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2008 IEEE Region 10 Colloquium and the Third International Conference on Industrial and Information Systems, Kharagpur, INDIA December 8 -10, 2008. >377 < 978-1-4244-2806-9/08/$25.00© 2008 IEEE Modeling and Implementation Of Wireless Embedded System For Intelligent Transport System Application Rabindranath Bera 1 , Sourav Dhar 1 , D. Kandar 1 1 Sikkim Manipal Institute Of Technology, Sikkim Manipal University, Majitar, Rangpo, East Sikkim -737132 (INDIA) E-mail: [email protected] , [email protected] N.B. Sinha 2 2 College of Engineering and Management , Kolaghat, (WB), INDIA Manojit Mitra 3 3 Bengal Engineering & Science University , Howrah, (WB), INDIA Abstract- Intelligent Transport system (ITS) deals with the remote sensing as well as access to the internet and multimedia on the move. There is a need for a system that supports both remote sensing and communication aspects with simple and reconfigurable hardware. In the present work MATLAB/SIMULINK based modeling of such a system is done based on DSSS. Hardware realization of this model is achieved through the SignalWAVE software defined radio (SDR). From the simulation results two empirical formulae are developed for target detection. Key words— Intelligent Transport system (ITS), Software Defined Radio (SDR), Direct Sequence Spread Spectrum (DSSS), Radar. I. INTRODUCTION THE US FCC has allocated 75 MHz of spectrum in the 5.9 GHz band for Dedicated Short Range Communications (DSRC) to enhance the safety and productivity of the nation’s transportation system [1]. The USDOT and IEEE have taken up the standardization of the associated radio technology Wireless Access for Vehicular Environments (WAVE)[2]. The United States Department of Transportation has declared that the reduction of vehicular fatalities is its top priority [3]. More health care dollars are consumed in the United States treating crash victims than any other cause of illness or injury [3]. The connection between 802.11 radios and safety provides the strongest case yet for getting such radios into cars. The 21 st century finds a lots of developments in the fields of mobile communication and remote sensing. The authors are engaged in the developments of systems related to both of the fields over last 20 years. They are now motivated to develop the wireless embedded system to be useful for intelligent transport system application converging both of the fields. II. CHOICE OF TECHNOLOGY FOR ITS There are a few possibilities concerning the communication between the road infrastructure and the vehicles as well as inter car communication. These are both subject to similar constraints and limitations. For ITS application the communication will have to be established and completed within a very short period of time at high speed. This is a limitation concern only short range technologies. The use of long range technologies like 3G would eliminate those issues.[4] A. Short range technologies In this work various wireless technologies are studied. One group of communication standards concerned short range radio communication. This does not involve the necessity of using the services of a third party thus reducing at least the initial costs of development and increasing the ease of deployment and implementation of the framework. Dedicated Short Range Communications (DSRC) Dedicated Short-Range Communications (DSRC) is a technology that is built on top of 802.11a standard and uses the spectrum of 5.9GHz to transfer the data over a wireless link. It is perceived as an emerging standard for Road to Vehicle Communication (RVC) and Inter-Vehicle Communication (IVC) systems. The 5.9Ghz band consists of seven channels (ten megahertz). One of them is a control channel, the other six are service channels. DSRC involves both vehicle-to-vehicle and vehicle-to-infrastructure communications and is expected to support both safety/public safety and non-safety applications. The focus is however on safety applications as the non-public safety use of the 5.9 GHz band is perceived as inappropriate if it leads to degrading the performance of safety/public safety applications. The main attribute of safety applications is to save lives by warning drivers about potentially dangerous situations and give them additional time to react. Therefore, the availability, reliability and low latency are the basic requirements of such applications. [5] WiFi and ad-hoc networks As wireless enabled handhelds are becoming more popular and are becoming widely used together with GPS modules in order to provide relevant information to the driver, we cannot ignore the possibility to use them as a potential “On-Board Units” that may be used either for inter-vehicle or road-to- vehicle communication. The possible use of external antennas in the cars is the subject of possible feasibility study. These technology, together with ad-hoc routing protocols may be a possible solution of the problems of ITS. 802.11b standard IEEE Kharagpur Section & IEEE Sri Lanka Section

