Adaptive Sample-Size Unscented Particle Filter with Partitioned Sampling for Three-Dimensional High-Maneuvering Target Tracking
Source abstract
Abstract
High-maneuvering target tracking is a focused application area in radar positioning and military defense systems, especially in three-dimensional space. However, using a traditional motion model and techniques expanded from general two-dimensional maneuvering target tracking may be inaccurate and impractical in some mission-critical systems. This paper proposes an adaptive sample-size unscented particle filter with partitioned sampling (PS-AUPF), which is used to track a three-dimensional, high-maneuvering target, combined with the CS-jerk model. In PS-AUPF, the partitioned sampling is introduced to improve the resampling and predicting process by decomposing motion space. At the same time, the adaptive sample size strategy is used to adjust the sample size adaptively in the tracking process, according to the initial parameters and the estimated state variance of each time step. Finally, the effectiveness of this method is validated by simulations, in which the sample size of each algorithm is set to the minimum required for the optimal accuracy, thus ensuring the reliability of the tracking results. The results have shown that the proposed PS-AUPF, with higher accuracy and lower computational complexity, performs better than other existing tracking methods in three-dimensional high-maneuvering target tracking scenarios.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Target Tracking and Data Fusion in Sensor Networks · Radar Systems and Signal Processing · Underwater Acoustics Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
High Speed Circuits and Neural Networks Laboratory, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
University of Chinese Academy of Sciences, Beijing 100089, China
Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology, Beijing 100089, China
CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai 200031, China
Funding
National Natural Science Foundation of China · Chinese Academy of Sciences National Defense Innovation Foundation · Chinese Academy of Sciences Strategic Pilot Science and Technology Project
References (53)
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1. Distributed Multimodel Bernoulli Filters for Maneuvering Target Tracking10.1109/JSEN.2018.2843389 ↗
2. A Variational Bayesian Based Strong Tracking Interpolatory Cubature Kalman Filter for Maneuvering Target Tracking10.1109/ACCESS.2018.2869020 ↗
3. Parallel Filtering Algorithm in Three Dimensions for IMM Maneuvering Target Tracking
4. Bi, X., Wang, W., Gao, J., and Du, J. (2015, January 14–16). The improved IMM tracking algorithm for high-speed maneuvering target. Proceedings of the IET International Radar Conference 2015, Hangzhou, China.
5. Zhu, Z. (2010). Application of particle filter algorithm in maneuvering target tracking. Particle Filter and Its Application, Science Press. [1st ed.].10.1007/978-3-642-13067-0_56 ↗
6. Wang, G., Wu, J., Li, X., Mo, R., and Zhang, M. (2018, January 9–11). A real-time tracking prediction for maneuvering target. Proceedings of the 2018 Chinese Control and Decision Conference (CCDC), Shenyang, China.10.1109/CCDC.2018.8407740 ↗
7. Ma, C., San, Y., and Zhu, Y. (2013, January 26–28). Multiple Model Truncated Particle Filter for maneuvering target tracking. Proceedings of the 32nd Chinese Control Conference, Xi’an, China.
