Open Access Open Access  Restricted Access Subscription or Fee Access

Automatic Target Recognition Based on the Features of UWB Radar Signals


(*) Corresponding author


Authors' affiliations


DOI: https://doi.org/10.15866/irea.v9i6.19590

Abstract


Accident prevention systems development has become one of the critical worries of car manufacturers and researchers in order to improve road safety. This need for security keeps pushing the development of electronic systems, allowing communication, localization, or detection obstacles on the roads. The objective is to design and implement a low-cost obstacle detection algorithm on an onboard system in order to prevent collisions. The UWB radar is the best candidate for this functionality because of its immunity to various climatic and environmental conditions. Classification of obstacles by a vehicle's short-range UWB radar system is a difficult task. Road traffic involves a variable number of different objects such as trees, pedestrians, cyclists, and vehicles, which complicates the signal processing phase and requires a complex algorithm for recognizing these obstacles. This work aims to develop an algorithm allowing the classification and the identification of road obstacles by a UWB radar system using the signals captured at the reception, whatever the atmospheric conditions are. This paper presents a new algorithm for identifying and classifying road obstacles in real-time and at a low cost. This algorithm exploits the characteristics of the pulses received and the correlation properties, which consist of correlating the signal received after its passage through the V2V channel with the database signatures. The performance of the proposed algorithm, in terms of accuracy and reliability, is confirmed through simulations.
Copyright © 2021 Praise Worthy Prize - All rights reserved.

Keywords


Classification; UWB Radar; Obstacles; Identification; Algorithm; Correlation

Full Text:

PDF


References


J. Orlovska et al., Effects of the driving context on the usage of Automated Driver Assistance Systems (ADAS) -Naturalistic Driving Study for ADAS evaluation, Transportation Research Interdisciplinary Perspectives, Vol. 4, March 2020, 100093.
https://doi.org/10.1016/j.trip.2020.100093

M. H.Tawfeek, K. El-Basyouny, A context identification layer to the reasoning subsystem of context-aware driver assistance systems based on proximity to intersections, Transportation Research Part C: Emerging Technologies, Volume 117, August 2020, 102703.
https://doi.org/10.1016/j.trc.2020.102703

J. Yin, B. Chen, K. R. Lai and Y. Li, Automatic Dangerous Driving Intensity Analysis for Advanced Driver Assistance Systems From Multimodal Driving Signals, in IEEE Sensors Journal, vol. 18, no. 12, pp. 4785-4794, 15 June15, 2018.
https://doi.org/10.1109/JSEN.2017.2765315

L. Kang and H. Shen, Attack Detection and Mitigation for Sensor and CAN Bus Attacks in Vehicle Anti-lock Braking Systems, 2020 29th International Conference on Computer Communications and Networks (ICCCN), 2020, pp. 1-9.
https://doi.org/10.1109/ICCCN49398.2020.9209645

Duanfeng Chu, Zhenglei Li, Junmin Wang, Chaozhong Wu, Zhaozheng Hu, Rollover speed prediction on curves for heavy vehicles using mobile smartphone, Measurement, Elsevier, Volume 130, 404-,411, 2018.
https://doi.org/10.1016/j.measurement.2018.07.054

Silalahi, Lukman M., et al. Design of Tire Pressure Monitoring System Using a Pressure Sensor Base. SINERGI, vol. 23, no. 1, 2019, pp. 70-78.
https://doi.org/10.22441/sinergi.2019.1.010

Amira Mimouna, Anouar Ben Khalifa, Ihsen Alouani, Abdelmalik Taleb-Ahmed, Atika Rivenq Menhaj, et al.. LSTM-based system for multiple obstacle detection using ultra-wide band radar. 13th International Conference on Agents and Artificial Intelligence, ICAART 2021, Feb 2021, Online Streaming, Austria. pp.418-425.
https://doi.org/10.5220/0010386904180425

Zhibiao Jiang, Jian Wang, Qian Song, and Zhimin Zhou Off-road obstacle sensing using synthetic aperture radar interferometry, Journal of Applied Remote Sensing 11(1), 016010 (12 January 2017).
https://doi.org/10.1117/1.JRS.11.016010