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Page 1: [IEEE 2008 IEEE Region 10 and the Third international Conference on Industrial and Information Systems (ICIIS) - Kharagpur, India (2008.12.8-2008.12.10)] 2008 IEEE Region 10 and the

2008 IEEE Region 10 Colloquium and the Third International Conference on Industrial and Information Systems, Kharagpur, INDIA December 8 -10, 2008. >377 <

978-1-4244-2806-9/08/$25.00© 2008 IEEE

Modeling and Implementation Of Wireless Embedded System For Intelligent Transport

System Application

Rabindranath Bera1, Sourav Dhar 1 , D. Kandar1

1 Sikkim Manipal Institute Of Technology, Sikkim Manipal University,

Majitar, Rangpo, East Sikkim -737132 (INDIA) E-mail: [email protected], [email protected]

N.B. Sinha2 2 College of Engineering and Management , Kolaghat,

(WB), INDIA

Manojit Mitra 3 3 Bengal Engineering & Science University , Howrah,

(WB), INDIA

Abstract- Intelligent Transport system (ITS) deals with the

remote sensing as well as access to the internet and multimedia

on the move. There is a need for a system that supports both

remote sensing and communication aspects with simple and

reconfigurable hardware. In the present work

MATLAB/SIMULINK based modeling of such a system is done

based on DSSS. Hardware realization of this model is achieved

through the SignalWAVE software defined radio (SDR). From

the simulation results two empirical formulae are developed for

target detection.

Key words— Intelligent Transport system (ITS),

Software Defined Radio (SDR), Direct Sequence Spread

Spectrum (DSSS), Radar.

I. INTRODUCTION

THE US FCC has allocated 75 MHz of spectrum in the

5.9 GHz band for Dedicated Short Range Communications (DSRC) to enhance the safety and productivity of the nation’s transportation system [1]. The USDOT and IEEE have taken up the standardization of the associated radio technology Wireless Access for Vehicular Environments (WAVE)[2]. The United States Department of Transportation has declared that the reduction of vehicular fatalities is its top priority [3]. More health care dollars are consumed in the United States treating crash victims than any other cause of illness or injury [3]. The connection between 802.11 radios and safety provides the strongest case yet for getting such radios into cars. The 21st century finds a lots of developments in the fields of mobile communication and remote sensing. The authors are engaged in the developments of systems related to both of the fields over last 20 years. They are now motivated to develop the wireless embedded system to be useful for intelligent transport system application converging both of the fields.

II. CHOICE OF TECHNOLOGY FOR ITS

There are a few possibilities concerning the communication between the road infrastructure and the vehicles as well as inter car communication. These are both subject to similar constraints and limitations. For ITS application the communication will have to be established and completed

within a very short period of time at high speed. This is a limitation concern only short range technologies. The use of long range technologies like 3G would eliminate those issues.[4]

A. Short range technologies

In this work various wireless technologies are studied. One group of communication standards concerned short range radio communication. This does not involve the necessity of using the services of a third party thus reducing at least the initial costs of development and increasing the ease of deployment and implementation of the framework. Dedicated Short Range Communications (DSRC)

Dedicated Short-Range Communications (DSRC) is a technology that is built on top of 802.11a standard and uses the spectrum of 5.9GHz to transfer the data over a wireless link. It is perceived as an emerging standard for Road to Vehicle Communication (RVC) and Inter-Vehicle Communication (IVC) systems. The 5.9Ghz band consists of seven channels (ten megahertz). One of them is a control channel, the other six are service channels. DSRC involves both vehicle-to-vehicle and vehicle-to-infrastructure communications and is expected to support both safety/public safety and non-safety applications. The focus is however on safety applications as the non-public safety use of the 5.9 GHz band is perceived as inappropriate if it leads to degrading the performance of safety/public safety applications. The main attribute of safety applications is to save lives by warning drivers about potentially dangerous situations and give them additional time to react. Therefore, the availability, reliability and low latency are the basic requirements of such applications. [5] WiFi and ad-hoc networks

