Vehicle detection and classification using LTE‐CommSense

IET Radar, Sonar & NavigationVolume 13, Issue 5 p. 850-857 Research ArticleFree Access Vehicle detection and classification using LTE-CommSense Santu Sardar, Corresponding Author Santu Sardar santusardar@anurag.drdo.in ANURAG, Defence Research & Development Organization, India Department of Electrical Engineering, IIT Hyderabad, IndiaSearch for more papers by this authorAmit K. Mishra, Amit K. Mishra Department of Electrical Engineering, University of Cape Town, South AfricaSearch for more papers by this authorMohammed Zafar Ali Khan, Mohammed Zafar Ali Khan Department of Electrical Engineering, IIT Hyderabad, IndiaSearch for more papers by this author Santu Sardar, Corresponding Author Santu Sardar santusardar@anurag.drdo.in ANURAG, Defence Research & Development Organization, India Department of Electrical Engineering, IIT Hyderabad, IndiaSearch for more papers by this authorAmit K. Mishra, Amit K. Mishra Department of Electrical Engineering, University of Cape Town, South AfricaSearch for more papers by this authorMohammed Zafar Ali Khan, Mohammed Zafar Ali Khan Department of Electrical Engineering, IIT Hyderabad, IndiaSearch for more papers by this author First published: 01 May 2019 https://doi.org/10.1049/iet-rsn.2018.5231Citations: 2AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract The authors demonstrated a vehicle detection and classification method based on long-term evolution (LTE) communication infrastructure-based environment-sensing instrument, termed as LTE-CommSense by the authors. This technology is a novel passive sensing system which focuses on the reference signals embedded in the sub-frames of LTE resource grid. It compares the received signal with the expected reference signal, extracts the evaluated channel state information (CSI) and analyses it to estimate the change in the environment. For vehicle detection and subsequent classification, authors' setup is similar to a passive radar in forward scattering radar (FSR) mode. Instead of performing the radio frequency (RF) signals directly, the authors take advantage of the processing that happens in a LTE receiver user equipment (UE). The authors tap into the channel estimation and equalisation block and extract the CSI value. CSI value reflects the property of the communication channel between communication base station (eNodeB) and UE. The authors use CSI values for with vehicle and without vehicle case in outdoor open road environment. Being a receiver-only system, there is no need for any transmission and related regulations. Therefore, this system is low cost, power-efficient and difficult to detect. Also, most of its processing will be done by the existing LTE communication receiver (UE). Here, the authors establish authors' claim by analysing field-collected data. Live LTE downlink (DL) signal is captured using modelled LTE UE using software defined radio (SDR). The detection analysis and classification performance show promising results and ascertain that LTE-CommSense is capable of detection and classification of different types of vehicles in outdoor road environment. 1 Introduction Coexistence of telecommunication and radar systems [1, 2] to detect and track targets is an active research domain. It gains importance because devices following this principle can be low footprint and passive in nature. There are many proposals of vehicle detection using active systems [3-5]. Main principle of these systems are to transmit a signal and analyse corresponding reflected signal from the vehicle. Passive Radar or commensal Radar [1, 2, 6, 7] enjoys benefit from each other without any detrimental effect on each other. The efforts of coexistence using commensal Radar principle have many real-world applications [7, 8]. Few ideas are proposed in open literature which utilises commensal radar principle for vehicle detection and classification [9, 10]. In [10], the incident spectrum was analysed using their custom RF receiver model. Later cluster analysis of the principal components of the spectrum was performed. One innovative way of utilising the telecommunication system is to collect information from the communication receiver modules which are responsible for reducing the channel imperfections. Channel estimation is performed to estimate the channel conditions by observing effect of channel on a known data pattern. These known data patterns are embedded within the payload at pre-defined locations. They are called reference symbols or pilots. After that, the channel equalisation block is present to nullify the effect of estimated channel imperfections. Therefore, the output of channel estimation and equalisation block contains channel information. The scattering, fading, power reduction with distance of the communication link are collectively known as channel state information (CSI). This CSI value is exploited by proposed CommSense