Min Zhu, Huiyan Chen, Guangming Xiong, A model predictive speed tracking control approach for autonomous ground vehicles Mechanical System and signal Processing, V87, Part B, 2017
https://doi.org/10.1016/j.ymssp.2016.03.003

M. S. Darms, P. E. Rybski, C. Baker and C. Urmson, Obstacle Detection and Tracking for the Urban Challenge, in IEEE Transactions on Intelligent Transportation Systems, vol. 10, no. 3, pp. 475-485, Sept. 2009.
https://doi.org/10.1109/TITS.2009.2018319

A. Paranjothi, M. S. Khan and M. Atiquzzaman, Hybrid-Vehcloud: An Obstacle Shadowing Approach for VANETs in Urban Environment, 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall), Chicago, IL, USA, 2018, pp. 1-5.
https://doi.org/10.1109/VTCFall.2018.8690729

A. Milella, G. Reina, J. Underwood and B. Douillard, Combining radar and vision for self-supervised ground segmentation in outdoor environments, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco, CA, 2011, pp. 255-260.
https://doi.org/10.1109/IROS.2011.6094548

M. Bertozzi, L. Bombini, P. Cerri, P. Medici, P. C. Antonello and M. Miglietta, Obstacle detection and classification fusing radar and vision, 2008 IEEE Intelligent Vehicles Symposium, 2008, pp. 608-613.
https://doi.org/10.1109/IVS.2008.4621304

D. Göhring, M. Wang, M. Schnürmacher and T. Ganjineh, Radar/Lidar sensor fusion for car-following on highways, The 5th International Conference on Automation, Robotics and Applications, Wellington, 2011, pp. 407-412.
https://doi.org/10.1109/ICARA.2011.6144918

W. Zhang et al., The intelligent vehicle target recognition algorithm based on target infrared features combined with lidar, Computer Communications, Volume 155, 1 April 2020, Pages 158-165.
https://doi.org/10.1016/j.comcom.2020.03.013

Gerardo-Castro M.P., Peynot T., Ramos F. (2015) Laser-Radar Data Fusion with Gaussian Process Implicit Surfaces, In: Mejias L., Corke P., Roberts J. (eds) Field and Service Robotics. Springer Tracts in Advanced Robotics, vol 105. Springer, Cham
https://doi.org/10.1007/978-3-319-07488-7_20

R.Hinkel, T.Knieriemen, Robot Control 1988 (Syroco' 88) Selected Papers from the 2nd IFAC Symposium, Karlsruhe, FRG, 5-7 October 1988 IFAC Symposia Series 1989, Pages 271-277.
https://doi.org/10.1016/B978-0-08-035742-3.50050-2

M. Abdellaoui, M. Fattah, Characterization of Ultra Wide Band indoor propagation, 2019 7th Mediterranean Congress of Telecommunications (CMT), Fès, Morocco, 2019, pp. 1-4.

L. Donghong, Z. Yongshun, C. Zhijie, C. Junbin, UWB radar target recognition based on time-domain bispectrum, Journal of Systems Engineering and Electronics, Volume 17, Issue 2, June 2006, Pages 274-278.
https://doi.org/10.1016/S1004-4132(06)60047-9

A. Kahana, E. Turkel, S. Dekel, D. Givoli, Obstacle segmentation based on the wave equation and deep learning, Journal of Computational Physics Volume 413, 15 July 2020, 109458.
https://doi.org/10.1016/j.jcp.2020.109458

Minh Thuy LE. Contribution to the Design of a Vehicle Identification and Classification System by Electromagnetic Waves, 2013.

Yang Liu. Radar Cross Section Near-Field to Far-Field Prediction for Isotropic-Point Scattering Target Based on Regression Estimation, Sensors 2020, 20, 6023.
https://doi.org/10.3390/s20216023

Rahmad Sadli, Charles Tatkeu, Khadija Hamidoun, Yassin El Hillali, Atika Rivenq, UWB radar recognition system based on HOS and SVMs. IET Radar Sonar and Navigation, Institution of Engineering and Technology, 2018, 12 (10), pp.1137-1145.
https://doi.org/10.1049/iet-rsn.2018.5065

Longbiao Chen, RADAR: Road Obstacle Identification for Disaster Response Leveraging Cross-Domain Urban Data, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Vol. 1, No. 4, 2018.
https://doi.org/10.1145/3161159