As wireless enabled handhelds are becoming more popular and are becoming widely used together with GPS modules in order to provide relevant information to the driver, we cannot ignore the possibility to use them as a potential “On-Board Units” that may be used either for inter-vehicle or road-to-vehicle communication. The possible use of external antennas in the cars is the subject of possible feasibility study. These technology, together with ad-hoc routing protocols may be a possible solution of the problems of ITS. 802.11b standard

IEEE Kharagpur Section & IEEE Sri Lanka Section

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2008 IEEE Region 10 Colloquium and the Third International Conference on Industrial and Information Systems, Kharagpur, INDIA December 8 -10, 2008. >377 <

978-1-4244-2806-9/08/$25.00© 2008 IEEE

operates in 2.4GHz ISM spectrum. The original 802.11 wireless standard specifies the maximum throughput of 1 Mbps and 2 Mbps. As a medium it uses radio waves and a technique called frequency hopping spread spectrum (FHSS) or direct sequence spread spectrum (DSSS). The frequency hopping technique divides the 2.4 GHz band into seventy five 1-MHz sub channels. Both the sender and a receiver agrees on a hopping pattern, and data is sent over a sequence of the sub channels. Each exchange of the information within the 802.11 network occurs over a different hopping pattern. These patterns are designed to minimize the chance of two senders using the same sub channel simultaneously and reducing the risk of a collision. [6] Currently most widespread version of 802.11 standard is b/g version. It provides high speed network access with the bit rates of 11/54Mbps. 802.11g provides higher data rates as well as stronger security. Parasitic routing

Parasitic Routing is a technology that is in the early phase of research and development. The idea of store-and-forward algorithm seems a good solution for this problem as well. It enables the cars to store important information for specified periods of time and forward them as soon as the destination becomes available. “The major difference between the store-carry-and-forward paradigm and the store-and-forward paradigm employed in the majority of routing protocols is that the former of the message takes advantage of additional resource of a node, its mobility. Store-and-forward protocols take advantage of an agent’s buffer, transmission capabilities, some processing time, and some energy, while store-carry-and-forward protocols use all the previous, and use a nodes mobility to enhance the likelihood of delivery, in doing so a message will most likely be held in an agent’s buffer, or queue, for an extended period of time also. The mobility may be random, designed in advance to deliver messages, or may be dictated on-the-fly in an attempt to ensure speedy delivery of all messages.”[7]

B. Long range communication

The use of long range technologies like 3G eliminates most of the problems that emerge while trying to communicate to nodes moving at high speeds. It may however imply the need of involving third parties like mobile operators in routing architecture. 3G and GPRS

Both of the technologies allow for acceptable data transfers and due to high mobile network coverage may give satisfactory results in road-to-vehicle communication. 3G is mobile providers’ technology. The services are provided by companies operating their own wireless networks and offer their services to end-users. The advantage of 3G is that a mobile base station can provide service to nodes that are as far as several kilo meters away and moving at high speeds (up to 100km/h). The base stations are interconnected by backhaul network and provide access to standard PSTN network. Currently 3G offers the data throughput of 384kbps. In the

future the specs of the standard take into consideration expanding the available bit rate to 2Mbps[8]. GPRS (often referred to as 2.5G) is the packet oriented extension of GSM. It allows data transmission exceeding 100kbps. The packet oriented approach makes it possible for many users to share the radio resource. Thanks to this approach flexible access is possible while idle users can still be online[9]. Interoperability for Microwave Access (WiMax)

WiMAX stands for Worldwide Interoperability for Microwave Access. It describes the systems that pass interoperability tests for the IEEE 802.16 standards. Products that pass the conformity tests for WiMAX are capable of forming wireless connections between them to permit the carrying of internet packet data. The idea of WiMax is in some ways similar to WiFi. It however, has many improvements that allow to carry the signal at high bit rates over longer distances than in WiFi. 802.16 supports point-to-multipoint architecture. A base station is a central point that handles multiple independent sectors. Downlink covers the transfer of the data to service subscriber and is multiplexed in TDM fashion. Uplink on the other hand is shared between service subscribers in TDMA fashion [10].