scheme [11, 12]. In the past two years, a good amount of work has been done regarding the feasibility of a GSM (Global System for Mobile telecommunication)-based CommSense system [6, 7]. Suitability of LTE-based CommSense system is presented in [8, 11]. LTE networks are widespread and follows Orthogonal frequency division multiplexing (OFDM) Principle. Wideband nature of OFDM can overcome fading and multipath problems and have low interception probability as well [13]. Therefore, we can assume that those information gets reflected in the evaluated CSI. Using frequency diversity, more information about the environment change of interest may be obtained. Therefore, LTE is selected for CommSense-based vehicle detection and classification application. This proposed idea for vehicle detection and classification has the following novelty and advantages. Use of CSI extracted from LTE DL signal in Commensal radar setup for vehicle detection and classification is a novel effort. A part of the processing gets done by the LTE communication receiver itself. This reduces the custom computation efforts and additional processing modules and infrastructure. As the communication base station will work as the transmitter, there is no requirement of a dedicated transmitter to be a part of our proposed sensing instrument. Therefore, the system complexity to develop such instrument is less and it does not require to adhere to any transmission regulations. This ensures quick and easy installation for practical applications. Without any transmission by this instrument and low footprint makes the system difficult to detect. Therefore, the proposed system is suitable for covert defence operations. Reuse of outputs of communication receiver modules and no requirement of signal transmission makes the system low power and less costly. This makes the system suitable for battery-operated field applications. The following results are shown here: First, the feasibility of using LTE CommSense instrument for vehicle detection is investigated. LTE downlink (DL) signal was captured using CommSense system implemented using a Universal Software Radio Peripheral (USRP) SDR platform. CSI values are extracted from the captured DL data for the case of empty road environment and with vehicle case in five different outdoor open road environment. Principal component analysis (PCA) are performed on the CSI values and optimal number of principal components are selected by considering the energy contribution and cluster analysis. After that, threshold-based detection method was devised and vehicle detection performance was evaluated. In the next stage, classification between empty road and three different types of vehicle namely a two wheeler (Honda Acticva), a sedan car (Hyundai I20), and a sport utility vehicle i.e. SUV (Ford EcoSport Titanium) was exercised. To evaluate the classification performance, classification accuracy, confusion matrix, false acceptance rate (FAR), and false rejection rate (FRR) were evaluated. The paper is organised as follows. Section 2 describes the basic structure of LTE CommSense, system design for the application scenario of vehicle detection and classification. Section 3 provides the experiments performed, their objectives. Results and data analysis for vehicle detection and classification are detailed in Section 4. The conclusion of this work and future scopes of improvements are provided in Section 5. 2 LTE-CommSense system General block diagram and description of LTE-CommSense are elaborated in [8, 11]. Fig. 1 provides a simple conceptual system described in [8, 11] using single UE which is implemented and utilised in this work of vehicle detection and classification. Fig. 1Open in figure viewerPowerPoint LTE-CommSense instrumentation system block diagram using single UE for vehicle detection and classification There are two parts of processing in the system: one is offline and the other one is online. A brief description of individual blocks are as follows. Block 1: This block first detects and then captures LTE DL signal from the 'eNodeB' using the USRP N200 SDR platform. This SDR is configured as an LTE UE using OpenLTE framework. This block is used both in offline and online modes. Block 2: In the LTE UE receiver work-flow, the channel equalisation block extracts the channel information from the channel affected call-specific reference symbols (cellRS) in the form of CSI. The objective of this block is to tap in to the channel equalisation block of the UE to capture the extracted channel information. This block is also used both in offline and online modes. Block 3: It is used in online and offline modes. For vehicle detection and classification application using LTE-CommSense, we perform PCA of the extracted CSI. The eigen values are arranged in descending order and their energy contribution to select optimal number of principal components for our analysis. Block 4: We