ORIGINAL ARTICLE

Integrity monitoring algorithms for airport surface movement

Wolfgang Schuster • Jie Bai • Shaojun Feng •

Washington Ochieng

Received: 30 September 2010 / Accepted: 18 January 2011 / Published online: 8 February 2011

� Springer-Verlag 2011

Abstract Navigation algorithms are proposed for carrier

phase ambiguity integrity monitoring to support aircraft

surface movement. The enhanced integrity monitoring

algorithm addresses the very stringent integrity require-

ments for surface movement by the use of multiple test

statistics and a group separation concept for single and

multiple failure detection and exclusion. The algorithms

are subject to a detailed performance characterization for

precision approaches and airport surface movement, using

simulations as well as static and dynamic field trials, taking

into account operational specificities, such as multipath and

potential decorrelations between the reference station and

aircraft due to ionospheric anomalies. Results show that the

proposed algorithms have the potential to satisfy airport

surface movement requirements if the ionospheric anom-

alies are monitored using a special ground-based network.

Keywords Integrity � Ambiguity resolution � Airport

surface movement � RTK � E-GBAS � E-CRAIM

Introduction

Studies have shown that airport surface movement (ASM)

is one of the bottlenecks within the chain of aircraft gate-

to-gate operations and is thus critical to the optimization of

operational capacity. Currently, surface movement radar is

the basic means for surface surveillance. However, the

aircraft position provided by radar is approximate and

therefore has a limited role in facilitating an increase in

capacity both under nominal and poor visibility conditions.

Therefore, to increase situational awareness and mitigate

potential runway incursions, future Advanced Surface

Movement Guidance and Control Systems (A-SMGCS)

must have access to a system capable of providing the

position, velocity and time (PVT) of aircraft and other

vehicles with very high accuracy, integrity, continuity, and

availability.

The current focus in the USA is on the development of a

CAT III Ground-Based Augmentation System (GBAS)

architecture using code-based single-frequency Global

Positioning System (GPS) observables (Boeing 2005).

However, the proposed architecture is not expected to meet

the significantly more stringent taxiing accuracy (0.5 m,

95%) and alert limit (1.4 m) requirements proposed in

Schuster and Ochieng (2011a). A subsequent investigation

into the performance of GNSS determined that code-based

solutions, whether stand-alone or using GBAS, are not able

to provide the required sub-meter accuracy with the level

of integrity required for surface movement (Schuster et al.

2009). Therefore, surface movement operations require the

use of carrier phase measurements in Real Time Kinematic

(RTK) mode with ambiguities resolved on-the-fly (OTF).

From the perspectives of optimization of navigation

infrastructure and cost effectiveness, it is sensible to

include the carrier phase-based ASM navigation function-

ality in the GBAS architecture for CAT III.

We develop a carrier phase-based High-Performance

Navigation (HP-NAV) algorithm, with a focus on reliable

and robust integrity monitoring, to achieve the required

navigation system performance for ASM. The core of the

algorithm is an Extended Carrier phase Receiver Autono-

mous Integrity Monitoring (E-CRAIM) module. It includes

a Minimum Constellation Method (MCM) to speed up the

initial ambiguity fix, an enhanced ambiguity validation

module and a re-search procedure in the case where the

W. Schuster (&) � J. Bai � S. Feng � W. Ochieng

Imperial College, London, UK

e-mail: w.schuster@imperial.ac.uk

123

GPS Solut (2012) 16:65–75

DOI 10.1007/s10291-011-0209-9

ambiguity was not fixed on the first iteration. These are

described in the following sections. The paper concludes

with a characterization of the performance of the HP-NAV

algorithm with simulated and real-flight data.

Extended carrier phase integrity monitoring concept

Figure 1 presents the basic structure of the HP-NAV

user-level algorithm for surface movement (Bai et al.

2008). It comprises of a number of functional modules:

data input and output for single- or multiple-GNSS

and Extended Ground-Based Augmentation Systems

(E-GBAS) (Schuster and Ochieng 2011b), data prepro-

cessing, positioning and navigation, and integrity moni-

toring. The data preprocessing module employs a number

of models for the mitigation of the effects of the iono-

sphere, troposphere, multipath, and cycle slips. The

outputs from this module are the carrier phase smoothed

pseudorange and carrier phase observables. The posi-

tioning solutions can be computed by the use of either

least squares estimation or extended Kalman filtering

(EKF). The focus of this research, integrity monitoring,

is based on an Extended Carrier phase Receiver Auton-

omous Integrity Monitoring (E-CRAIM) scheme that

includes monitoring in the ambiguity (i.e., ambiguity

validation) and positioning domains. E-CRAIM is a

derivative of conventional C-RAIM (Ochieng et al. 2007;

Schuster et al. 2007; Feng et al. 2008), with the fol-

lowing additional features.