III. REMOTE SENSING TECHNOLOGIES USED IN ITS

A number of remote sensing devices are used for ITS application. The commonly known remote sensing devices based on using sensors are pneumatic road tubes, inductive loops, magnetic sensors, piezoelectric sensors, video cameras, infrared lasers sensors, microwave (MW) radars, and ultrasonic sensors [11]–[15]. Any kind of sensor will provide a specific mechanism of detecting and classifying vehicles and has its own advantages and disadvantages. Since user needs and classification conditions can differ, no sensors and corresponding techniques have proven to be the best for all possible applications [11][16].Therefore, any new classification technique providing specific advantages can be of great interest for the highway agencies. In certain situations, some benefits can be provided by MW radar sensors. MW sensors do not require installation in the roadway, thus making sensor calibration and maintenance easier and less disruptive. This is important for use in high-volume urban freeways, highways, and other locations where access to the roadway is extremely limited and expensive. Another strength is that MW radar sensors are largely immune to adverse weather and light conditions or vibrations. Such properties have lead to intense practical interest in MW classification systems [11][16]. In various traffic management applications, roadside mounted and forward-looking frequency-modulated continuous wave (FMCW) and noise-correlation radar units combined with continuous-wave (CW) Doppler sensors are commonly used [11], [17], [18]. Both of these MW radar sensors are primarily intended for extraction of vehicle length and shape information. However, high resolution in the distance domain is required to obtain accurate vehicle shape information from a

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roadside mounted sensor. As a result, the FMCW radar classification system considered in [17] gives only 75% accuracy when separating traffic into five categories. A forward-scattering CW Doppler radar was used to obtain a vehicle signature in [19]. The vehicle signatures obtained using Doppler radar were also used in [20] and [21]. Because of the high variability in geometric shapes of vehicles, extracting sufficient information from the signatures for the detailed classification of vehicles is a difficult task. Therefore, such types of classification systems are feasible for categorization into a relatively small number of vehicle classes such as small, medium, and large cars [19], [20] or tracked and wheeled vehicles [21]. To enhance classification accuracy and increase the number of vehicle types being classified, a vehicle classification system using down-looking spread-spectrum MW radar was proposed in [22]. In this system, the sensor was mounted above the roadway in such a way that vehicles pass directly below the sensor. Such installation made it possible to use the vehicle height profile as the main feature for classification. As a result, the reported classification accuracy was 99% for five vehicle types and a data set of 1706 vehicles.

IV. MODELING OF THE SYSTEM

The shown model (Fig.1) is capable enough to serve two applications at a time, i.e., communication as well as remote sensing. The data along with the other signals is sent over the channel and after being received at the receiver, the received signal is processed in such a way that we can have a reliable communication between the transmitter and the receiver.

Fig.1 WCDMA model Capable of Doing both Communication and Radar

Operations

The very basic difference between communication and remote sensing is that, an error-free communication needs to remove any signal distortion added to the transmitted signal in the channel at the receiver side, whereas, remote sensing takes care of those distortions and then describe, rather define the target based on those information. This paved the authors the way to add on the WCDMA communication model with the remote sensing application. Here, the communication model is modified in some way to tap out the information regarding the distortions which are otherwise corrected at the receiver. So, this is how the target is predicted. The GUI is displaying the target information. Fig. 2 shows the Simulation Model of DSSS radar used for target detection. The Pilot data generator generates the reference data which is to be spread by the spreading code. The Spreading system consists of a spreading code generator

which is multiplied with the incoming pilot to generate the spreaded data which is passed to the RRC transmit filter. After spreading the bandwidth becomes equal to 3.84 MHz.

Fig. 2 Simulation Model for DSSS radar used for target detection.

