杂波环境下可移动主被动传感器长时调度方法

doi:10.12305/j.issn.1001-506X.2023.01.20

摘要/Abstract

摘要：

针对杂波环境下的多目标跟踪问题, 基于可移动主被动传感器系统, 提出了一种辐射控制的长时调度方法。首先, 建立调度模型, 对多目标运动状态和量测结果、传感器调度动作等进行数学描述; 同时, 基于雷达工作原理和截获概率的思想, 提出改进的辐射风险量化方法。随后, 利用高斯混合概率假设密度滤波算法预测长时跟踪精度, 利用所提改进的量化方法预测长时辐射代价, 并利用改进的灰狼优化算法求解传感器调度方案。最后, 执行调度方案获得多目标量测信息, 采用联合广义标签多伯努利滤波算法计算目标估计状态。仿真实验表明, 所提调度方法在保证跟踪精度的基础上, 能够实现对辐射代价的有效控制, 与其他方法相比具有明显的优势。

关键词: 传感器调度, 多目标跟踪, 随机有限集, 辐射控制, 灰狼优化算法

Abstract:

For multi-target tracking in clutter environment, a non-myopic scheduling method for radiation control is presented based on a mobile active/passive sensor system. Firstly, a scheduling model is established to describe the motion state and measurement results of multi-target, sensor scheduling actions and so on. At the same time, based on the principle of radar and the idea of interception probability, an improved radiation risk quantification method is proposed. Then, the Gaussian mixture probability hypothesis density filtering algorithm is used to predict the non-myopic tracking accuracy, the proposed improved quantization method is used to predict the non-myopic radiation cost, and the improved grey wolf optimization algorithm is used to solve the sensor scheduling scheme. Finally, the scheduling scheme is executed to obtain multi-target measurement information, and the joint generalized labeled multi-Bernoulli filtering algorithm is used to calculate the target estimation state. The simulation results indicate that the proposed scheduling method can effectively control the radiation cost while guaranteeing the tracking accuracy, and has obvious advantages over other methods.

Key words: sensor scheduling, multi-target tracking, random finite set, radiation control, grey wolf optimization algorithm

中图分类号:

TP391
TN95

安雷, 李召瑞, 吉兵. 杂波环境下可移动主被动传感器长时调度方法[J]. 系统工程与电子技术, 2023, 45(1): 165-174.

Lei AN, Zhaorui LI, Bing JI. Non-myopic scheduling method for mobile active/passive sensor in clutter environment[J]. Systems Engineering and Electronics, 2023, 45(1): 165-174.

