Improved Extended Kalman Filter Tracking Method Based On Active Waveguide Invariant Distribution

SUN Tongjing; ZHU Qingyu; WANG Zhizhuan

doi:10.11999/JEIT240595

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SUN Tongjing, ZHU Qingyu, WANG Zhizhuan. Improved Extended Kalman Filter Tracking Method Based On Active Waveguide Invariant Distribution[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240595

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SUN Tongjing, ZHU Qingyu, WANG Zhizhuan. Improved Extended Kalman Filter Tracking Method Based On Active Waveguide Invariant Distribution[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240595

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Improved Extended Kalman Filter Tracking Method Based On Active Waveguide Invariant Distribution

doi: 10.11999/JEIT240595

1.
Department of Automation, Hangzhou Dian zi University, Hangzhou 310018, China
2.
School of ship and ocean engineering, Shanghai Jiao Tong University, Shanghai 200240, China
3.
Underwater Test and Control Technology Key Laboratory, Dalian 116013, China

Funds: The Joint Fund of National Natural Science Foundation of China (U22A2044)

Received Date: 2024-07-12
Rev Recd Date: 2024-12-04

Available Online: 2024-12-07

Abstract

Abstract

In the complex Marine environment, the known information of the target is seriously disturbed by environmental noise and reverberation, which leads to poor target tracking effect, and it is difficult to extract the utilizable features of the target from these disturbances. This paper proposes an improved extended Kalman filter tracking method based on active waveguide invariant distribution by integrating the coupling characteristics of the target and environment into the target tracking algorithm. Firstly, based on the basic theory of target scattering in shallow sea waveguides, the mathematical model of invariant representation of active waveguide under the condition of receiving and receiving separation is derived, and the constraint relation of distance, frequency, and invariant distribution of active waveguide is obtained. Then this constraint is added to the state vector of the extended Kalman filter, and the fit degree between the model and the real trajectory of the target is improved by adding new constraints to enhance the precision of target tracking. Finally, the tracking performance of the proposed method is verified by simulation experiments and measured data. The results show that: compared with the conventional extended Kalman filter tracking method, the proposed method can improve the tracking accuracy of the target better. The optimization rate of the simulation results can reach about 50%, and the optimization rate of the measured data processing results is about 60%.
- Underwater target tracking,
- Extended Kalman filter,
- Shallow water waveguide,
- Target interference characteristic,
- The active waveguide invariant

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References(22)