RRC (Root Raised Cosine) Transmit filter is the part of the matched filter. It does base band wave shaping. Up-sampling factor = 8 is used here. The RF Transmitter section is upconverting the incoming baseband signal to the particular RF. RF transmitter is tunable for the full band of RF frequency from 300MHZ to 3000 MHz. The channel is considered to be a Rayleigh fading channel. It is used for modeling of the multi-path environment. Also there is a provision for simulating the system with AWGN channel conditions. Target is modeled where it is assumed that the incident vector of radio waves will be modified by the target in terms of amplitude, phase and frequency. The RF receiver first receives the incoming signal and adds some noise to it (through its noise temperature block), this noise corresponds to the noise being actually added to the signal at the RF receiver when the receiver is implemented practically. Now, the signal is passed through an amplifier and a front end filter followed by a low noise amplifier to increase the strength of the data signal. Now, there is a requirement of converting the RF band signal into the baseband signal which is achieved by a down converter mixer which is effective in simply removing the RF carrier thus making the signal suitable for baseband processing. This signal is further amplified using final stage amplifier. RF receiver is also tunable for the entire band of 300 MHz to 3000MHz. RRC receive filter is the other part of the matched filter. After passing through the RRC receive filter, the signal bandwidth again becomes equal to 3.84 MHz The baseband receiver consists of - i) Reference Code Generator: This generates the reference code which was actually transmitted. The channel estimation is done by just observing the difference between the reference code and the received code. ii) Chip Correlator: Here the incoming signal is decorrelated using the same spreading sequence which was previously used during spreading at the transmitter end. For the correct decorrelation the received signal is time synchronized with the reference spreading code, the delay information being provided by the channel estimation. iii) Symbol Correlator: This is used in predicting the changes encountered by the originally transmitted code which are

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basically the amplitude, phase and frequency changes. If the channel conditions are known beforehand, the target information can be extracted easily. iv) Carrier frequency recovery: This block is responsible for extracting the frequency offset in the transmitted code and this frequency offset is nothing but the Doppler frequency of the target. The algorithm used is a non data aided and clock aided open loop algorithm based on 2P power method. v) Energy estimation: This subsystem calculates the amount of energy being absorbed by the target. The subsystem does so by observing the change in the energy of the received code and the energy of the transmitted code with no target present in the environment. This difference of the energies is used to get the energy absorption by the target, the approach being detailed ahead. vi) Phase estimation: This block uses the frequency offset value for the received code and then with some mathematical calculations gives us the phase change caused in the signal by the target alone. Finally there is a graphical user interface (GUI) which displays the extracted information.

V. DETECTION OF SINGLE TARGET THROUGH END TO END

SIMULATION

A. Channel Estimation

During the transmission and reception of signal the multi-paths arrive at the receiver with different delays. For the RAKE receiver to operate correctly it must have the ability to distinguish those received multi-paths, the ability being termed as the resolving power of the system. Resolving power (in sec) = 1 / (bandwidth) Resolving power (in meter) = c / (bandwidth); where, c = 3e8 m/sec. Now this resolving power is wholly decided by the bandwidth of the system, which comes out to be 200ns for the 5 MHz system. Now, to properly estimate the channel a simulation was conducted to observe the effect of different delays amongst the fingers in this system. It was noted that when the delays amongst the fingers were given less than 200ns a lot of jittering was found in the constellation of the Rx signal showing its constraint of not distinguishing the different multi-paths while for delays greater than or equal to 200ns the jittering was greatly reduced. Since radar systems have a problem of ground clutters which can have multi-paths having relative delays of less than 200ns. So, the solution itself asks for improving the resolving power of the system and therefore there is a need for increasing the bandwidth.

B. Doppler Frequency Extraction

Considering that the target is rotating, we need to find out the frequency of rotation of the target. In WCDMA system this frequency change is depicted in the form of a phase change in the phase of the received signal. To extract the Doppler frequency from the received phase we have used a carrier frequency recovery algorithm. This algorithm is based

on a delay-and-multiply scheme. This particular algorithm is a non-data-aided and clock-aided open loop algorithm based on the 2P-Power Method and helps in extracting the frequency from the phase of the received signal.