have to generate the database to train the LTE-CommSense using the generated principal components of CSI values. Data corresponding to multiple capture corresponding to five different road background and three different vehicles are used to generate the training database and maintained in the control unit. The control unit tunes LTE-CommSense for targeted application. Block 5: This is the LTE-CommSense detector and used in both modes of operation. It accepts principal components of a CSI corresponding to a DL signal capture of current channel condition. The objective of the detector is to give decision of regarding a vehicle is present or not. Block 6: This is the vehicle classifier using LTE-CommSense principle and is used in both the modes of operation. First, it works in offline mode and gets trained by the vehicle database provided by the control unit in 'Block 4'. After that in online mode, it accepts principal components of a CSI corresponding to a DL signal capture. The classifier provides us the classification accuracy. In the feasibility analysis presented here, we evaluate classification accuracy, confusion matrix, false acceptance rate (FAR), and false rejection rate (FRR) to carry out the performance analysis of the proposed classifier and decide its classification capability. The naive nearest neighbour classifier is selected in this feasibility analysis. Previous works on CommSense have shown promising results using both GSM and LTE telecommunication standards ([6-8, 11]). Our proposed hypothesis in this work is that, using extracted CSI values of LTE downlink signal, vehicle can be detected and multiple vehicle can be classified. CSI can be termed as known channel properties of a communication link. CSI provides information regarding how communication downlink signal traverses from the base station (eNodeB in case of LTE). It represents the combined effect of the channel imperfections i.e. decay of signal power with respect to distance, channel fading, and scattering. In communication, CSI helps to adjust the transmission to the present condition of the channel. It is estimated in the receiver (UE in case of LTE) and sent back to transmitter. In LTE communication, cell-specific reference symbols (CellRS) are used to evaluate the CSI values. Mathematically, let the reference symbols be and CSI be H. Corresponding received signals at the UE can be represented as: (1) Representing in vector form for all the training signal, we obtain, (2) Here, H is the statistical channel model of the channel in between communication transmitter and the receiver and N is the model of noise in the channel. H is modelled using circular symmetric complex Normal distribution i.e. ; where is the standard deviation of the normal distribution. Corresponding least square (LS) estimate of the CSI is given as: (3) If channel distributions and noise distributions are known a prior, then estimation error will be reduced. For minimum mean square estimation (MMSE) in Rayleigh fading channel, if we know that and , where R is the channel covariance matrix, then, (4) Here, denotes the Kronecker product [14]. In [11], we have established that, MMSE estimator is more suitable candidate for CommSense-based applications. Accordingly, it is selected here. In case of LTE communication, CSI values gets continuously evaluated using embedded CellRS symbols in the LTE sub-frames. As these CSI values contains the channel information in between eNodeB and UE, we propose to use this to detect and classify vehicles on road. To perform this, we devise the following experimental setup. 3 Experimental setup The objective of this experiment is two-fold. First we will evaluate the feasibility of detecting a vehicle in outdoor environment. If detected, we try to see the distinguishing capability of our proposed system. For that, we consider three different types of vehicles, namely, a two wheeler (Honda Acticva), a sedan car (Hyundai I20), and a sport utility vehicle i.e. SUV (Ford EcoSport Titanium) as shown in Fig. 2. A brief structural description of the considered vehicles is provided in Table 1. Table 1. Structural descriptions of the vehicles considered for evaluation of vehicle classification performance of LTE-CommSense Vehicle name Dimension (Length×Width×Height) Weight, kg Honda Acticva 1761×710×1158 (mm3) 109 Hyundai I20 3985×1734×1505 (mm3) 1066 Ford EcoSport Titanium 3998×1765×1708 (mm3) 1320 Fig. 2Open in figure viewerPowerPoint Different vehicles used in the experiment (a) 2 Wheeler: Honda Activa, (b) Sedan Car: Hyundai I20 (c) SUV car: Ford EcoSport Titanium To obtain live LTE downlink signal, SDR-based data capturing system was developed. USRP N200 SDR platform [15] and RFX2400 RF daughter card [16] along with VERT2450 antenna [17] was used to work at LTE passband operating frequency range. A GNURadio-based open-source model of the 3GPP LTE receiver on OpenLTE [18] was utilised to control