• Advanced ambiguity resolution with re-search and re-

validation in case of initial failure.

• Failure detection with fixed ambiguity.

• Protection level (PL) estimations based on either

external or onboard observable error estimation.

Each of the above features is addressed in turn below.

Ambiguity resolution and validation

Past studies have shown that the LAMBDA method

(Teunissen 1995), a combination of least squares and

efficient ambiguity search, has the highest possible suc-

cess rate of all integer ambiguity estimators (Teunissen

1999). However, LAMBDA is not designed to mitigate

measurement errors. On the other hand, the MCAR is

able to mitigate measurement errors using multiple-fre-

quency observables. However, its main weaknesses are

amplified noise and multipath. These limitations are

addressed here by exploiting an ambiguity resolution

scheme (Fig. 2) that combines these two methods. Note

that only the dual-frequency scenario is shown in Fig. 2

for ease of comprehension. The triple frequency process

follows the same procedure with an additional extra-

widelane step.Fig. 1 High-level flowchart of HP-NAV software

Fig. 2 Ambiguity search

procedure—high-level overview

66 GPS Solut (2012) 16:65–75

123

The ambiguity search procedure consists of the follow-

ing steps:

• Double-differenced L1 smoothed code phase data are

used to estimate the receiver position. Due to the

relatively short baselines, as would be expected within

an airport environment, the expectation is that the

initial smoothed code-based estimation should deliver

decimeter-level accuracy and thereby facilitate the

direct resolution of the L1 carrier phase ambiguity.

• A LAMBDA search is executed as a first attempt to

resolve the L1 integer ambiguities.

• The ambiguity results from LAMBDA are sent to the

validation module.

• If the L1 ambiguities pass the validation test, they are used

to compute the antenna position; otherwise the process is

repeated with double-differenced widelane data.

The process for double-differenced widelane involves

the use of smoothed code to estimate the float widelane

ambiguities. Given the relatively long wavelength, the float

ambiguities and the corresponding variances and covari-

ances are typically well estimated. As in the case of L1,

float widelane ambiguities are sent to the LAMBDA search

module followed by the validation process. If the widelane

ambiguities pass the validation test, they are used subse-

quently to estimate the L1 ambiguities with the more

accurate range estimation. The L1 ambiguity estimation

and validation processes are then repeated until the L1

ambiguities pass the validation test, yielding the final PVT

solution. If the widelane ambiguities do not pass the vali-

dation test and a third frequency signal is available, the

extra-widelane observables are used to repeat the ambi-

guity search procedure until the L1 ambiguities are fixed. If

the L1 ambiguities cannot be resolved, the PVT solution is

based on float ambiguities.

The scheme described above is designed to start with the

direct resolution of the L1 carrier phase ambiguities and only

uses widelane and ultimately extra-widelane observables if

the preceding step fails. This order is inverted compared to

the original MCAR method to reduce computation time for

real-time applications. It is justifiable on the basis that the

short baselines, aided by a ground monitoring network for

ionospheric anomalies (Schuster and Ochieng 2011b),

should result in a relatively high proportion of successful

resolution of L1 ambiguities, without the need to use the

widelane or extra-widelane observables. This ground mon-

itoring network consists essentially of a few range and

position-domain monitors placed strategically on the airport

surface, augmenting the core GBAS architecture foreseen

for CAT III precision landings.

Enhanced ambiguity validation

The uncertainty of parameter estimators implies that an

integer fixed solution is not always correct. Therefore, a

procedure is executed to validate the integer ambiguity

solution and is tightly linked to the ambiguity search

method. The typical search procedure for single-frequency

measurements comprises the following steps.

• Estimation of the float ambiguities using both carrier

and code phase observables.

• Search for integer ambiguities.

• Estimation of the least squares solution using carrier

phase and fixed integer ambiguities.

The most common ambiguity validation methods are

empirical, such as the F-Test in the measurement domain.

However, the F-Test requires fine-tuning for each specific

operational environment. Therefore, for widely varying

environmental conditions, as encountered when taxiing

from the runway with open space to the ramp with build-

ings nearby, the use of these validation methods with

empirical thresholds on their own are expected to have low

success rates (Bai 2008). A different type of validation

method is the integer aperture (IA) (Verhagen and Teun-

issen 2006). It is based on the concept of pull-in region,

shown as black hexagons in Fig. 3. Provided the float

ambiguity distribution, shown in gray, is centered on one of

the acceptance regions of the ratio test, shown in red, the

corresponding fixed solution is accepted.

The left panel shows the unbiased case. The two panels

on the right show the effect of a bias in the observations. It

is clear that if the bias is sufficiently large, there is a high

probability of accepting the wrong ambiguity solution.