C. Target Phase Extraction

Since this RAKE receiver has a de-rotator block which de-rotates incoming signal to its original phase by subtracting the extra phase offset. We note the value of the initial phase offset considering there is no target taking into consideration that there is some phase error due to delay caused in the channel. Now as the transmitted energy is reflected by the target there is some phase difference being introduced here by the target. Now as discussed earlier in frequency extraction, the phase difference in the signal after striking the target is due to Doppler frequency also, in addition to the normal phase offset due to target. It is noted that the change in the overall phase of the signal due to presence of frequency offset due to the target. What was observed that for 1 Hz of frequency offset due to the target, the change in the total phase was about 0.624 radian (let this value be ‘k’). The interesting thing obtained was that for any frequency offset the net change in phase was a perfect multiple of ‘k’(TABLE I). This very fact paved the authors the way to determine the phase introduced by the target. So, the phase introduced by the target could be easily separated out and that can be mathematically expressed as – Phase offset due to the target = P – P i – (F x k) (1)

Where, Pi = initial phase of the signal for no target condition F = frequency offset due to the target P = total phase of the received signal. Observations (For First Finger) Initial Phase of the Rx Signal (Pi) = 2.974 radian

TABLE I. PHASE INFORMATION FROM THE TARGET

Phase offset, Pi(in degree)

Frequency Offset, F (in Hz)

New phase of Rx Signal, P (in radian)

Phase detected [P- Pi –( F x k)] (in degrees)

5 1 - 6.196 5.01 8 2 - 6.144 8.007 12 4 - 6.075 11.98 15 7 - 6.023 14.94 30 6 - 5.761 29.95

D. Extraction Of the Amount Of Energy Absorbed by the

Target

In real world scenario all the energy transmitted is not reflected towards the receiver by the target. Hence for measuring the amount of energy scattered or lost due to the target, we simulate in our simulation an environment where we introduce an additional loss of energy in channel. We see that since rake has this normalizing factor by which it performs automatic gain controlling in dynamic environment to give equalized output at the receiver. We utilize this property of rake combiner and determine by what amount this factor is changing with the amount of energy lost due to target

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and the observation below shows us the variation of normalization factor with the energy lost in dB. Observation (For First Finger)

Initial Normalization Factor(C) = 9.01131e-2

TABLE II. ENERGY INFORMATION FROM THE TARGET

Path loss (in dB)

New Normalization Factor (D)

Variation Factor (C/D)

10 9.01131e-3 1e1 20 9.01131e-4 1e2 30 9.01131e-5 1e3 40 9.01131e-6 1e4 50 9.01131e-7 1e5

Having a look (TABLE II) on the variation factor we can have a mathematical formulation to predict the amount of energy lost due to the target. The relation is given as – 10

(p/10) = (Initial Normalization Factor) / (New Normalization

Factor) (2)

Where, p = Energy lost due to the target.

VI. IMPLEMENTATION THROUGH SDR

For implementation of this model SignalWAVE SDR is used. It consists of a DSP and an FPGA section. Thus two separate models were developed to realize the DSSS radar system.

A. FPGA model(Fig. 3)

• The system generator and board configurations are attached with the model.

• The FPGA clock speed of 30 MHz is to be selected therefore in the System Generator the simulation time of 33.3 ns is set and ADC and DAC sample times are also set as 1/3e7 sec. While DSP bus speed is set to 48KHz.

• In the model DSP BUS takes care of taking the modulated signal out of the DSP processor into the FPGA and afterwards it sends the signal to the DAC port for the transmission.

Note: In the SignalWAVE the DAC and ADC ports are directly connected to the FPGA section. • This signal from the DAC is looped back to the

SignalWAVE through the ADC port , and then it is re-sent to the DSP processor for the demodulation using the custom registers.

B. DSP model(Fig.4)

Transmitter section

• Pilot generator: It is used to generate symbol pilot data at sample rate of 256/24000 s.