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参考文献 31

1	闫涛, 韩崇昭, 张光华. 空中目标传感器管理方法综述[J]. 航空学报, 2018, 39 (10): 26- 36.
	YAN T , HAN C Z , ZHANG G H . An overview of sensor management approaches for aerial target[J]. Acta Aeronautica Sinica, 2018, 39 (10): 26- 36.
2	ROGALSHI A , MARTYNIUK P , KOPYTKO M , et al. Trends in performance limits of the hot infrared photodetectors[J]. Applied Sciences, 2021, 11 (2): 501. doi: 10.3390/app11020501
3	XU G G , SHAN G L , DUAN X X . Non-myopic scheduling method of mobile sensors for manoeuvring target tracking[J]. IET Radar, Sonar & Navigation, 2019, 13 (11): 1899- 1908.
4	VIGNESH R , SUNDARAM G A S , GANDHIRAJ R . Phase-modulated stepped frequency waveform design for low probability of detection radar signals[J]. Advances in Intelligent Systems and Computing, 2019, 910, 181- 195.
5	SHAN G L , XU G G , QIAO C L . A non-myopic scheduling method of radar sensors for maneuvering target tracking and radiation control[J]. Defence Technology, 2020, 16 (1): 242- 250. doi: 10.1016/j.dt.2019.10.001
6	HUAI T Y, LIU M Q, ZHANG S L, et al. Active sensor radiation control based on posterior Cramer-Rao lower bound with passive measurement[C]//Proc. of the IEEE Chinese Control Conference, 2019: 3564-3570.
7	BAO T T, ZHANG Z K, SABAHI M F. An improved radar and infrared sensor tracking fusion algorithm based on IMM-UKF[C]// Proc. of the IEEE 16th International Conference on Networking, Sensing and Control, 2019: 420-423.
8	PANG C , SHAN G L , MA W N , et al. Sensor radiation interception risk control in target tracking[J]. Defence Technology, 2019, 16 (3): 695- 704.
9	QIAO C L , SHAN G L , YAN L , et al. Nonmyopic sensor scheduling for target tracking with emission control[J]. International Journal of Adaptive Control and Signal Processing, 2019, 33 (5): 767- 783. doi: 10.1002/acs.2987
10	SMITH J, PARTICKE F, HILLER M, et al. Systematic analysis of the PMBM, PHD, JPDA and GNN multi-target tracking filters[C]//Proc. of the IEEE 22nd International Conference on Information Fusion, 2019.
11	ZHU Y , WANG J , LIANG S , et al. Covariance control joint integrated probabilistic data association filter for multi-target tracking[J]. IET Radar, Sonar & Navigation, 2019, 13 (4): 584- 592.
12	BREKKE E , CHITRE M . Relationship between finite set statistics and the multiple hypothesis tracker[J]. IEEE Trans.on Aerospace & Electronic Systems, 2018, 54 (4): 1902- 1917.
13	MAHLER R . Advances in statistical multisource-multitarget information fusion[M]. Norwood: Artech House, 2014.
14	ZHANG H Q , GE H W , YANG J L . Improved Gaussian mixture probability hypothesis density for tracking closely spaced targets[J]. International Journal of Electronics & Telecommunications, 2017, 63 (3): 247- 254.
15	DELANDE E , BRYANT D , GEHLY S , et al. The CPHD filter with target spawning[J]. IEEE Trans.on Signal Processing, 2017, 65 (5): 13124- 13138. doi: 10.1109/TSP.2016.2597126
16	VO B N , VO B T , HOANG H G . An efficient implementation of the generalized labeled Multi-Bernoulli filter[J]. IEEE Trans.on Signal Processing, 2017, 65 (8): 1975- 1987. doi: 10.1109/TSP.2016.2641392
17	GOSTR K A , HOSEINNEZHAD R , BAB H A , et al. Sensor-management for multitarget filters via minimization of posterior dispersion[J]. IEEE Trans.on Aerospace & Electronic Systems, 2017, 53 (6): 2877- 2884.
18	陈辉, 贺忠良, 连峰, 等. 基于高斯混合多目标滤波器的传感器控制策略[J]. 电子学报, 2019, 47 (3): 521- 530.
	CHEN H , HE Z L , LIAN F , et al. Sensor control strategy based on Gaussian mixture multi-target filter[J]. Acta Electronica Sinica, 2019, 47 (3): 521- 530.
19	JIANG X Y, YI W, YUAN Y, et al. Network cost based node selection strategy for multiple target tracking in netted radar system[C]//Proc. of the IEEE Radar Conference, 2019.
20	ANGLEY D , RISTIC B , SUVOROVA S , et al. Non-myopic sensor scheduling for multistatic sonobuoy fields[J]. IET Radar, Sonar & Navigation, 2017, 11 (12): 1770- 1775.
21	赖作镁, 乔文昇, 古博, 等. 任务性能约束下传感器协同辐射控制策略[J]. 系统工程与电子技术, 2019, 41 (8): 1749- 1754.
	LAI Z M , QIAO W S , GU B , et al. Research on sensor coope-rative radiation control strategy under task performance constraints[J]. Systems Engineering and Electronics, 2019, 41 (8): 1749- 1754.
22	LIAN F , HOU L M , LIU J , et al. Constrained multi-sensor control using a multi-target MSE bound and a δ-GLMB filter[J]. Sensors, 2018, 18 (7): 2308- 2031. doi: 10.3390/s18072308
23	XU G G , SHAN G L , DUAN X S . Sensor scheduling for ground maneuvering target tracking in presence of detection blind zone[J]. Journal of Systems Engineering and Electronics, 2020, 31 (4): 692- 702. doi: 10.23919/JSEE.2020.000044
24	LI Y Y , GUO L M , HUANG X S . Research and application of multi-target tracking based on GM-PHD filter[J]. Optics and Photonics Journal, 2020, 10 (6): 125- 133. doi: 10.4236/opj.2020.106013
25	NIE Y F , ZHANG T . Improved pruning algorithm for Gaussian mixture probability hypothesis density filter[J]. Journal of Systems Engineering and Electronics, 2018, 29 (2): 229- 235.
26	丁鹭飞, 耿富录, 陈建春. 雷达原理[M]. 5版北京: 电子工业出版社, 2014.
	DING L F , GENG F L , CHEN J C . Radar principles[M]. 5th ed Beijing: Publishing House of Electronics Industry, 2014.
27	LUKE R , STEPHEN B . Non-coherent radar detection performance in medium grazing angle X-Band sea clutter[J]. IEEE Trans.on Aerospace & Electronic Systems, 2017, 53 (2): 669- 682.
28	吴巍, 王国宏, 薛冰. 多传感器协同管理与辐射控制技术[M]. 北京: 国防工业出版社, 2019.
	WU W , WANG G H , XUE B . Multisensor synergistic management and radiation control technology[M]. Beijing: National Defense Industry Press, 2019.
29	吴卫华, 孙合敏, 蒋苏蓉, 等. 随机有限集目标跟踪[M]. 北京: 国防工业出版社, 2020.
	WU W H , SUN H M , JIANG S R , et al. Target tracking with random finite sets[M]. Beijing: National Defense Industry Press, 2020.
30	LI Y W , HU J W , JI B , et al. New accuracy evaluation index for track fusion algorithms[J]. Journal of Shanghai Jiaotong University (Science), 2020, 25 (1): 97- 105.
31	MIRJALILI S , MIRJALILI S M , LEWIS A . Grey wolf optimizer[J]. Advances in Engineering Software, 2014, 69, 46- 61.

衡量指标	h=2			h=3			h=4			MMAPS
衡量指标	本文方法	NAPS	NMAS	本文方法	NAPS	NMAS	本文方法	NAPS	NMAS	MMAPS
OSPA	3.02	3.03	3.02	3.02	3.02	3.03	3.02	3.04	3.03	3.03
OSPA-L	2.74	2.73	2.64	2.72	2.71	2.73	2.66	2.60	2.66	2.73
OSPA-C	0.28	0.30	0.37	0.30	0.31	0.29	0.36	0.44	0.37	0.30
CRC	52.85	74.07	226.32	50.73	62.30	160.77	85.94	108.40	463.62	102.70
TIME/s	29.37	35.30	33.81	34.29	36.14	32.11	40.26	39.66	38.78	22.22

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