8. Wang, X., Xu, M., Wang, H., Wu, Y., and Shi, H. (2012). Combination of Interacting Multiple Models with the Particle Filter for Three-Dimensional Target Tracking in Underwater Wireless Sensor Networks. Math. Probl. Eng., 2012.10.1155/2012/829451 ↗
9. Multiple-Model Estimators for Tracking Sharply Maneuvering Ground Targets10.1109/TAES.2018.2793019 ↗
10. An Improved IMM Algorithm Based on STSRCKF for Maneuvering Target Tracking10.1109/ACCESS.2019.2912983 ↗
11. Survey of maneuvering target tracking. Part I. Dynamic models10.1109/TAES.2003.1261132 ↗
12. Highly maneuvering target tracking using multi-parameter fusion Singer model10.21629/JSEE.2017.05.03 ↗
13. Barbu, V.S., Karagrigoriou, A., and Makrides, A. (2016, January 15–18). On Semi-Markov Modelling and Inference for Multi-state Systems. Proceedings of the 2016 Second International Symposium on Stochastic Models in Reliability Engineering, Life Science and Operations Management (SMRLO), Beer Sheva, Israel.10.1109/SMRLO.2016.20 ↗
14. Bao, S., Cheng, S., and Xu, C. (2017, January 21–23). Strong Tracking Current Statistical Model Based on Mixed Bayesian-Fisher Model. Proceedings of the 2017 2nd International Conference on Cybernetics, Robotics and Control (CRC), Chengdu, China.10.1109/CRC.2017.11 ↗
15. Meng, Q., Hou, B., Dong, L., He, Z., and Wang, J. (2018). Performance Analysis and Comparison for High Maneuver Target Track Based on Different Jerk Models. J. Control Sci. Eng., 2018.10.1155/2018/6432485 ↗
16. A jerk model for tracking highly maneuvering targets10.1109/7.624345 ↗
17. A CS-Jerk Model for Tracking Highly Maneuvering Targets
18. A Highly Maneuvering Target Tracking Algorithm Based on Improved CS-Jerk Model
19. Particle filter algorithm
20. Two Stage Particle Filter for Nonlinear Bayesian Estimation10.1109/ACCESS.2018.2808922 ↗
21. Xie, W., Wang, L., Bai, B., Peng, B., and Feng, Z. (2019, January 20–24). An Improved Algorithm Based on Particle Filter for 3D UAV Target Tracking. Proceedings of the ICC 2019—2019 IEEE International Conference on Communications (ICC), Shanghai, China.10.1109/ICC.2019.8762028 ↗
22. Kazimierski, W., and Łubczonek, J. (2012, January 23–25). Verification of marine multiple model neural tracking filter for the needs of shore radar stations. Proceedings of the 2012 13th International Radar Symposium, Warsaw, Poland.10.1109/IRS.2012.6233384 ↗
23. Kazimierski, W., and Stateczny, A. (2012, January 23–25). Optimization of multiple model neural tracking filter for marine targets. Proceedings of the 2012 13th International Radar Symposium, Warsaw, Poland.10.1109/IRS.2012.6233382 ↗
24. Kazimierski, W., Zaniewicz, G., and Stateczny, A. (2012, January 23–25). Verification of multiple model neural tracking filter with ship’s radar. Proceedings of the 2012 13th International Radar Symposium, Warsaw, Poland.10.1109/IRS.2012.6233383 ↗
25. Sun, C., Zhu, S., and Liu, J. (2015, January 28–30). Fusing Kalman filter with TLD algorithm for target tracking. Proceedings of the 2015 34th Chinese Control Conference (CCC), Hangzhou, China.10.1109/ChiCC.2015.7260218 ↗
26. Merwe, R.V.D., Doucet, A., Freitas, N.D., and Wan, E. (2001, January 13). The Unscented Particle Filter. Proceedings of the 13th International Conference on Neural Information Processing Systems, Denver, CO, USA.
