SNR region (a priori region), signal observations provide little new information- High SNR region (asymptotic region), the MSE is accurately described by the CRB- Medium SNR (transition region or ambiguity region), observations are subject to ambiguities that are not accounted for by the CRB

This behaviour is referred to as the threshold effect

80


Improved bounds

Unfortunately, the CRB is not accurate at low and moderate SNR and/or when the observation time is short improved bounds

The Ziv-Zakai Lower Bound

The Ziv-Zakai bound (ZZB) can be applied to a wider range of SNRs, but more complex than CRB for analytical evaluation

41

81


The Ziv-Zakai Lower Bound

where Pmin (τ ) is the error probability of the classical binary detection schemewith equally probable hypothesis:

H1 : r(t) ∼ p {r(t)|τ}

H2 : r(t) ∼ p {r(t)|τ + z}

When τ is uniformly distributed in [0, Ta), the ZZB is

ZZB =1

Ta

Z Ta

0

z (Ta − z)Pmin (z) dz

In AWGN the minimum attainable probability of error becomes

Pmin (z) = Q

µqSNR (1− ρp(z))

¶where Q (·) is the Gaussian Q-function and ρp(z) is the autocorrelation functionof p(t).

82


0 5 10 15 20 25 30 35 40SNR (dB)

10-13

10-12

10-11

10-10

10-9

10-8

10-7

RM

SE

(s)

CRB, RRC, τp=3.2 ns

ZZB, RRC, τp=3.2 ns

CRB, RRC, τp=1 ns

ZZB, RRC, τp=1 ns

CRB, Gauss. deriv., n=2

ZZB, Gauss. deriv., n=2

CRB, Gauss. deriv., n=6

ZZB, Gauss. deriv., n=6

TOA estimation theoretical limits using UWB pulses

From D. Dardari, C.-C. Chong, and M. Z. Win, “Improved lower bounds on time-of-arrival estimation error in realistic UWB channels,” in IEEE International Conference on Ultra-Wideband, ICUWB 2006, (Waltham, MA, USA), pp. 531–537, Sept. 2006.

CRB and Ziv-Zakai Bound in AWGN (single path)

30 cm

3 cm

3 mA priori RMSE Ta/

√12 A priori region

Asymptotic region

Ambiguity region

42

83


Main sources of error

in TOA Ranging

84


Main Issues in UWB TOA Estimation

Rich multipath(ambiguities in first path detection, paths overlapping)

Non Line-of-Sight (NLOS)

conditions•direct path blockage•extra propagation delay due to different e.m. propagation speeds

Interference(narrowband and wideband)

In addition:• clock drift (time synch algorithms)• design of low-complexity TOA estimators

43

85


TOA Estimation in Multipath Environments

In general, dense multipath is composed of tens or hundreds paths.It may be difficult to recognize the first path, especially at low and medium SNRs

detection of first path may be challengingPaths could be partially overlapped (not resolvable channel)


r(t) =pEp

LXl=1

αl p(t− τl) + n(t)p(t)

86


TOA Estimation in Multipath Environments

Received signal

r(t) =pEp

LXl=1

αl p(t− τl) + n(t)

Problem formulation

• we are interested in the estimation of the ToA, τ = τ1, of the direct path byobserving the received signal r(t) within the observation interval [0, Tob);

• we consider τ to be uniformly distributed in the interval [0, Ta), withTa < Tob;

• set of nuisance parameters U = {τ2, τ3, . . . , τL,α1,α2, . . . ,αL}

44

87


CRB and ZZB for TOA Estimation with Multipath

5 10 15 20 25SNR (dB)

10-12

10-11

10-10

10-9

10-8

RM

SE

(s)

CRB ZZB, CM1ZZB, CM4ZZB, CM5

Using the IEEE802.15.4a channel model

D. Dardari, A. Conti, U. Ferner, A. Giorgetti, and M. Z. Win, “Ranging with Ultrawide Bandwidth Signals in MultipathEnvironments”, Proc. of IEEE (Special Issue on UWB Technology & Emerging Applications), Feb. 2009.