Fig. 3 Concept of pull-in

region in IA method

GPS Solut (2012) 16:65–75 67

123

Therefore, it is essential to use a reliable bias detector with

this method.

Building on the strengths of the ambiguity validation

methods above, an enhanced ambiguity validation scheme

is developed below. It focuses on building the appropriate

test statistics under the null hypothesis and alternative

hypothesis as well as the appropriate thresholds. While

there is no standard criterion for choosing test statistics, the

emphasis is placed on the following criteria:

• the decision logic is based on raw observable data as

much as possible;

• minimization of assumptions and approximations;

• the decision logic can be applied to both code and

carrier phase observables as well as combinations

thereof.

Based on the above considerations, the F-test is suitable

for L1 ambiguity validation, referred to in the rest of the

paper as the ‘‘L1-test’’. The test statistic is given by:

T ¼ r2

r2y

ð1Þ

where

r2 ¼rT G�1

y r

mð2Þ

and m is the number of degrees of freedom; G the cofactor

matrix of the variance–covariance matrix of the

observables y and r is the vector of residuals.

Ambiguities are accepted when:

r2

r2y

\FFAðm:1Þ

where FFA is the value of the central F-distribution for the

required probability of false alert (FA).

Since in the presence of E-GBAS, it is highly unlikely

that a bias of more than half a wavelength is present in the

extra-widelane/widelane ambiguity estimation; the IA

method is chosen for this test. Note, however, that for

optimal performance, it is advisable to use the IA test

alongside the F-test to guarantee a very high level of

protection against any residual bias.

If L1 ambiguities cannot be resolved at the first iteration,

extra-widelane and widelane solutions progressively pro-

vide improved range estimations to enable the L1 ambi-

guities to be resolved. In case the extra-widelane or

widelane ambiguities cannot pass the IA test, a large error is

likely to exist in the observables or in the estimated range.

In this case, the ranges are re-estimated using a different

satellite configuration. If extra-widelane and widelane

ambiguities are resolved, the MCAR procedure continues

with the L1 ambiguity validation, which determines whe-

ther re-search and re-validation procedures are required.

The L1-test effectively makes the final decision as to

whether the carrier phase-based positioning algorithm

provides the correct integer ambiguity values. The method

to generate the test statistics for the L1-test and to deter-

mine the threshold is described in more detail in Sect.

‘‘Ambiguity re-search and re-validation.’’ While past

algorithms typically use the float solution if the L1-test fails

as a result of incorrect integer ambiguity resolution, this

paper proposes a re-search and re-validation procedure to

maximize algorithm performance and system availability.

Ambiguity re-search and re-validation

Sub-meter level accuracy, of the same order of magnitude

as the wavelength of the widelane observables (*86 cm),

should be achievable with double-differenced carrier phase

smoothed code phase observables. For most cases, the

correct widelane ambiguity will therefore be in the range

between N-1 and N?1, where N is the estimated integer

ambiguity based on the float ambiguity. In order to maxi-

mize availability, a search of all float ambiguities within

this region is carried out until the L1-test is passed. The

search begins with the float ambiguity nearest to the edge

of the pull-in region and takes 3n-1 steps for the full tree

(FT), where n is the number of ambiguities. The search

procedure is terminated as soon as the L1-test is passed.

The main drawback of this method is the significant

computational burden. This makes it impractical, espe-

cially for multiple constellation satellite configurations.

Moreover, this method neglects the fact that the probability

of the correct ambiguity being located at lower values of

the initial integer estimation is different to that of the

correct ambiguity being located at higher values. For

example, consider the hypothetical case where the float

estimation is 4.56 and the initial integer estimation is 5. If 5

is not correct, 6 would have a lower probability to be

correct than 4. Therefore, it would not be efficient to test 6

immediately after testing 5.

In order to significantly reduce the computation time, an

improved search scheme is proposed to determine the most

likely direction in the above method, thereby optimizing

the search. A change in the widelane ambiguity by one unit

causes a significantly larger change in the L1 ambiguity,

which is easily detected by comparing the sum-square-

error (SSE) results from the C-RAIM algorithm. If the SSE

value is above a given threshold, all branches from that

knot are ignored in the search. The barrier threshold can be

determined empirically, for example, based on the failure

detection threshold in C-RAIM.

An alternative method, used here, is the Index-Moni-

tored Bootstrap (IMB) re-search and re-validation proce-

dure, presented in Teunissen (2001). Assuming that the

code-based position error is less than the wavelength of

68 GPS Solut (2012) 16:65–75

123

widelane, or extra-widelane, i.e., as would be typical for

the short baselines for GBAS-based systems, the correct

ambiguity values are expected to be one of the two integers

closest to the float value. In other words, the correct

ambiguity solution is likely to be a combination of the best

and second best integer estimations of the elements in the

ambiguity set. The order of switching between the various

ambiguities is based on the following rules:

• Minimize the number of elements that are changed.