the SDR platform. The capturing SDR device N200 along with OpenLTE implemented on it and interfaced host computer are shown in Fig. 3a. The internal block diagram of USRP SDR platform is shown in Fig. 3b. The SDR unit was connected to the host computer via Ethernet. The OpenLTE framework is implemented on the host computer. The commands for searching for live LTE DL signal, capturing detected LTE DL data on the host computer for a specified time duration were also input to the SDR platform via host PC using the OpenLTE framework. The LTE UE receiver works at Band–40 (2300–2400 MHz frequency band) in time division duplexing (TDD) topology. The experiments performed for this feasibility analysis exploit a 15 MHz bandwidth (BW) of LTE downlink signal. Fig. 3c shows the SDR platform relative placements of the vehicle for this experiment. The vehicle is situated on a road in outdoor environment. In this figure, the commercial LTE base station, eNodeB is located 1.35 km away from our object of interest i.e. vehicle/car. The user equipment (UE) modelled using the SDR platform is situated at a distance of 2 m meter from the car. This distance is considered for the situation where the road is 4 m wide, the vehicle/car is at the middle of the road and our sensing instrument is stationed at the side of the road. Fig. 3Open in figure viewerPowerPoint Experimental Setup and data collection scenario (a) USRP N200 SDR platform (front) working as an LTE UE to capture live LTE signal and host PC screen showing detected LTE channel using OpenLTE, (b) USRP N200 internal block diagram showing individual components, (c) Position of receiver (LTE UE), vehicle and transmitter (eNodeB) in outdoor environment For each of the vehicle, LTE downlink signal is captured for five different outdoor road environment. For each capture, 1000 CSI values were extracted from the LTE receiver channel equalisation block. 4 Results and analysis Vehicle detection is exercised first. After detection, classification is performed to evaluate the distinguishing capability of proposed scheme between different types of vehicles. 4.1 Vehicle detection CSI values evaluated for five different background situations. For each background, CSIs are evaluated for only background and with vehicle case. The vehicle considered in this case is sedan car (Hyundai I20). For each of the cases explained above, 1000 CSI values were evaluated from the captured DL data. 4.1.1 CSI analysis Fig. 4 shows CSI for with and without vehicle case and their differences for each of the five different backgrounds. We can observe a clear and consistent CSI difference for each case. Fig. 4Open in figure viewerPowerPoint Evaluated CSI information for with and without vehicle case in five different outdoor environment 4.1.2 PCA analysis Fig. 5a shows the eigenvalues arranged in decreasing order and their energy contribution. It can be observed that, less first three of eigenvalues contribute to 99.9% cumulative energy. As the first eigenvalue contributes to 99.79% of the total cumulative energy, it can be stated that the largest component have a useful discrimination capability. Fig. 5Open in figure viewerPowerPoint Dimension reduction using Principal Component Analysis (PCA) and its visualisation (a) Highest 10 eigenvalues and their corresponding energy contribution, (b) Scatterplot for highest two principal components, (c) Scatterplot for highest three principal components Figs. 5b and c show the two-dimensional (2D) and three-dimensional (3D) scatterplot corresponding to highest two and highest three principal components, respectively. In case of 2D scatterplot, X-axis is the direction of the principal eigen-vector corresponding to largest eigenvalue and Y-axis is the direction of the eigen-vector corresponding to second largest eigenvalue. Additionally in case of 3D scatterplot, Z-axis is direction of the eigen-vector corresponding to third largest eigenvalue. Projection of the plotted points to the respective axis provides corresponding principal components. The largest principal component corresponds to the largest eigenvalue. Therefore, the largest principal component has maximum discriminating power. This is more evident from Fig. 5b. It can be observed that, the projection of the points onto the X-axis can be separated with minor overlaps using a single selected threshold. In the figure, the blue points corresponds to the case of data captures in presence of vehicle on the road and black points are for the case of without vehicle. It can be easily observed that, two distinct clusters with minor overlap between them are visible in the 2D and 3D scatterplots. 