References

[1]	郭戈, 王兴凯, 徐慧朴. 基于声呐图像的水下目标检测、识别与跟踪研究综述[J]. 控制与决策, 2018, 33(5): 906–922. doi: 10.13195/j.kzyjc.2017.1678. GUO Ge, WANG Xingkai, and XU Huipu. Review on underwater target detection, recognition and tracking based on sonar image[J]. Control and Decision, 2018, 33(5): 906–922. doi: 10.13195/j.kzyjc.2017.1678.
[2]	KALMAN R E and BUCY R S. New results in linear filtering and prediction theory[J]. Journal of Basic Engineering, 1961, 83(1): 95–108. doi: 10.1115/1.3658902.
[3]	KALMAN R E. A new approach to linear filtering and prediction problems[J]. Journal of Basic Engineering, 1960, 82(1): 35–45. doi: 10.1115/1.3662552.
[4]	LI Tiancheng, SU Jinya, LIU Wei, et al. Approximate Gaussian conjugacy: Parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(12): 1913–1939. doi: 10.1631/FITEE.1700379.
[5]	周云, 胡锦楠, 赵瑜, 等. 基于卡尔曼滤波改进压缩感知算法的车辆目标跟踪[J]. 湖南大学学报: 自然科学版, 2023, 50(1): 11–21. doi: 10.16339/j.cnki.hdxbzkb.2023002. ZHOU Yun, HU Jinnan, ZHAO Yu, et al. Vehicle target tracking based on Kalman filtering improved compressed sensing algorithm[J]. Journal of Hunan University: Natural Sciences, 2023, 50(1): 11–21. doi: 10.16339/j.cnki.hdxbzkb.2023002.
[6]	白冬杰. 车载毫米波雷达多目标跟踪算法研究[D]. [硕士论文], 北京交通大学, 2019. BAI Dongjie. Research on multi-target tracking algorithm of vehicle-mounted millimeter-wave radar[D]. [Master dissertation], Beijing Jiaotong University, 2019.
[7]	吴叶丽, 行鸿彦, 侯天浩, 等. 基于改进自适应扩展卡尔曼滤波的高精度姿态解算[J]. 探测与控制学报, 2023, 45(6): 69–76. WU Yeli, XiNG Hongyan, HOU Tianhao, et al. An improved adaptive extended Kalman filter for high precision attitude solution[J]. Journal of Detection & Control, 2023, 45(6): 69–76.
[8]	成春彦, 李亚安. EKF和UKF算法在双观测站纯方位目标跟踪中的应用[J]. 水下无人系统学报, 2023, 31(3): 388–397. doi: 10.11993/j.issn.2096-3920.202203014. CHENG Chunyan and LI Yaan. Applications of EKF and UKF algorithms in bearings-only target tracking with a double observation stations[J]. Journal of Unmanned Undersea Systems, 2023, 31(3): 388–397. doi: 10.11993/j.issn.2096-3920.202203014.
[9]	丁凯. 基于前视声纳的水下目标跟踪技术研究[D]. [硕士论文], 哈尔滨工程大学, 2006. doi: 10.7666/d.y936424. DING Kai. Research on tracking of underwater object based on forward-looking sonar[D]. [Master dissertation], Harbin Engineering University, 2006. doi: 10.7666/d.y936424.
[10]	CHUPROV S D. Interference structure of a sound field in a layered ocean[M]. BREKHOVSKIKH L M and ANDREEVOI L B. Ocean Acoustics: Current State. Moscow: Nauka, 1982: 71–91.
[11]	BROOKS L A, KIDNER M R F, ZANDER A C, et al. Techniques for extraction of the waveguide invariant from interference patterns in spectrograms[C]. Proceedings of ACOUSTICS 2006, Christchurch, New Zealand, 2006: 445.
[12]	SELL A W and LEE CULVER R. Waveguide invariant analysis for modeling time-frequency striations in a range-dependent environment[J]. The Journal of the Acoustical Society of America, 2011, 129(S4): 2509. doi: 10.1121/1.3588287.
[13]	TURGUT A, ORR M, and ROUSEFF D. Broadband source localization using horizontal-beam acoustic intensity striations[J]. The Journal of the Acoustical Society of America, 2010, 127(1): 73–83. doi: 10.1121/1.3257211.
[14]	李永飞, 郭瑞明, 赵航芳. 浅海内波环境下声场干涉条纹的稀疏重建[J]. 物理学报, 2023, 72(7): 074301. doi: 10.7498/aps.72.20221932. LI Yongfei, GUO Ruiming, and ZHAO Hangfang. Sparse reconstruction of acoustic interference fringes in shallow water and internal wave environment[J]. Acta Physica Sinica, 2023, 72(7): 074301. doi: 10.7498/aps.72.20221932.
[15]	余赟, 惠俊英, 殷敬伟, 等. 基于波导不变量的目标运动参数估计及被动测距[J]. 声学学报, 2011, 36(3): 258–264. doi: 10.15949/j.cnki.0371-0025.2011.03.015. YU Yun, HUI Junying, YIN Jingwei, et al. Moving target parameter estimation and passive ranging based on waveguide invariant theory[J]. Acta Acustica, 2011, 36(3): 258–264. doi: 10.15949/j.cnki.0371-0025.2011.03.015.
[16]	宋雪晶. 基于声场干涉结构的水声目标被动定位技术[D]. [博士论文], 哈尔滨工程大学, 2017. SONG Xuejing. Underwater acoustic target passive localization techniques based on acoustic field interference structure[D]. [Ph. D. dissertation], Harbin Engineering University, 2017.
[17]	QUIJANO J E, ZURK L M, and ROUSEFF D. Demonstration of the invariance principle for active sonar[J]. The Journal of the Acoustical Society of America, 2008, 123(3): 1329–1337. doi: 10.1121/1.2836763.
[18]	QUIJANO J E, CAMPBELL R L JR, OESTERLEIN T G, et al. Experimental observations of active invariance striations in a tank environment[J]. The Journal of the Acoustical Society of America, 2010, 128(2): 611–618. doi: 10.1121/1.3455813.
[19]	ZURK L M and ROUSEFF D. Striation-based beamforming for active sonar with a horizontal line array[J]. The Journal of the Acoustical Society of America, 2012, 132(4): EL264–EL270. doi: 10.1121/1.4748281.
[20]	HE Chensong, QUIJANO J E, and ZURK L M. Enhanced Kalman filter algorithm using the invariance principle[J]. IEEE Journal of Oceanic Engineering, 2009, 34(4): 575–585. doi: 10.1109/joe.2009.2028058.
[21]	芬恩·B·延森, 威廉·A·库珀曼, 米切尔·B·波特, 等, 周利生, 王鲁军, 杜栓平, 译. 计算海洋声学[M]. 2版. 北京: 国防工业出版社, 2018: 54–57, 267–269. JENSEN F B, KUPERMAN W A, POTER M B, et al, ZHOU Lisheng, WANG Lujun, and DU Shuanping. translation. Computational Ocean Acoustics[M]. 2nd ed. Beijing: National Defense Industry Press, 2018: 54–57, 267–269.
[22]	林萌, 李翠华, 黄剑航. 基于Radon变换的运动模糊图像参数估计[J]. 计算机技术与发展, 2008, 18(1): 33–36. doi: 10.3969/j.issn.1673-629X.2008.01.009. LIN Meng, LI Cuihua, and HUANG Jianhang. Parameters estimation of motion blurred images based on radon transform[J]. Computer Technology and Development, 2008, 18(1): 33–36. doi: 10.3969/j.issn.1673-629X.2008.01.009.