88


ZZB for TOA Estimation using Measured Data

0 5 10 15 20 25 30SNR (dB)

10-12

10-11

10-10

10-9

10-8

10-7

RM

SE

(s)

LOS (Rx2)LOS (Rx3)NLOS (Rx4)NLOS (Rx5)NLOS (Rx6)NLOS (Rx7)NLOS (Rx8)

D. Dardari, C.-C. Chong, and M. Z. Win, “Improved lower bounds on time-of-arrival estimation error in realistic UWB channels,” in IEEE International Conference on Ultra-Wideband, ICUWB 2006, (Waltham, MA, USA), pp. 531–537, Sept. 2006.

C. –C. Chong and S. K. Yong, “A generic statistical based UWB channel model for high-rise apartments,” IEEE Trans. Antennas and Propagation, vol. 53, no. 8, pp. 2389-2399, Aug. 2005.

high-rise apartment

45

89


ML TOA Estimator in Multipath Environments

Problems:• Implementation at Nyquist sampling rate or higher difficult with UWB

signals• High complexity

looking for sub-optimal schemes

The ML estimate of τ is τ = argmaxτ

{χH(τ )R−1(τ )χ(τ )}, where R(τ ) is the

autocorrelation matrix of p(t) and

χ(τ ) ,Z Tob

0

r(t)

⎡⎢⎢⎢⎢⎢⎢⎣p(t− τ1)p(t− τ2)

.

.

.p(t− τL)

⎤⎥⎥⎥⎥⎥⎥⎦ dt

When channel parameters are unknown, TOA estimation in multipath environ-ments is closely related to channel estimation, where path amplitudes and TOAτ = [τ1, τ2, . . . , τL]

T are jointly estimated using, for example, a ML approach

90


Reducing the Estimator Complexity

Examples:

-Sub-optimal ML

-Generalized ML

-Simple thresholding

-Multi-resolution

-Delay & average (noisy template)

-Two-stages approaches

-Energy detection based

-……………………

46

91


Sub-optimal ML Algorithms

• They can be derived from the ML channel estimation criterion and based on a simple peak detection process

– Single Search– Search and Subtract– Search, Subtract and Readjust

These algorithms essentially involve the detection of N largest positive and negative values of the MF output and the determination of the corresponding time locations .

While these algorithms are equivalent when the multipaths are separable, the last two algorithms

take into account the effects of a non-separable channel (overlapped echoes)

92


Single Search

1k 2k

3k

• The delay and amplitude vectors are estimated with a single look.

( )r t ( )y t

MF

( )y t

Peak Detectorˆ ˆ,

i ik kcτ

1,2,...,ik N=

ABS ()

( )y t

3k

47

93


Search and Subtract

( )r t

1ˆkτ

( ) ( ) ( )1ir t r t r t−= −

MF Peak Detect

1,2,...,ik N=

Path estimator

+

( ) ( )ˆ ˆ ˆi ii k kr t c p t τ= −

( )1ir t−

ABS ()

This algorithm provides a way to detect multipath components in a non-separable channel.

( )y t ( )y t

ˆ ˆ,i ik kcτ

path 2 path 1

received signal

received signalafter subtract

( )r t

94


Search Subtract and Readjust

Peak Detect

{ }1

ˆ ˆj

i

k jc c

==

ˆikτ

Peak estimator

Path estimator

( ) ( )1ˆ ˆ ˆ

j j

ii k kj

r t c p t τ=

= −∑

( ) ( )ph t p T t= −

MF

++ ( )1ir t−

ABS ()

( ) ( ) ( )1ir t r t r t−= −

1,2,...,ik N=

( )y t ( )y t

( )r t

So far (Search and Subtract) the delay and amplitude of each path are estimated separately at each step; in this algorithm a joint estimation of the amplitudes of different paths is introduced.