4.1.3 Threshold selection and detection performance Fig. 6 shows the histogram of mean of considered number of principal components for with (blue) and without vehicle (orange) case. Each subfigure is for a specific number of PCs considered and mentioned in figure. It can be observed that mean values for both the cases forms separate regions but overlap is there. Fig. 6Open in figure viewerPowerPoint Histogram of the mean values of principal components for with and without vehicle case considering different number(s) of principal components Fig. 7 shows the selection of threshold and corresponding error percentages for a number of principal components considered. This is performed for different number(s) of principal components, shown in each subfigure. We have considered a simple detection rule: if mean of considered principal components is less than the threshold then vehicle is present; else vehicle is absent. It is observed that for very less number of principal components, minimum error percentage is more. This simple threshold-based method can achieve as low as detection error as shown in Fig. 7. Fig. 7Open in figure viewerPowerPoint Threshold selection: error performance with respect to threshold values for different number(s) of principal components Following can be concluded from this vehicle detection feasibility analysis. The CSI plots are consistent for with vehicle and without vehicle cases as shown in Fig. 4. CSI pattern for with vehicle case are different than without vehicle case. Their differences are also consistent as shown in the third column of Fig. 4. The scatterplots in 2D and 3D show distinct clusters for with and without vehicle cases. We observe that, using this simple detection rule, we are able to achieve >90% detection accuracy. This proves that vehicle detection is feasible using LTE-CommSense infrastructure. 4.2 Vehicle classification After we have established that, LTE-CommSense can be successfully used for detection of vehicle on road, next we investigate whether it can differentiate between different types of vehicle or not. The nearest neighbour classifier is used here for classification. The nearest neighbour classifier is the simplest classifier. Every test data are compared with every labelled trained data to predict its label or class. For this comparison, norm-based distance measure is selected. To calculate norm, we compare two data by adding all the absolute difference of corresponding data points. In future, this can be replaced with better classifier, if needed. 4.2.1 PCA analysis Fig. 8a shows the eigenvalues arranged in decreasing order and their corresponding energy contribution. We can see that, less first three of eigenvalues contribute to 99.9% cumulative energy as was already seen during detection process also. Fig. 8Open in figure viewerPowerPoint PCA analysis for vehicle classification (a) Highest ten eigenvalues and their corresponding energy contribution, (b) Scatterplot for highest two principal components for three different vehicles and outdoor background (Black: outdoor background, Red: Bike, Blue: Sedan, Green: SUV), (c) Scatterplot for highest three principal components for three different vehicles and outdoor background (Black: Outdoor background, Red: Bike, Blue: Sedan, Green: SUV) Figs. 8b and c shows the 2D and 3D scatterplots corresponding to highest two and highest three principal components, respectively, for three vehicles and the background. Here, the black points correspond to the case of data captures for the road and backgrounds only, red points are for Honda Activa, blue points corresponds to Hyundai I20 vehicle, and green points are for the case of Ford EcoSport Titanium. In 2D and 3D scatterplots, it can be observed that four distinct clusters are visible. Also, the clusters corresponding to road background and Honda Activa are nearer to each other and so is the clusters of Hyundai I20 and Ford EcoSport Titanium. This means that distinguishing between road background and Honda Activa is more difficult compared to distinguishing between road background and Hyundai I20 or Ford EcoSport Titanium. Similarly, distinguishing between Hyundai I20 and Ford EcoSport Titanium is more difficult compared to distinguishing between Ford EcoSport Titanium and road background or Honda Activa. In the next stage, we will numerically analyse this distinguishing ability property in terms of classification performance, confusion matrix, FAR, and FRR. 4.2.2 Classification performance The accuracy of a classifier ([19, 20]) denotes the extent with which the system provides correct classification output i.e. the system's ability to distinguish between occurrence of different types of changes in the environment. FRR or false negative rate (Type I Error) and FAR or false positive rate (Type II Error) [21] are another two performance parameters for classification. FAR is fraction of the falsely accepted patterns divided by the number of all impostor patterns. The fraction of the number of rejected client patterns divided by the total number of client patterns is called FRR. The frequency at which false acceptance errors are made can be denoted as FAR. If is the number of false acceptance cases and is