H. Jha, V. Lodhi and D. Chakravarty, Object Detection and Identification Using Vision and Radar Data Fusion System for Ground-Based Navigation, 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN), 2019, pp. 590-593.
https://doi.org/10.1109/SPIN.2019.8711717

Chang, Shuo; Zhang, Yifan; Zhang, Fan; Zhao, Xiaotong; Huang, Sai; Feng, Zhiyong; Wei, Zhiqing. 2020. Spatial Attention Fusion for Obstacle Detection Using MmWave Radar and Vision Sensor, Sensors, 20, no. 4: 956.
https://doi.org/10.3390/s20040956

Wang, Zhangjing; Miao, Xianhan; Huang, Zhen; Luo, Haoran. Research of Target Detection and Classification Techniques Using Millimeter-Wave Radar and Vision Sensors, Remote Sens. 13, no. 6: 1064. 2021.
https://doi.org/10.3390/rs13061064

Zhang, X., Zhou, M., Qiu, P., Huang, Y. and Li, J. (2019), Radar and vision fusion for the real-time obstacle detection and identification, Industrial Robot, Vol. 46 No. 3, pp. 391-395.
https://doi.org/10.1108/IR-06-2018-0113

Giovanni Manfredi, Efficient Simulation Tool to Characterize the Radar Cross Section of a Pedestrian in Near Field, Progress In Electromagnetics Research C, Vol. 100, 145-159, 2020.
https://doi.org/10.2528/PIERC19112701

D. Daghouj, M. Fattah, S. Mazer, Y. Balboul, M. El Bekkali, UWB Coherent Receiver Performance in a Vehicular Channel, International Journal of Advanced Trends in Computer Science and Engineering, Volume 9 No.2, March - April 2020.

Konkyana, V., Sudhakar, A., Design and Analysis of Dual Notch Band Ultra-Wide Band Antenna Using Complementary Split Ring Resonator for Wireless Applications, (2019) International Journal on Engineering Applications (IREA), 7 (3), pp. 72-80.
https://doi.org/10.15866/irea.v7i3.16906

Lee, M., Hur, S., & Park, Y. (2015). An Obstacle Classification Method Using Multi-feature Comparison Based on 2D LIDAR Database. 2015 12th International Conference on Information Technology - New Generations.
https://doi.org/10.1109/ITNG.2015.114

D. Daghouj, S. Mazer, and all, Modeling Of An Obstacle Detection Chain In A Vehicular Environment, 2019 7th Mediterranean Congress of Telecommunications (CMT), Fès, Morocco, 2019, pp. 1-4.
https://doi.org/10.1109/CMT.2019.8931380

Daghouj, D., Fattah, M., Mazer, S., Balboul, Y., El Bekkali, M., UWB Waveform for Automotive Short Range Radar, (2020) International Journal on Engineering Applications (IREA), 8 (4), pp. 158-164.
https://doi.org/10.15866/irea.v8i4.18997

L. Sakkila, et al., Methods of target recognition for UWB radar, 2010 IEEE Intelligent Vehicles Symposium, San Diego, CA, 2010, pp. 949-954.
https://doi.org/10.1109/IVS.2010.5547962

Marrugo Cardenas, N., Amaya Hurtado, D., Ramos Sandoval, O., Comparison of Multi-Class Methods of Features Extraction and Classification to Recognize EEGs Related with the Imagination of Two Vowels, (2018) International Journal on Communications Antenna and Propagation (IRECAP), 8 (5), pp. 398-405.
https://doi.org/10.15866/irecap.v8i5.12709

Hendel, M., Benyettou, A., Hendel, F., Fusion of Direct Probabilistic Multi-Class Support Vector Machines to Enhance Mental Tasks Recognition Performance in BCI Systems, (2018) International Journal on Communications Antenna and Propagation (IRECAP), 8 (5), pp. 430-438.
https://doi.org/10.15866/irecap.v8i5.14068

El Badlaoui, O., Maazouzi, A., Hammouch, A., Cepstral Features Extraction for Heart Sounds Classification, (2018) International Review of Electrical Engineering (IREE), 13 (5), pp. 421-427.
https://doi.org/10.15866/iree.v13i5.14965


Refbacks

  • There are currently no refbacks.



Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2023 Praise Worthy Prize