48

95


Simple Thresholding

The optimum choice of η depends on channel statistics and SNR:

• Small η → high probability of early detection prior to the first path (earlyTOA estimation) due to noise and interference

• Large η→ low probability of detecting the first path and a high probabilityof detecting an erroneous path (late TOA estimation) due to fading

Threshold η

threshold

Another classical approach is the Thresholding Estimator

96


Algorithmstested

Lower complexity

Higher complexity

Simple thresholding, peak detection

Iterative cancellation techniques (to deal with paths overlapping)

Main observations:

-There is no a large performance difference between simple and complex algorithms considered (especially at medium and large SNRs)

-Ranging resolutions on the order of 10cm are achievable!

Decreasing SNR

Performance in Realistic EnvironmentsResults from: C. Falsi, D. Dardari, L. Mucchi, and M. Z. Win, “Time of arrival estimation for UWB localizers in realistic environments,” EURASIP J. Appl. Signal Processing (Special Issue on Wireless Location Technologies and Applications), 2006.

49

97


Sub-Nyquist Sampling Rate TOA Estimators

TOA estimators based on energy detection (ED) can operate at sub-Nyquist sampling rate low complexity

The TOA resolution is bounded by the ED integration time

• T[.]: pre-processing filter– to improve the first path detection– to mitigate the effect of the interference

• First path detector: several algorithms can be adopted

Asymptotic MSE = T 2int/12

98


Example of ranging preamble structure

Each user has a different time-hopping sequenceto allow multi-user communication

50

99


ED-based TOA estimation algorithms

• Simple thresholding• P-MAX• Backward search• Jump Back and Search Forward• ………

Collected energy vector

Possible algorithms

100


Simple thresholding

Decision vector

Detect the time slot within the observation interval that contains the first pathby comparing each element of {zk} to a fixed threshold η.The first threshold crossing event is taken as the estimate of the TOA

The optimum choice of η depends on channel statistics and SNR:

• Small η → high probability of early detection prior to the first path (earlyTOA estimation) due to noise and interference

• Large η→ low probability of detecting the first path and a high probabilityof detecting an erroneous path (late TOA estimation) due to fading

51

101


P-Max

Decision vector

The P-Max criteria is based on the selection of the earliest sample among theP largest in {zk}.

TOA estimation performance depends on the parameter P , where P can beoptimized according to received signal characteristics

- D. Dardari, C.-C. Chong, and M. Z. Win, “Analysis of threshold-based ToA estimators in UWB channels,” in European Signal Processing Conference, EUSIPCO 2006, Florence, ITALY, Sep. 2006.

102


Backward Search

Decision vector

The search begins from the largest sample in {zk}, with index kmax, and thesearch proceeds element by element backward in a window of length Wsb untilthe sample-under-test goes below the threshold η

- I. Guvenc and Z. Sahinoglu, “Threshold-based TOA estimation for impulse radio UWB systems,” in Proc. IEEE Int. Conf. on Utra-Wideband (ICU), Zurich, Switzerland, Sep 2005, pp. 420–425.

52

103


Jump Back and Search Forward

Decision vector

The leading edge of the signal is searched element-by-element in a window oflength Wsb samples preceding the strongest one.The search proceeds forward until the sample-under-test crosses the threshold

The optimal selection ofWsb and η depend on the received signal characteristics

104


10 15 20 25 30 35 40SNR (dB)

10-10

10-9

10-8

10-7

RM

SE

(s)

MAX

P-Max, P=3

JBSF, Wsb=20

Simple Thresholding

SBS, Wsb=20

SBSMC, Wsb=30, D=5

Performance comparison between TOA est. techniques

From: D. Dardari, A. Conti, U. Ferner, A. Giorgetti, and M. Z. Win, “Ranging with Ultrawide Bandwidth Signals in Multipath Environments”, Proc. of IEEE (Special Issue on UWB Technology & Emerging Applications), Feb, 2009.