• Apply changes to the ambiguity values according to

their distance to the edge of the pull-in region, starting

with the ambiguity values closest to the edge.

This is equivalent to a combination traversal problem

and takes a total of 2n-1 steps to search the entire candi-

date set. This method is significantly faster and more

efficient than FT. However, irrespective of the method

used, the re-search and re-validation scheme still requires

significant computational resources that could hamper its

use in real time. Therefore, for time critical applications,

for example, with short time-to-alerts such as for surface

movement, it is important to minimize the time to ambi-

guity resolution. This is accomplished by using the MCM

below in combination with the IMB.

Minimum constellation method

It is well known that the noise in the double-differenced

residuals increases with decreasing satellite elevation, and

that this makes integer ambiguity resolution at lower ele-

vation difficult. Furthermore, it is the tradition to use the

largest possible number of satellites in the computation of

the position of the user. However, and particularly in the

case of multiple constellations, the question of an optimal

configuration for the best position solution is still to be

agreed. The approach taken here consists of two steps:

• Resolve the ambiguities for a reduced set of satellites,

excluding those at low elevations.

• Use the resolved ambiguities in the first step to aid the

resolution of the remaining satellites and use all the

satellites to exploit the better geometry and measure-

ment redundancy for position estimation.

The procedure above significantly increases the speed

and reliability of ambiguity resolution. In order to ensure a

minimum consistency check capability, at least 5 satellites

are required for the minimum constellation. Therefore, for

nominal scenarios, the satellites with the five highest ele-

vations are used for the minimum constellation calculation.

In the case of a combined GPS-Galileo system, for exam-

ple, there may be up to 20 satellites in view and the

geometry of the five highest satellites may be weak. In this

case, the five satellites above 45 degrees with the best

geometry are used. If ambiguities are not fixed during the

first iteration, a re-search and re-validation scheme is

executed using either the IMB or FT search method,

depending on the quality of the error budget estimation. It

is notable that for five satellites, it takes 15 and 80 itera-

tions with IMB and FT, respectively. The threshold used

for the L1 Test to validate the minimum constellation

solution is smaller than the threshold used for the full

constellation for the following reasons:

• The noise contribution to the observables from the

highest satellites is smaller.

• The number of degrees of freedom is reduced.

If the ambiguity set for the minimum constellation

passes the validation test, the positioning solution with

fixed ambiguities is used to help compute the ambiguities

for the remaining satellites. If the ambiguity set for the

minimum constellation does not pass the validation test,

the position solution with the smallest SSE value is used to

find the ambiguities for the remaining satellites.

Failure detection with fixed ambiguity: test statistic

and threshold

Integrity monitoring relies on a reliable determination of

the test statistic, the decision threshold and the protection

level. The test statistic is based on the probability model

used and the hypotheses investigated. The model provides

a representation of the bias and noise errors. However,

double-differenced observables are not independent.

Therefore, a decorrelation of the dependencies is per-

formed before the construction of the test statistics.

Potential failures are identified based on the measurement

type. Two methods are used to compute the protection

levels: the position uncertainty estimate and a projection of

the test statistic to the position domain. The larger of these

values is conservatively chosen as the protection level.

Figure 4 presents an overview of the C-RAIM module of

the E-CRAIM algorithm.

Fig. 4 C-RAIM software overview (integrated as C-RAIM in Fig. 1)

GPS Solut (2012) 16:65–75 69

123

The test statistic is determined from measured data and

compared with the decision threshold to determine whether

a failure has occurred. The threshold is set such that the

probability of exceeding it under nominal conditions of the

test statistic distribution corresponds to the tolerable

probability of false alert. This threshold then defines the

minimum detectable bias (MDB) (Teunissen 1990), which

describes the smallest bias value in the observables that can

be detected for a given probability of missed detection.

The E-CRAIM algorithm is based on a combination of

different measurements:

• pseudorange

• single-frequency carrier phase (L1).

To eliminate receiver and satellite clock errors as well as

ephemeris errors, double-differencing with respect to a

static reference receiver is used. This introduces correla-

tions between measurements, which are however, mathe-

matical, and the test statistics are therefore constructed

such as to decorrelate those measurements:

Tq ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

rTq Wqrq

n� 4

s

ð3Þ

TL1 ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

rTL1WL1rL1

n� 4

r

ð4Þ

where

• Tq is the pseudorange-measurement-based statistic.

• TL1 is the L1-measurement-based test statistic.

• r is the measurement residual given by rk ¼ yk � Hkxk

where y is the measurement vector; H the design matrix

and x the state (positioning) vector, where the sub-

scripts q and L1 indicate pseudorange and L1 carrier,

respectively.