the total number of impostor attempts, then FAR can be defined as: (5) Similarly, false rejection is the act of deciding that a category is an impostor while the category is actually genuine. The frequency at which false rejections are made is called FRR. If is the total number of failed rejections and is the total number of legitimate classification attempts, then FRR can be given as: (6) Classification accuracy, confusion matrix, FAR, and FRR are evaluated in this section to evaluate the classification performance between three different types of vehicle and empty road. We have selected simple nearest neighbour classifier for this analysis. If this analysis provides acceptable results, we may use better classifier in future to improve the performance further. Table 2 shows the accuracy, confusion matrix, FRR, and FAR for different of training and testing images. The following can be observed form this table. We have achieved an average of 92.6% classification accuracy. Table 2. Vehicle classification performance using LTE-CommSense Training images Testing images Accuracy, % Confusion matrix FRR FAR 200 250 94.7 232 18 0 0 0.01 0.07 11 238 0 1 0 0 240 10 0 0 13 237 500 500 99.5 499 1 0 0 0 0.002 0 500 0 0 0 0 500 0 0 0 0 500 500 750 91.37 667 82 1 0 0.028 0.1 42 704 3 1 0 0 700 50 0 18 80 670 500 1000 88.65 828 166 4 2 0.05 0.17 85 899 8 8 0 0 888 112 0 0 65 935 750 1000 93.43 919 77 1 3 0.02 0.08 48 944 4 4 0 0 939 61 0 0 65 935 1000 1500 88.95 1451 49 0 0 0.048 0.03 144 1351 1 4 0 0 1266 234 0 0 231 1269 1500 200 93.15 1934 66 0 0 0.008 0.033 38 1942 12 8 0 0 1802 198 0 0 226 1774 2000 2500 89.2 2033 438 13 16 0.009 0.1 54 2387 17 42 0 0 2042 458 0 0 42 2458 2500 3000 96.2 2849 138 10 3 0.005 0.05 51 2921 9 19 0 0 2900 100 0 0 129 2871 3000 3500 93.05 3395 103 2 0 0.04 0.03 420 3073 6 1 0 0 3316 384 0 0 57 3443 3500 4000 96.59 3902 99 2 0 0.01 0.02 187 3809 4 0 0 0 3853 147 0 0 108 3892 4000 4500 93.1 4013 280 73 134 0.004 0.1 56 4265 7 172 0 0 4432 68 0 0 452 4048 4000 5000 87.6 4031 595 130 244 0.01 0.32 97 4512 28 363 0 0 4861 139 0 0 887 4113 From the evaluated confusion matrix in the table, we can observe that, confusion between category 1 (empty road with background) and category 2 (two wheeler Honda Activa) are more. This is because the size of the two wheeler is much smaller compared to the four wheelers (Table 1). Similarly, confusion between category 3 (Sedan car Hyundai I20) and category 4 (SUV car Ford EcoSport Titanium) are more. This is due to the structural similarity between them compared to empty road or two wheeler as is evident from Table 1. With less number of training images, nearest neighbour classifier provides 92.6% classification accuracy. This may further improve by using more efficient classifier. 5 Conclusion This work is a feasibility analysis of using LTE-CommSense for vehicle detection and subsequent classification using practical data captured with SDR platform. We observe that the CSI values corresponding to with vehicle and without vehicle cases are consistently different from each other. Scatterplot in 2D and 3D shows distinct clusters formation for with and without vehicle. Histogram of mean values of principal components form distinct regions with minor overlap. Threshold-based detection method was exercised for different numbers of principal components. Results shows that using this simple technique, 90% detection accuracy can be achieved. These results show that vehicle detection is feasible using LTE-CommSense with less number of principal components. In the subsequent classification, we have achieved 92.6% classification accuracy even with less number of training images. From the confusion matrix, we can conclude that, confusions between category 1 (empty road with background) and category 2 (two wheeler Honda Activa) are more because the size of the two wheeler is much smaller compared to other vehicles considered. Likewise, confusions between category 3 (Sedan car Hyundai I20) and category 4 (SUV car Ford EcoSport Titanium) are also more because of structural similarity between them. This successful demonstration of vehicle detection and classification using LTE-CommSense principle with practical data promises its usability in vehicle classification, traffic condition analysis, and maintenance of traffic rules. In future, absolute magnitude, squared magnitude, and log magnitude will be exercised in place of the mean and the detection performance will be compared. The nearest neighbour classifier will be replaced with more efficient classifiers to achieve better classification performance. Use of Doppler information to achieve higher vehicle detection and classification performance will be investigated. 6 Acknowledgments This research is partly funded by Visvesvarya fellowship and support. 7 References 1Griffiths, H., Baker, C.: 'Passive coherent location radar systems. 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