Error floor

Signal bandwidth 1.6 GHzCenter frequency 4 GHzED integration time 2 nsPreamble length 400 pulsesFrame duration 120 nsIEEE802.15.4a (CM4)channel model

Tint/√12

53

105


Interference effects

UWB systems are expected to work in coexistence with other UWBand narrowband systems as an underlying technology

Both wideband interference (WBI) and narrowband interference (NBI)can be present and degrade the ToA estimation

Either non-linear and linear 2D filtering techniques can be applied to mitigate the effect of the interference

- Z. Shainoglu and I. Guvenc, “Multiuser interference mitigation in noncoherent UWB ranging via nonlinear filtering,” EURASIP J. Wireless Communications and Networking, vol. 2006, pp. 1–10, 2006.

- D. Dardari, A. Giorgetti, and M. Z. Win, “Time-of-arrival estimation in the presence of narrow and wide bandwidth interference in UWB channels,” in IEEE International Conference on Ultra-Wideband, ICUWB 2007, Singapore, Sep. 2007.

106


Interference Mitigation Techniques

Min Filter

Min Filter+Differential Filter

Each column of {vn,k} can be processed as follows

zk =

Nt−HXn=0

min{vn,k, vn+1,k, . . . , vn+H−1,k},

where k = 0, . . . ,K − 1, and H is the length of the filter.

To improve the estimation performance in the presence of both NBI and WBI,the min filter is first applied to each matrix column to produce the intermediatevector {zk}.The vector {zk} is further processed (differential filter) as follows

zk = zk − zk+1, k = 0, . . . , K − 1 ,

with zK = 0.

54

107


Energy matrix filtering examples

First step: after the min filterBefore filtering

Second step:

after the differential filter

True ToA

True ToA

True ToA

WBI+NBI

108


10 15 20 25 30 35 40SNR (dB)

10-10

10-9

10-8

10-7

RM

SE (s

)

Averaging filterDifferential filterMin filterMin & differential filters

w/o interf.

NBI & MUI

Performance of the threshold-based estimator with different 2D filtering tech-niques in the presence of both NBI, INR = 35 dB, and WBI, SIR = −15 dB.

Nsym = 400: preamble length.

From D. Dardari, A. Giorgetti, and M. Z. Win, “Time-of-arrival estimation in the presence of narrow and wide bandwidth interference in UWB channels,” in IEEE International Conference on Ultra-Wideband, ICUWB 2007, Singapore, Sep. 2007.

Performance comparison between mitigation techniques

55

109


NLOS condition: extra propagation delay (1/2)

D. Dardari, A. Conti, J. Lien, and M. Z. Win, “The effect of cooperation in UWB based positioning systems usingexperimental data,” EURASIP Journal on Advances in Signal Processing, Special Issue on Cooperative Localization inWireless Ad Hoc and Sensor Networks, 2008.

The extra delay ∆τ introduced by a homogeneous material with thickness dWis given by

∆τ = (√²r − 1)

dWc

where ²r is the relative electrical permittivity of the material.

110


Extra propagation delay effect leads to biased estimations, typically some nanoseconds

Possible solutions:

• larger number of anchor nodes

• knowledge of some a priori information (e.g., scenario layout)

• cooperation among nodes

Ranging error on the order of 50 cm!

NLOS condition: extra propagation delay (2/2)

56

111


The IEEE 802.15.4a standard

112


Overview of IEEE 802.15.4 (ZigBee)

Features:• Low Data Rate• Low Power Consumption• Low Cost• Self-Organization and Flexible Topology

57

113


The IEEE 802.15.4a

As an amendment to 802.15.4 for an alternative PHY to provide (2007):

• High precision ranging/location capability• Ultra low power• Lower cost• High aggregate throughput• Scalability to data rates• Longer range

Asset tracking

Home automation

and.. WSNs, health care monitoring…

114


Physical layers

The amendment adds two new PHYs:

• 150 - 650 MHz and 3.1 - 10.6 GHz Ultra-Wide Band (UWB)– Impulse Radio (IR) based signaling– Mandatory nominal data rate of 1 Mbps– Modulation: Combination of BPM (burst position mod.) and BPSK

• 2450 MHz Chirp Spread Spectrum (CSS)

58

115


UWB PHY: Channels

A compliant UWB device shall be capable of transmitting in at least one of three specified bands.