• Wq and WL1 are the weight matrices accounting for the

covariance, the measurement noise and the correlations,

for the pseudorange and L1 carrier phase observables,

respectively.

• n is the number of satellites.

The chi-square distribution threshold Tn is determined

from the probability of false alert and the number of

degrees of freedom (DOF). Whether it is a central or

noncentral chi-square distribution depends on the absence

or the presence of a range bias error. The threshold T for

the test statistic is given by:

T ¼ r

ffiffiffiffiffiffiffiffiffiffiffi

Tn

n� 4

r

ð5Þ

where r is the standard deviation of the measurement

errors, dependent on the type of observables used, and is

obtained either from external integrity monitoring net-

works, such as the E-GBAS, or estimated at user level. If

the test statistic exceeds the threshold, the test statistic

value is taken as the threshold, reflecting the variability of

the MDB as a result of a change in the SSE. The MDB is

then recomputed for each value of the test statistic

obtained.

Protection levels

The PL is an upper bound that the position error must not

exceed without being detected for a given integrity risk.

The protection levels are computed under two hypotheses:

fault-free and biased measurements. In the first case, the

position uncertainty estimate is obtained from the covari-

ance matrix, projected to the position domain, multiplied

by the fault-free missed detection multiplier, similar to the

method currently proposed for CAT III landings (RTCA

2001). In the second case, the decision threshold of the test

statistic together with the allowed probability of missed

detection determines the MDB. The MDB is projected to

the position domain using the maximum of the ratios of the

position error to the test statistic of all satellites. The larger

of the protection levels is conservatively chosen. Details on

the PL calculation can be found in Ochieng et al. (2007)

and Schuster et al. (2008).

An accurate estimation of the uncertainties of the

observables is key to determining the PLs. Traditional

C-RAIM methods assume constant uncertainty values for a

given observable type, when external uncertainty monitors,

such as GBAS or E-GBAS, are not available. This is not

suitable for aviation applications due to the diversity of the

operational environments. Even in the presence of uncer-

tainty estimations from the reference receivers of an

E-GBAS network, the final estimation of the uncertainties

in the observables, and therefore the solution uncertainties,

need to be carried out at user level. Therefore, two methods

are used, as described in Bai (2008). A conservative esti-

mation takes the maximum uncertainty value from the two

methods as the final estimation.

Analysis and results

The algorithms are subject to a detailed performance

characterization, taking into account operational specifici-

ties, and potential decorrelations between the reference

station and aircraft.

Comparison of different ambiguity search

and validation techniques

Both real and simulated static GNSS data are processed

with the HP-NAV software on an epoch-by-epoch basis to

determine the accuracy and integrity performance of the

70 GPS Solut (2012) 16:65–75

123

enhanced ambiguity resolution method and E-CRAIM

algorithm. A data set from the Ordnance Survey (OS)

station NOTT (Nottingham) and from another receiver at

the University of Nottingham are used, with a baseline

length of 2.5 km and time period of 1 h (3,600 epochs,

from 06:00:00 to 06:59:59 on 29 Jun 2006) to compare the

performances of the various ambiguity resolution and

validation techniques. The C/A code observable on L1 and

carrier phase observables on L1 and L2 are used.

Table 1 compares the performances of the FT and IMB re-

search schemes, using all available satellites pairs in the

ambiguity search. ‘‘Average Knots’’ refers to the average

number of knots searched in 3,600 epochs. ‘‘Reported Fail-

ure’’ is the number of epochs for which the ambiguity is

considered as not resolved, as determined by the ambiguity

validation module. ‘‘Actual Failure’’ corresponds to the

number of epochs for which the correct ambiguity is not

included in the re-search space. ‘‘MD’’ (missed detection) is

the number of epochs for which the wrong ambiguities pass

the validation test. As expected, the FT method results in the

lowest failure rate, because it searches the entire ambiguity

space. However, this method is also computationally inten-

sive, with up to 2187 knots searched for a single epoch. The

IMB method is significantly more efficient, but the failure rate

is relatively poor and insufficient for practical applications.

Table 2 shows the performance of the MCM with the

IMB search scheme. The statistical data for both the min-

imum constellation and the full constellation, after the

ambiguities for the minimum constellation have been

solved, are shown. Compared to the results in the absence

of using MCM, the performance is dramatically enhanced.

The performance is such that it is adequate for real-time

applications, with a very high success rate for short base-

lines, of the order of 99.94% for the data set analyzed.

Accuracy performance characterization

Simulation and field trials are used here to characterize the

accuracy of HP-NAV for CAT III PA and ASM. The high-

level architecture of the methodology used in the evalua-

tion of HP-NAV performance is shown in Fig. 5. Apart

from the controller and trajectory modules, the HP-NAV

platform consists essentially of three modules for the

simulations: GNSS simulator, navigation algorithms, and

validation module. For the field trials, only the navigation

algorithms and validation module are used. Emphasis is

placed on testing the HP-NAV algorithms under repre-

sentative aircraft operational environments.