27. Adaptive sample-size unscented particle filter based on likelihood distribution
28. Yin, F., He, B., Feng, C., Guo, S., Zhang, X., Liu, H., Mu, X., Lv, P., and Yan, T. (2019, January 16–19). Application of AUV Navigation Based on SVD Unscented Particle Filter. Proceedings of the 2019 IEEE Underwater Technology (UT), Kaohsiung, Taiwan.10.1109/UT.2019.8734410 ↗
29. Robust information unscented particle filter based on M-estimate10.1049/iet-spr.2018.5151 ↗
30. Residual and stratified branching particle filters10.1016/j.csda.2017.02.003 ↗
31. Self-adaptive resampling particle filter based on student’s t distribution
32. Aunsri, N. (2018, January 25–28). Improved Dual Tone Multi Frequency identification Using Adaptive Resampling Particle Filter. Proceedings of the 2018 21st International Symposium on Wireless Personal Multimedia Communications (WPMC), Chiang Rai, Thailand.10.1109/WPMC.2018.8712915 ↗
33. Zhang, X., and Gu, F. (May, January 29). Adaptive Particle Sampling and Resampling in Parallel/Distributed Particle Filters. Proceedings of the 2019 Spring Simulation Conference (SpringSim), Tucson, AZ, USA.10.23919/SpringSim.2019.8732902 ↗
34. Application of deterministic resampling particle filter to fatigue prognosis10.21595/jve.2017.18537 ↗
35. Fei, X., Shen, Y., and Yan, T. (2018, January 22–25). Application of AUV Navigation Based on Deterministic Particle Filter Algorithm. Proceedings of the OCEANS 2018 MTS/IEEE Charleston, Charleston, SC, USA.10.1109/OCEANS.2018.8604529 ↗
36. A Novel Particle Filter Based on Hybrid Deterministic and Random Sampling10.1109/ACCESS.2018.2879470 ↗
37. Wang, W., Tan, Q.K., Chen, J., and Ren, Z. (2016, January 12–14). Particle filter based on improved genetic algorithm resampling. Proceedings of the 2016 IEEE Chinese Guidance, Navigation and Control Conference (CGNCC), Nanjing, China.10.1109/CGNCC.2016.7828809 ↗
38. Zhang, S. (2018, January 8–11). Mobile Robot Location Algorithm Based on Improved Particle Filtering. Proceedings of the 2018 IEEE 18th International Conference on Communication Technology (ICCT), Chongqing, China.10.1109/ICCT.2018.8600004 ↗
39. Novel target tracking method based on firefly algorithm optimized particle filter
40. Adaptive control bat algorithm intelligent optimization particle for maneuvering target tracking
41. Maccormick, J., and Isard, M. (July, January 26). Partitioned Sampling, Articulated Objects, and Interface-Quality Hand Tracking. Proceedings of the European Conference on Computer Vision, Dublin, Ireland.
42. Gonzales, C., Dubuisson, S., and Xuan, S.N.G. (2016, January 24–27). Simultaneous Partitioned Sampling for Articulated Object Tracking. Proceedings of the Advances Concepts for Intelligent Vision Systems-International Conference, Lecce, Italy.
43. KLD-Sampling: Adaptive Particle Filters
44. Dihua, S., Hao, Q., Min, Z., Senlin, C., and Liangyi, Y. (2018, January 9–11). Adaptive KLD sampling based Monte Carlo localization. Proceedings of the 2018 Chinese Control and Decision Conference (CCDC), Shenyang, China.10.1109/CCDC.2018.8407846 ↗
45. Li, A.W., and Bastos, G.S. (2017, January 10–12). A hybrid self-adaptive particle filter through KLD-sampling and SAMCL. Proceedings of the 2017 18th International Conference on Advanced Robotics (ICAR), Hong Kong, China.10.1109/ICAR.2017.8023503 ↗
46. Torma, P., and Szepesvari, C. (2005, January 15–17). On using likelihood-adjusted proposals in particle filtering: Local importance sampling. Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, Zagreb, Croatia.10.1109/ISPA.2005.195384 ↗
47. Liang-qun, L., Chao, H., and Guo-liang, Z. (2014, January 7–9). A novel particle filtering algorithm for the noncooperative target tracking in general aviation. Proceedings of the 2014 International Conference on Audio, Language and Image Processing, Shanghai, China.10.1109/ICALIP.2014.7009856 ↗
48. A survey of maneuvering target tracking—Part III: Measurement models
49. Applying the unscented Kalman filter for nonlinear state estimation10.1016/j.jprocont.2007.11.004 ↗
50. Crisan, E.D., and Rozovsky, B. (2011). A tutorial on particle filtering and smoothing: Fiteen years later. Proceedings of the Oxford Handbook of Nonlinear Filtering, Oxford University Press.
51. Sheng, Z., Xie, S., and Pan, C. (2008). Parameter Estimation. Probability Theory and Mathematical Statistics, Higher Education Press. [4th ed.].
52. Kalman Filter for Robot Vision: A Survey10.1109/TIE.2011.2162714 ↗
53. Sheng, Z., Xie, S., and Pan, C. (2008). Sample & Sampling distribution. Probability Theory and Mathematical Statistics, Higher Education Press. [4th ed.].