• Sub-GHz Band: 150 – 650 MHz

• Low Frequency Band (LFB): 3244 - 4742 MHz

• High Frequency Band (HFB): 5944 – 10234 MHz

116


UWB-PHY: Bursts

• Each information-bearing symbol is represented by a burst of short time duration pulses

• The duration of an individual pulse is nominally considered to be the length of a chip

• Chip duration is equal to 2.02429 ns (Bandwidth > 494MHz)

59

117


UWB PHY: Modulation

Combination of burst position modulation (BPM) and binary phase shift keying (BPSK)

A UWB PHY symbol carries two bits of information: one bit for the position of a burst and the other bit for the phase (parity) of the burst.

• Each symbol can have 8, 16, 32, 64, and 128 possible slots for aburst.

• A symbol period is divided into two BPM intervals, and a burst occurs one of two intervals.

• A burst is formed by grouping 2n, n=0, 1, 2, ..., 9 consecutive chips.

118


The symbol structure

The UWB-PHY supports both coherent and non-coherent receivers.

60

119


UWB-PHY: Spreading

• The UWB PHY provides interference suppression through scrambling and time-hopping.

• Scrambling: each symbol contains a single burst of pulses, which are scrambled by a time-varying scrambling sequence.

• Time Hopping: each burst position varies from one symbol to the next according to a time hopping code.

120


Device Types

• Fully-function device (FFD)– Any topology– PAN coordinator– Talks to any other device– Capable of perform energy detection (ED) and active

scans

• Reduced-function device (RFD)– Limited to star topology– Talks only to an FFD– Simple implementation and application

61

121


Topology

• Star topology: Communication is established between devices and a single central controller,called the PAN coordinator.

• Peer-to-Peer Topology: Any device can communicate with any other device as long as they are in range of one another.

122


Ranging in the IEEE802.15.4a standard (1/2)

• Ranging is optional (UWB PHY only)

• Main ranging protocol: Two-way TOA ranging (others possible)

• Private ranging protocol to protect against malicious devices (optional)

• Both coherent and non coherent estimation

Preamble

[16,64,1024,4096] symbols

Start of frame delimiter, SFD

[8,64] symbols

PHY header Data field

IEEE 802.15.4a packet structure

Preamble: acquisition, channel sounding and leading edge detectionSFD: frame synchronization, ranging counter management.

Designed to minimize the frame synchronization error

•The preamble and SFD lengths are specified by the application based on channel conditionsand receiver capabilities (coherent/non-coherent)

•The application bases its choice on figure of merit reports from PHY on good range measurement is

•Longer lengths are preferred for non-coherent receivers to improve SNR via processing gain

62

123


Ranging in the IEEE802.15.4a standard (2/2)

• Each symbol is composed of a ternary sequence taken from a set of 8 sequences

• Each ternary sequence, and its non coherent version, has ideal periodic autocorrelation function (no side lobes) so that what is observed at the receiver is only the channel response

Example of one the 8 possible length-31 ternary sequence

[-000+0-0+++0+-000+-000+-+++00-+0-00]

Autocorrelation function

124


Some advanced issues

• Secure ranging• Cognitive ranging• Robust ranging (interference mitigation) • Passive ranging and localization• New generation RFID systems• ……….

63

125


Secure Ranging

126


Secure Ranging

Ranging can be subjected to hostile attacks in certain environments.