The raw data for both the simulations and the field trials

were fed to the HP-NAV software. The resulting position

solutions were compared inside the validation module with

the solutions from the respective reference trajectories, on

an epoch-by-epoch basis.

Simulations

Dual-frequency GPS L1 and L2 scenarios were simulated

using a SPIRENT hardware simulator, together with a

Septentrio PolaRx2 GPS L1/L2 receiver. In order to real-

istically emulate precision approach and surface movement

operations, a representative operational environment

including representative airport layout and dimensions, as

well as typical aircraft speeds, were chosen. Figure 6 pre-

sents an overview of the scenarios simulated. Due to lim-

ited computing resources, only one reference receiver from

the GBAS network was simulated. The initial performance

results below here are for nominal conditions represented

by receiver hardware noise (3.98 dB) during all phases,

multipath from buildings in the gate area and atmospheric

conditions during all phases.

Multipath is simulated in the gate area using the ‘‘ver-

tical plane model’’ (Spirent 2007), taking into account

reflections from each of the buildings shown in Fig. 6.

Troposphere and ionosphere variations are modeled with

the STANAG (NATO 1993) and Klobuchar (1975) models,

respectively. Two cases are analyzed in Fig. 7: no cycle

slip detector used and ambiguities resolved at each epoch,

and cycle slip detector is used, as shown on the right-hand

side in Fig. 7, and once the ambiguities are resolved they

Table 1 Performance of FT and IMB without MCM

Method Average knots Reported failure Actual failure MD

FT 63 58 (1.6%) 55 (1.5%) 0

IMB 8 267 (7.4%) 265 (7.4%) 0

Table 2 Performance of MCM-IMB combination

Constellation Average search Reported failure Actual failure MD

Min 1.46 1 (0.03%) 0 (0%) 0

Full 1.0 0 (0%) 2 (0.06%) 0Fig. 5 HP-NAV platform overview

GPS Solut (2012) 16:65–75 71

123

are recomputed only if a cycle slip is detected. As can be

seen in Fig. 7, errors are typically smaller than 20 cm, with

a 3D accuracy of 18 cm (95%). The percentage of correct

ambiguity resolutions for a time period of about 15 min is

about 96%. If no cycle slip detector is used and the

ambiguities are resolved at each epoch, the percentage of

correct ambiguity resolutions drops to about 94%.

The results also show no significant statistical difference

in the number of unresolved ambiguities between the gate

area and the remaining phases of operation. This is despite

the additional presence of multipath in the gate area. Note

that the relatively larger errors are for the epochs where it

is not possible to resolve the ambiguities and thus code

phase are used. An important result is that, while the

ambiguities are not always resolved, there are no events

with an incorrectly resolved ambiguity.

Field trials

In order to confirm the simulation results under real envi-

ronmental conditions, an extended measurement campaign

was carried out by Imperial College London and the

‘‘Institut Fur Flugfuhrung’’ (IFF) of the Technische Uni-

versitat Braunschweig (TUBS—Germany). Real ground

trials, using a dedicated test vehicle, and flight trials, using

an advanced test aircraft, both operated by the TUBS, were

carried out. Both the aircraft and test vehicle were equip-

ped with a dual-frequency GPS/IMU (inertial measurement

unit) receiver and recording unit. The ground setup consists

in four dual-frequency GPS reference receivers and

recording units. Two primary sets of experiments were

carried out: flight trials, including 20 CAT III precision

approaches and landings, and ground trials, including

several hours of static, low-dynamics and medium-

dynamics trials.

For each test, GPS L1 and L2 code and carrier phase

data were recorded at the aircraft and van as well as four

reference stations, including three mobile reference stations

(RS) with Novatel OEMV receivers placed in the vicinity

of the test areas, and a permanent RS on the roof of the IFF

with a Novatel OEM3 receiver. For the flight trials, base-

lines between the RS and the landing threshold varying

between 1,000 and 5,000 m were used. For the ground

trials, baseline distances between 500 and 8,000 m were

used. The four reference receivers were placed at various

distances from the test areas in order to enable a perfor-

mance characterization of the OTF RTK and integrity

algorithms, as a function of baseline. Emphasis for the

trials is placed on testing the system’s response to varia-

tions in multipath and the loss of low-elevation satellites.

Static tests

Tests were carried out, using a Novatel 702L antenna

together with OEMV receiver, at various points located at

5, 15, 25, and 50 m from a reflecting steel surface, sur-

veyed using a TopCon tachymeter (TopCon 2004), in order

to characterize the performance under varying multipath

conditions representative of a typical airport environment.

