A Target Tracking Method Based on Box-particle Filter Under Measurement Uncertainty

WANG Ning; DUAN Rui; ZHOU Xiaoyi

doi:10.11999/JEIT231439

Volume 46 Issue 9

Sep. 2024

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(9): 3654-3661

WANG Ning, DUAN Rui, ZHOU Xiaoyi. A Target Tracking Method Based on Box-particle Filter Under Measurement Uncertainty[J]. Journal of Electronics & Information Technology, 2024, 46(9): 3654-3661. doi: 10.11999/JEIT231439

Citation:

WANG Ning, DUAN Rui, ZHOU Xiaoyi. A Target Tracking Method Based on Box-particle Filter Under Measurement Uncertainty[J]. Journal of Electronics & Information Technology, 2024, 46(9): 3654-3661. doi: 10.11999/JEIT231439

Citation:

PDF( 2990 KB)

A Target Tracking Method Based on Box-particle Filter Under Measurement Uncertainty

doi: 10.11999/JEIT231439

School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China

Funds: The National Natural Science Foundation of China (12074315, 62101549), The Fundamental Research Project of Taicang (TC2023JC18)

Received Date: 2024-01-02
Rev Recd Date: 2024-07-10

Available Online: 2024-08-02

Publish Date: 2024-09-26

Abstract

Abstract

There is significant uncertainty in the measurements of active sonar in terms of range-bearing resolution because of the complex underwater environment. In this case, the energy of the target echo may occupy multiple adjacent coordinate grids in the sonar range-bearing energy spectrum. Moreover, the measurement uncertainty mentioned above will cause multiple regional clutter interferences when there exists strong reverberation in the environment. To reduce the bias of state space estimation, Particle Filtering (PF) based tracking methods require a large number of particles to approximate the posterior probability density, resulting in a rapid decrease in real-time tracking performance. A Box-Particle Filtering tracking method based on Interval measurement (IBPF) is proposed to address the above problem. IBPF utilizes a box particle with range-bearing intervals instead of point measurements of active sonar, which greatly reduces the number of particles required for posterior probability density estimation while improving the stability of state estimation, and can further improve computational efficiency. The experiment result shows that the proposed IBPF achieves better tracking performance with higher computational efficiency, which reduces computation time by 18.06% and increases the number of successful tracking frames by 4.29%.
- Underwater target tracking,
- Active sonar,
- Box-particle filter,
- Measurement uncertainty

FullText(HTML)

References(18)

References

[1]	STONE L D, STREIT R L, CORWIN T L, et al. Bayesian Multiple Target Tracking[M]. 2nd ed. Boston: Artech House, 2014.
[2]	ORTON M and FITZGERALD W. A Bayesian approach to tracking multiple targets using sensor arrays and particle filters[J]. IEEE Transactions on Signal Processing, 2002, 50(2): 216–223. doi: 10.1109/78.978377.
[3]	XU Cong, HE Zishu, LIU Haicheng, et al. Bayesian track-before-detect algorithm for nonstationary sea clutter[J]. Journal of Systems Engineering and Electronics, 2021, 32(6): 1338–1344. doi: 10.23919/JSEE.2021.000113.
[4]	EDELSON G S. Two-stage active sonar network track-before-detect processing in a high clutter harbor environment[J]. The Journal of the Acoustical Society of America, 2016, 140(S4): 3349. doi: 10.1121/1.4970702.
[5]	DIAMANT R, KIPNIS D, BIGAL E, et al. An active acoustic track-before-detect approach for finding underwater mobile targets[J]. IEEE Journal of Selected Topics in Signal Processing, 2019, 13(1): 104–119. doi: 10.1109/JSTSP.2019.2899237.
[6]	HOU Xianghao, YANG Long, YANG Yixin, et al. Bearing-only underwater uncooperative target tracking for non-Gaussian environment using fast particle filter[J]. IET Radar, Sonar & Navigation, 2022, 16(3): 501–514. doi: 10.1049/rsn2.12198.
[7]	SALMOND D J and BIRCH H. A particle filter for track-before-detect[C]. The 2001 American Control Conference, Arlington, USA, 2001: 3755–3760. doi: 10.1109/ACC.2001.946220.
[8]	RISTIC B, VO B T, VO B N, et al. A tutorial on Bernoulli filters: Theory, implementation and applications[J]. IEEE Transactions on Signal Processing, 2013, 61(13): 3406–3430. doi: 10.1109/TSP.2013.2257765.
[9]	DUAN Rui, YANG Kunde, WU Feiyun, et al. Particle filter for multipath time delay tracking from correlation functions in deep water[J]. The Journal of the Acoustical Society of America, 2018, 144(1): 397–411. doi: 10.1121/1.5047671.
[10]	ZHANG Dianlun, GAO Linsen, SUN Dajun, et al. Soft-decision detection of weak tonals for passive sonar using track-before-detect method[J]. Applied Acoustics, 2022, 188: 108549. doi: 10.1016/j.apacoust.2021.108549.
[11]	MACAULAY J, KINGSTON A, CORAM A, et al. Passive acoustic tracking of the three-dimensional movements and acoustic behaviour of toothed whales in close proximity to static nets[J]. Methods in Ecology and Evolution, 2022, 13(6): 1250–1264. doi: 10.1111/2041-210X.13828.
[12]	JANG J, MEYER F, SNYDER E R, et al. Bayesian detection and tracking of odontocetes in 3-D from their echolocation clicks[J]. The Journal of the Acoustical Society of America, 2023, 153(5): 2690–2705. doi: 10.1121/10.0017888.
[13]	TIAN Mengchu, CHEN Zhimin, WANG Huifen, et al. An intelligent particle filter for infrared dim small target detection and tracking[J]. IEEE Transactions on Aerospace and Electronic Systems, 2022, 58(6): 5318–5333. doi: 10.1109/TAES.2022.3169447.
[14]	DAUM F and HUANG J. Curse of dimensionality and particle filters[C]. IEEE Aerospace Conference Proceedings, Big Sky, USA, 2003: 1979-1993. doi: 10.1109/AERO.2003.1235126.
[15]	JAULIN L, KIEFFER M, DIDRIT O, et al. Applied Interval Analysis[M]. London: Springer, 2001: 11-42. doi: 10.1007/978-1-4471-0249-6.
[16]	MAHLER R P S. Statistical Multisource-Multitarget Information Fusion[M]. Boston: Artech House, 2007: CH5.
[17]	ABDALLAH F, GNING A, and BONNIFAIT P. Box particle filtering for nonlinear state estimation using interval analysis[J]. Automatica, 2008, 44(3): 807–815. doi: 10.1016/j.automatica.2007.07.024.
[18]	GNING A, RISTIC B, and MIHAYLOVA L. Bernoulli particle/box-particle filters for detection and tracking in the presence of triple measurement uncertainty[J]. IEEE Transactions on Signal Processing, 2012, 60(5): 2138–2151. doi: 10.1109/TSP.2012.2184538.