In order to make impostor and snooper attacks more difficult, the IEEE 802.15.4a standard includes an optional private ranging mode

Node A Node B

Private ranging

After a preliminary authentication step, nodes exchange information, via a secure communication protocol, on both the sequences to be used in the next ranging cycle as well as their timestamp reports

The response delay τd is kept secret

64

127


Interference mitigation &

Cognitive Ranging

128


Spectral Skyline

• Total Congestion• No room to build new high-bandwidth services

Source: Turi Aytur, WiMedia Technical Overview, Realtek Semiconductor

65

129


UWB: the quiet neighbor

• Wide bandwidth and very low power spectral density• “Underlay” technology coexists with other services

130


Narrow Band Interference (NBI)

• UWB system operate over extremely wide frequency bands, where various narrow band technology operate with much higher power levels.

• The narrowband system are affected from the UWB signal only at anegligible level.

• The influence of NBI on the UWB system may jam the UWB receiver.

Coexistence

66

131


UWB towards Cognitive Radio paradigm

In this line, CR based on UWB could represent a more complete solution as it:

begins to transmit inside those bands (Agile spectrum generation)

frequency

Pow

er S

pect

rum

eventually being get out if needed when the primary users show up

frequency

Pow

er S

pect

rum

actively looks for unused spectrum (Spectrum sensing)

frequency

Pow

er S

pect

rum

holes

132


Cognitive Ranging

Target:

Find the optimal transmitted signal spectrum shape which maximizes the theoretical ranging accuracy

frequency

Pow

er S

pect

rum

We expect that a higher ranging accuracy can be achieved if we tolerate the presence of interference under certain power mask constraints (underlay transmission) .

frequency

Pow

er S

pect

rum

Transmitting only in the spectrum holes could not be the best solution

D. Dardari, Y. Karisan, S. Gezici, A. A. D’Amico, and U. Mengali, “Performance limits on ranging with cognitive radio,”in International Workshop on Synergies in Communicaions and Localization (SyCoLo 2009), Dresden, Germany, June 2009.

67

133


Passive Ranging and Localization

134


Passive localization

• Besides the localization of “friendly” collaborative objects (active tags), passive geolocation, i.e., the possibility of detecting and tracking non collaborative objects (targets) is gaining an increasing attention.

• Attractive to monitoring critical environments:– Power plants– Reservoirs– Critical structures vulnerable to attacks

• Attractive for rescue in disaster scenarios (e.g., to quickly localize people trapped in collapsed buildings or in the presence of dense smoke…)

targets typically human beings or vehicles

68

135


Anti-intruder radar systems

• To this aim, a wireless infrastructure composed of cooperative UWB nodes could represent a cheap solution thanks to high resolutioncapabilities and low cost signal processing techniques

• Tactical wireless sensor networks• Radar sensor networks• Multistatic UWB radars

136


• Monostatic radarTX and RX are colocated

• Bistatic radarTx and RX are separated by a distance comparable to the target

distance

Radar basics

target

target

69

137


• Multistatic radarTxs and RXs are separated by a distance comparable to the target

distance

– Multiple TXs and one RX– One TX and multiple RXs

• They do not suffer from coupling between TX and RX• Can detect stealth targets• RXs are not detectable• They do not require directional antennas to locate the target• With more RX we can increase the radar sensitivity and counteract

fading, shadowing and clutter• They require proper information exchange (cooperation is needed)

Multistatic radar basics

target

138


Selected references

D. Dardari, A. Conti, U. Ferner, A. Giorgetti, and M. Z. Win, “Ranging with Ultrawide Bandwidth Signals in MultipathEnvironments”, Proc. of IEEE (Special Issue on UWB Technology & Emerging Applications), Feb. 2009.

I. Guvenc and Z. Sahinoglu, “Threshold-based TOA estimation for impulse radio UWB systems,” in Proc. IEEE Int. Conf. on Utra-Wideband (ICU), Zurich, Switzerland, Sep 2005, pp. 420–425.

S. Gezici, Z. Tian, G. B. Giannakis, H. Kobayashi, A. F. Molisch, H. V. Poor, and Z. Sahinoglu, “Localization via ultra-wideband radios: a look at positioning aspects for future sensor networks,” IEEE Signal Processing Mag., vol. 22, pp. 70–84, Jul. 2005.