Reference point accuracies of 23.1 mm were achieved.

Table 3 summarizes these results. As expected, shorter

baselines yield improved accuracies over longer baselines.

Moreover, with the shorter baselines, ambiguities are reli-

ably resolved in 100% of the cases on an epoch-by-epoch

basis. For the longer baseline, ambiguity resolution is

somewhat degraded (about 93%). However, when the cycle

Fig. 6 Overview of simulation scenarios

Fig. 7 Approach and ASM

simulation—accuracy results:

float ambiguities (left); fixed

ambiguities (right)

72 GPS Solut (2012) 16:65–75

123

slip detector is used, the performance is improved to the

level where all ambiguities are successfully resolved.

Protection levels were typically at the level of about 0.5 m,

significantly smaller than the 1.4 m alert limit (AL)

requirement for ASM during the static trials at 50 m from

the steel hangar doors. However, at 5 m from the hangars,

the protection levels increase above the 1.4 m AL due to

satellite masking and the resulting relatively poor satellite

geometry.

Low-dynamics tests

An experimental vehicle fitted with a Novatel OEMV GPS

receiver and Novatel 532 antenna, as well as a Litef LLN-

G1 IMU, was used to make ten laps on a concrete surface

surrounded on three sides by trees and buildings, featuring

many chances for blockages of satellite visibility and

multipath. Two reference systems were used:

• a Leica TCR 805 totalstation with an automatic

tracking function;

• a secondary system coupling the OEMV with an IMU.

Figure 8 shows the test van with the totalstation in the

foreground. The IMU and Novatel antenna are fixed to the

roof of the van in a weatherproof enclosure. The prism for

the totalstation is located at the front of the roof of the test

van. A pattern featuring backwards passages was chosen

such that the reflector was permanently visible from the

totalstation.

Figure 9 gives an overview of the measurement results.

The shaded areas depict the motion of the van, while the

non-shaded areas correspond to the static periods. As

expected, the accuracy of the static phases of the low

dynamic data is very good, with a value of 0.090 m (95%),

despite the presence of strong multipath conditions. The

accuracy of the measurements of the dynamic portions of

the low-dynamics data is at 0.093 m (95%), very similar to

the static portions. This is as expected, since ambiguities

are resolved on an epoch-by-epoch basis. The protection

levels computed by the E-CRAIM software (Bai et al.

2008) were below 1.4 m, which is the alert limit for airport

surface movement, 100% of the time. Results from shorter

baselines showed no significant difference with respect to

the longer baseline under the nominal conditions encoun-

tered during these measurement trials. However, it is

anticipated that, in the presence of strong ionosphere

conditions, shorter baselines will provide improved per-

formance over the longer baselines. This is the subject of

further research.

Flight tests

The flight trials were carried out using an advanced test

aircraft, a Dornier Do-128-6 owned by the IFF. Using

standard approach procedures, 20 CAT III precision

approaches were taken. The distance flown along the

approach is approximately eight nautical miles and the

Fig. 9 Low-dynamics data results (8,000-m baseline)

Fig. 8 Left: Experimental van and totalstation; Right: Van trajectories (� Google Earth and AeroWest)

Table 3 Static GPS L1/L2 measurements: steel hangar doors at

various distances from the receiver

Distance to steel hangar

doors (m)

Baseline

(m)

Accuracy—m

(95%)

PL \ 1.4 m

(%)

5 1,400 0.048 74

7,000 0.051 74

50 1,400 0.036 100

7,000 0.070 100

GPS Solut (2012) 16:65–75 73

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approach was aborted on reaching a height of about 5

meters above the runway.

The reference trajectory for the flight trials used a

combination of a coupled IMU/GPS system, featuring an

iMAR iTRACE RF-200 IMU and a Novatel OEMV,

together with a laser tracker, manufactured by IBEO and

operated by NLR personnel, using a special reflector fitted

to the aircraft.

A posteriori analyses suggest that the reference system

accuracy during the CAT III precision approaches was of

the order of 0.7 m. Given expected positioning accuracies

of the HP-NAV software during the CAT III precision

approach of the order of 0.20 m, as obtained with the

simulations, a detailed validation of the HP-NAV software

was not possible. However, analyses indicate that there is

good agreement between the positions extracted from the

reference trajectory and the positions computed by the HP-

NAV software all along the approach. Further work is

required to improve the accuracy of the reference trajectory.

E-CRAIM position-domain performance

characterization

In order to determine the suitability of the E-CRAIM

algorithm for surface movement, its performance was

analyzed under varying fault scenarios. Initially, the

emphasis was placed on the detection of cycle slips for the

cases where the preprocessor was unable to identify and

correct for the cycle slip, and on the detection of localized

ionosphere anomalies that are undetectable with the

E-GBAS architecture.

