Zhurnal Radioelektroniki - Journal of Radio Electronics. eISSN 1684-1719. 2023. №8
ContentsFull text in Russian (pdf)
DOI: https://doi.org/10.30898/1684-1719.2023.8.3
Multi-Target Tracking Algorithm in Conditions of Multipath Radar Reflection
Le Ba Thanh
Moscow Institute of Physics and Technology (National Research University), 9 Institute Lane, Dolgoprudny, Moscow region, 141700 Russian Federation
The paper was received June 30, 2023
Abstract. This work presents a modified Poisson multi-Bernoulli mixture (PMBM) algorithm, adapted to perform trajectory processing tasks under conditions of multipath radar reflection. Based on the evaluative data of the previous kinematic state, size, and orientation of the target obtained using the extended PMBM algorithm, a pre-processing stage is carried out. During this stage, the origins of primary radar detections are initially classified. Then, after classification, these radar detections are further processed to estimate the state of targets at the next point in time. To assess the effectiveness of the proposed algorithm, a simulation modeling method based on MATLAB is used. With its help, scenarios for tracking distributed targets are modeled under conditions of the emergence of false signals from multipath radar reflections. The simulation results endorse the algorithm's capability to concurrently track multiple targets upon receiving primary radar detections from an assortment of radars amidst multipath radar reflections. The application of the algorithm finds use in the field of autonomous driving, driver assistance systems, applications ensuring driver safety, and other areas.
Keywords: extended target tracking; distributed goal; multipath radar scattering.
Corresponding author: Le Ba Thanh, thanhlb@phystech.edu)
References
1. Thanh L. B., Alexandrovich P. D., Ruben P. Multi-object multi-sensor tracking simulation using poisson multi-bernoulli mixture filter // 2021 23rd International Conference on Digital Signal Processing and its Applications (DSPA). – IEEE, 2021. – P. 1-6. https://doi.org/10.1109/DSPA51283.2021.9535849
2. Thanh L. B. Data Association for Multi-Object Tracking Using Assignment Algorithms // 2021 International Conference Engineering and Telecommunication (En&T). – IEEE, 2021. – P. 1-5. https://doi.org/10.1109/EnT50460.2021.9681742
3. Le Ba Thanh. Nonlinear filtering for the state of extended targets. Zhurnal radioelektroniki [Journal of Radio Electronics] [online]. 2023. №7. https://doi.org/10.30898/1684-1719.2023.7.8 (In Russian)
4. Le Ba Thanh. Algorithm for multiple extended target tracking. Zhurnal radioelektroniki [Journal of Radio Electronics] [online]. 2023. №7. https://doi.org/10.30898/1684-1719.2023.7.12 (In Russian)
5. Prophet R. et al. Instantaneous ghost detection identification in automotive scenarios // 2019 IEEE Radar Conference (RadarConf). – IEEE, 2019. – P. 1-6. https://doi.org/10.1109/RADAR.2019.8835603
6. Kraus F. et al. Using machine learning to detect ghost images in automotive radar // 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC). – IEEE, 2020. – P. 1-7. https://doi.org/10.1109/ITSC45102.2020.9294631
7. Ester M. et al. A density-based algorithm for discovering clusters in large spatial databases with noise // kdd. – 1996. – V. 96. – №. 34. – P. 226-231. https://cdn.aaai.org/KDD/1996/KDD96-037.pdf
8. Kellner D., Klappstein J., Dietmayer K. Grid-based DBSCAN for clustering extended objects in radar data // 2012 IEEE Intelligent Vehicles Symposium. – IEEE, 2012. – P. 365-370. https://doi.org/10.1109/IVS.2012.6232167
9. Zhang A. et al. Dive into deep learning // arXiv preprint arXiv:2106.11342. – 2021. https://doi.org/10.48550/arXiv.2106.11342
10. Martínez-Otzeta J. M. et al. Ransac for robotic applications: A survey // Sensors. – 2022. – V. 23. – №. 1. – P. 327. https://doi.org/10.3390/s23010327
11. Kuhn H. W. The Hungarian method for the assignment problem // Naval Research Logistics (NRL). – 2005. – V. 52. – №. 1. – P. 7-21. https://doi.org/10.1002/nav.20053
12. Bertsekas D. P. Auction Algorithms // Encyclopedia of optimization. – 2009. – V. 1. – P. 73-77. http://web.mit.edu/dimitrib/www/Auction_Encycl.pdf
13. Rahmathullah A. S., García-Fernández Á. F., Svensson L. Generalized optimal sub-pattern assignment metric // 2017 20th International Conference on Information Fusion (Fusion). – IEEE, 2017. – P. 1-8. https://doi.org/10.23919/ICIF.2017.8009645
14. Granstrom K., Orguner U. A PHD filter for tracking multiple extended targets using random matrices // IEEE Transactions on Signal Processing. – 2012. – V. 60. – №. 11. – P. 5657-5671. https://doi.org/10.1109/TSP.2012.2212888
15. Granström K. et al. Gamma Gaussian inverse Wishart probability hypothesis density for extended target tracking using X-band marine radar data // IEEE Transactions on Geoscience and Remote Sensing. – 2015. – V. 53. – №. 12. – P. 6617-6631. https://doi.org/10.1109/TGRS.2015.2444794
For citation:
Le Ba Thanh. Multi-target tracking algorithm in conditions of multipath radar reflection. Zhurnal radioelektroniki [Journal of Radio Electronics] [online]. 2023. №8. https://doi.org/10.30898/1684-1719.2023.8.3 (In Russian)