D. Dardari, C.-C. Chong, and M. Z. Win, “Improved lower bounds on time-of-arrival estimation error in realistic UWB channels,” in IEEE International Conference on Ultra-Wideband, ICUWB 2006, (Waltham, MA, USA), pp. 531–537, Sept. 2006.

R. Verdone, D. Dardari, G. Mazzini, A. Conti, Wireless Sensors and Actuator Networks: Technologies, Analysis and Design, Elsevier, 2008

Z. Sahinoglu, S. Gezici, I. Guvenc, “Ultra-wideband positioning systems: theoretical limits, ranging algorithms, and protocols”, Cambridge University press, 2008.

D. Dardari, C.-C. Chong, and M. Z. Win, “Threshold-based time-of-arrival estimators in UWB dense multipath channels,”IEEE Trans. Commun., vol. 56, no. 8, Aug. 2008.

D. Dardari, A. Giorgetti, and M. Z. Win, “Time-of-arrival estimation in the presence of narrow and wide bandwidth interference in UWB channels,” in IEEE International Conference on Ultra-Wideband, ICUWB 2007, Singapore, Sep. 2007.

Z. Shainoglu and I. Guvenc, “Multiuser interference mitigation in noncoherent UWB ranging via nonlinear filtering,”EURASIP J. Wireless Communications and Networking, vol. 2006, pp. 1–10, 2006.

70

139


Selected references

D. Dardari, A. Conti, J. Lien, and M. Z. Win, “The effect of cooperation in UWB based positioning systems usingexperimental data,” EURASIP Journal on Advances in Signal Processing, Special Issue on Cooperative Localization inWireless Ad Hoc and Sensor Networks, 2008.

C. Falsi, D. Dardari, L. Mucchi, and M. Z. Win, “Time of arrival estimation for UWB localizers in realistic environments,”EURASIP J. Appl. Signal Processing (Special Issue on Wireless Location Technologies and Applications), 2006.

P. Cheong, A. Rabbachin, J. Montillet, K. Yu, and I. Oppermann, “Synchronization, TOA and position estimation forlow-complexity LDR UWB devices,” in Proc. of IEEE Int. Conf. on Ultra-Wideband (ICUWB), Zurich, SWITZERLAND,Sep 2005, pp. 480–484.

J.-Y. Lee and R. A. Scholtz, “Ranging in a dense multipath environment using an UWB radio link,” IEEE J. Sel. AreasCommun., vol. 20, no. 9, pp. 1677–1683, Dec. 2002.

Y. Shen and M. Z. Win,“Effect of path-overlap on localization accuracy in dense multipath environments,” in Proc. IEEE Int. Conf. on Commun., Beijing, CHINA, May 2008.

Y. Shen and M. Z. Win, “Fundamental limits of wideband localization accuracy via Fisher information,” in Proc. IEEEWireless Commun. and Networking Conf., Kowloon, HONG KONG, Mar. 2007, pp. 3046–3051.

B. Alavi and K. Pahlavan, “Modeling of the TOA-based distance measurement error using UWB indoor radiomeasurements,” IEEE Commun. Lett., vol. 10, no. 4, pp. 275–277, Apr. 2006.

140


Selected references

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Special thanks

O. Andrisano (University of Bologna, Italy)M. Chiani (University of Bologna, Italy)C-C. Chong (DoCoMo Labs, USA)C. Falsi (University of Florence, Italy)U. Ferner (MIT, USA)S. Gezici (Bilkent University, Turkey)A. Giorgetti (University of Bologna, Italy)J. Lien (JPL, Nasa, USA)D. Jourdan (Athera, Washington, USA)L. Mucchi (University of Florence, Italy)M. Z. Win (MIT, USA)

In addition, thanks to N. Decarli, T. Pavani, R. Soloperto for their support during the measurement campaign

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