Data were simulated with the GPS Simulation Software

(GSS) developed at Imperial, using a standard GPS

24-satellite constellation, making use of recorded ephem-

eris on June 29, 2006. A baseline length of 5 km between

the reference and aircraft was chosen. The results shown

below are for static aircraft only. Realistic multipath on

both carrier phase observables, with variations up to one

quarter of the wavelength, which is the theoretical

maximum for carrier phase, was assumed. Residuals from

E-GBAS under nominal conditions, i.e., assuming that

E-GBAS monitors have not detected any ionosphere

anomaly, were added to all observables. The assumption is

made that cycle slips were not detected, and hence not

fixed, by the preprocessor.

Figure 10 shows typical results. The threshold for the

test statistics is determined by the number of satellites and

thus changes upon a change in satellite configuration. In

the absence of faults, the test statistic is well below the

threshold and most of the time, the protection levels are

below the alert limit for surface movement (1.4 m). On

introducing a single cycle slip on carrier L1, the test sta-

tistic increases, although not sufficiently to be detected

reliably. However, while the accuracy of the position

solution is degraded, it is well below the accuracy

requirements of surface movement. As expected, the larger

the number of cycle slips, the easier the detection. Also

shown are the impacts of cycle slips on multiple satellites

simultaneously. The effect of a single cycle slip on two

satellites simultaneously is of the same order of magnitude

as the effect of two cycle slips on a single satellite. Test

case (f) displays the impact of a localized ionosphere

anomaly, which was assumed not to have been detected by

E-GBAS, on the E-CRAIM algorithm. A temporal varia-

tion of 1 cm/s is shown in the example below, leading to a

maximum ionosphere bias of 1 m over 100 s. The integrity

algorithm detects the anomaly well before it creates a

hazard to the aircraft, since the alert limit is only exceeded

approximately 40 s after the anomaly is detected. The

accuracy of the position solution is about 0.08 meters

(95%), similar to the static and low-dynamics test results

shown previously in this paper.

Conclusions

We developed enhanced multi-frequency and multi-con-

stellation GNSS-based navigation algorithms for airport

Fig. 10 Performance of E-CRAIM for airport surface movement—sample results

74 GPS Solut (2012) 16:65–75

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surface movement, with focus on the integrity function

E-CRAIM. The chosen method of combining LAMBDA

with MCAR was shown to significantly enhance the

probability of correct ambiguity resolution. A new ambi-

guity validation method using a combination of the F-test

for the L1 observables and the IA method for the widelane

and extra-widelane observables was shown to improve the

speed at which the ambiguities are validated. One of the

novelties in this paper is the introduction of a re-search and

re-validation method for those scenarios where the ambi-

guity could not be fixed on the first iteration. The IMB

scheme was optimized in terms of efficiency for real-time

short baseline applications on the airport environment. In

order to reduce the high computational requirements, a

two-step procedure using the MCM is proposed. This was

shown to result in a significantly higher percentage of

correct ambiguity resolutions with a significantly higher

efficiency. A novelty of the E-CRAIM algorithm is that it

uses a multi-step approach to enhance integrity perfor-

mance. The use of external integrity information, such as

from the E-GBAS, improves the overall E-CRAIM per-

formance, compared to traditional C-RAIM.

The algorithms were shown to be robust against large

noise levels, as well as cycle slip conditions undetected by

the preprocessor and ionosphere fronts undetected by the

E-GBAS. The combined algorithm was shown to be robust

and at a level of performance where it has the potential to

meet the airport surface movement navigation system

performance requirements.

Acknowledgments The authors would like to thank the students

and members of the Institute of Flight Guidance at the Technical

University of Braunschweig for their support of the flight trials.

Aerodata, Braunschweig, are gratefully acknowledged for providing

the laser tracker. Many thanks go to Harald de Haan (NLR) for the

operation of the laser tracker and to Holmer Denks (DLR) for car-

rying out the initial simulations.

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Author Biography

Wolfgang Schuster is a senior researcher in Positioning and

Navigation Systems (PNS) and Air Traffic Management (ATM), at

the Centre for Transport Studies in the Department of Civil and

Environmental Engineering at Imperial College London. His work

focuses on the development of methodologies and technologies to

improve the efficiency and safety of air transport and to minimise

aviation’s impacts on the environment. In regard to PNS, his main

contributions are in the domain of intelligent integrity monitoring,

specifically for precision landings and airport surface movement. In

the ATM domain, his expertise is in the development of advanced

collision risk models, high-performance trajectory prediction and

efficient conflict detection and resolution tools, with emphasis on

modelling uncertainties. Dr. Schuster holds a DPhil in High-Energy

Physics from the University of Oxford (UK) and a commercial pilot

license.

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