• Spreading system: It consists of OVSF code generator which spreads the incoming pilot symbol by a factor of 256.The resultant sample time becomes 1/24000 s.

• Scrambling system: The incoming spreaded code is further scrambled using a gold code sequence.

Fig. 3 Implementation of DSSS system- FPGA section

• Transmit filter: The signal is passed through the root raised cosine filter which shapes the incoming signal by oversampling by two. Now the sample time is 1/48000 sec.

• AWGN channel: Since there is no external transmission through antenna. Hence we have modelled awgn channel in DSP. Here we specify the SNR level which adds white Gaussian noise into the incoming signal.

• Target: Target is modelled as a path loss block where loss is given in terms of db.

• DSP bus: The resultant signal is passed to fpga from where it is passed out through DAC by the dsp bus which is opened in write mode. Its sample time is set to 1/48000 sec.

Receiver Section

• Custom register: It is used to receive the incoming signal from ADC of FPGA into DSP.

• Receive filter: It reshapes the incoming signal by further lowering the noise floor.

• Power spectrum scope: The received signal along with the original signal before passing through channel and target is fed to this scope. Here we visualize the difference in energy of these two signals which is the amount of loss given in the target.

The resulting spectrum is observed in the spectrum analyzer of MATLAB/SIMULINK. Spectrum of Fig. 5 is obtained with no target placed. Spectrums of Fig. 6 and Fig. 7 are obtained for 10dB and for 30dB path loss respectively. Black and blue curves are representing transmitted and received signals respectively.

Fig. 4 DSP Section for the DSSS radar system

VII. COMPARISON WITH EXISTING SYSTEMS

Use of radar for ITS applications is described in [23]-[26], are analog radar. The DSSS based Radar described in this paper is

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a new concept in ITS field. The existing systems are not using any reconfigurable hardware. Implementation of digital radar using SDR is proposed hare that supports reconfiguareability.

Results of the implementation

VIII. CONCLUSION

In this paper a system that is capable of performing both communication and remote sensing assignments is proposed. Two important formulae are developed from the simulation results for finding Phase offset due to the target and for extraction of the amount of energy absorbed by the target respectively. In the implementation part, the effect of path loss is observed and extraction of phase and frequency are yet to be done.

ACKNOWLEDGEMENT

Authors would like to thank Mr. S.Shome, Technical assistant and B.Tech final year students Mr. Ashis Misra, Saurabh Shah and Mr. Avinash of Electronics and Communication Engineering Department, SMIT, for their extensive support.

REFERENCES

[1] USFCC, Report and Order, FCC 03-324, Dec. 2003. [2] http://www.standards.its.dot.gov/Documents/dsrc_advisory.htm [3] J. Paniati (Dir., ITS, U.S Dept. of Transp.), Intelligent Safety Efforts in America,10thITSWorldConf.http:// www.its.dot.gov/speeches/madridvii2003.ppt, Nov. 17, 2003. [4] Maciej Wieckowski "A communication framework for Pedestrian Detection System" dissertation, Master of Science in Computer Science.University of Dublin, Trinity College, Web site: https://www.cs.tcd.ie/publications/tech-reports/reports.07/TCD-CS-2007-18.pdf [5] Jijun Yin, Tamer ElBatt, Gavin Yeung, Bo Ryu, Stephen Habermas, Hariharan Krishnan, Timothy Talty (2004). “Performance Evaluation of Safety Applications over DSRC Vehicular Ad Hoc Networks”. Retrieved from UCLA Computer Science Department Web site: www.cs.ucla.edu/~gavin/pub/p52_Yin.pdf [6] “Introduction to Wireless LAN and IEEE 802.11” Retrieved from Tutorial-Reports.Website:http://www.tutorialreports.com/wireless/wlanwifi/index.php [7] Eoin Bailey (2005). “An Implementation of a Parasitic Routing Algorithm”,Retrieved from Computer Science Department The University of

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Fig.5 Spectrums of Tx and Rx signals through SDR

Fig.6 Spectrums of Tx and Rx signals through SDR

Fig.7 Spectrums of Tx and Rx signals through SDR