非均匀杂波环境下的低空多目标跟踪算法

doi:10.12305/j.issn.1001-506X.2025.10.09

摘要/Abstract

摘要：

针对低空环境中自然及人为因素干扰导致背景杂波呈现非均匀分布，使多目标跟踪算法中出现较多虚假目标问题，提出一种基于聚类的标签无迹卡尔曼概率假设密度低空多目标跟踪算法。首先，通过密度聚类将量测数据划分为密集区域和稀疏区域，分别去除各区域中的杂波量测，使各区域量测数据均匀化，从而抑制了非均匀杂波的干扰。然后，引用标签进行目标区分和航迹维持，使目标轨迹具有连续性。仿真结果表明，该算法有效抑制了虚假目标的产生，实现了非均匀杂波环境下准确、连续且高效的多目标跟踪。

关键词: 多目标跟踪, 随机有限集, 密度聚类, 杂波抑制, 有限混合模型

Abstract:

Aimming at the problem that in low-altitude environments, natural and human-made interferences can cause the background clutter to exhibit non-uniform distributions, resulting in an increase of false targets in multi-target tracking algorithms, a label unscented Kalman probability hypothesis density based on clustering （C-Label-UK-PHD） algorithm is proposed for low-altitude multi-target tracking. The algorithm first applies density clustering to partition the measurement data into dense and sparse regions, removing clutter measurements from each region to homogenize the measurement data, thereby suppressing the interference from non-uniform clutter. Then labels are used to distinguish between different targets and maintain the continuity of target trajectories. The simulation results show that this algorithm effectively suppresses the generation of false targets and achieves accurate, continuous and efficient multi-target tracking in non-uniform clutter environment.

Key words: multi-target tracking, random finite set, density clustering, clutter suppression, finite mixture model

中图分类号:

TN 957.52

尚李娜, 董玫, 陈伯孝. 非均匀杂波环境下的低空多目标跟踪算法[J]. 系统工程与电子技术, 2025, 47(10): 3218-3227.

Lina SHANG, Mei DONG, Baixiao CHEN. Low-altitude multi-target tracking algorithm in non-uniform clutter environment[J]. Systems Engineering and Electronics, 2025, 47(10): 3218-3227.

图/表 16

表1

图1

图2

图3

表2

图4

图5

图6

图7

图8

图9

图10

图11

图12

图13

图14

参考文献 20

1	权在昕, 武丁杰, 高嘉静, 等. 城市低空空中交通及无人机路径规划研究综述[J]. 航空计算技术, 2024, 54(2): 121−126.
	QUAN Z X, WU D J, GAO J J, et al. Overview of urban low altitude air mobility and UAV path planning[J]. Aeronautical Computing Technique, 2024, 54(2): 121−126.
2	冯杨, 蒋超, 崔玉伟. 俄乌冲突中无人机作战运用及启示[J]. 中国军转民, 2022, (23): 35- 40.
	FENG Y, JIANG C, CUI Y W. The application and enlightenment of UAVs in Russia-Ukraine conflict[J]. Defense Industry Conversion in China, 2022, (23): 35- 40.
3	LI X R, BAR-SHALOM Y. Tracking in clutter with nearest neighbor filters: analysis and performance[J]. IEEE Trans. on Aerospace and Electronic Systems, 1996, 32 (3): 995- 1010. doi: 10.1109/7.532259
4	REID D. An algorithm for tracking multiple targets[J]. IEEE Trans. on Automatic Control, 1979, 24 (6): 843- 854. doi: 10.1109/TAC.1979.1102177
5	FORTMANN T, BAR-SHALOM Y, SCHEFFE M. Sonar tracking of multiple targets using joint probabilistic data association[J]. IEEE Journal of Oceanic Engineering, 1983, 8 (3): 173- 184. doi: 10.1109/JOE.1983.1145560
6	MAHLER R. Multitarget Bayes filtering via first-order multitarget moments[J]. IEEE Trans. on Aerospace and Electronic Systems, 2003, 39 (4): 1152- 1178.
7	VO B N, MA W K. The Gaussian mixture probability hypothesis density filter[J]. IEEE Trans. on Signal Processing, 2006, 54 (11): 4091- 4104. doi: 10.1109/TSP.2006.881190
8	PANTA K, CLARK D E, VO B N. Data association and track management for the Gaussian mixture probability hypothesis density filter[J]. IEEE Trans. on Aerospace and Electronic Systems, 2009, 45 (3): 1003- 1016. doi: 10.1109/TAES.2009.5259179
9	VO B T, VO B N, CANTONI A. Analytic implementations of the cardinalized probability hypothesis density filter[J]. IEEE Trans. on Signal Processing, 2007, 55 (7): 3553- 3567. doi: 10.1109/TSP.2007.894241
10	WOOD T M, YATES C A, WILKINSON D A, et al. Simplified multitarget tracking using the PHD filter for microscopic video data[J]. IEEE Trans. on Circuits and Systems for Video Technology, 2012, 22 (5): 702- 713. doi: 10.1109/TCSVT.2011.2177937
11	GRANSTROM K, ORGUNER U. A PHD filter for tracking multiple extended targets using random matrices[J]. IEEE Trans. on Signal Processing, 2012, 60 (11): 5657- 5671. doi: 10.1109/TSP.2012.2212888
12	LIAN F, HAN C Z, LIU W F. Estimating unknown clutter intensity for PHD filter[J]. IEEE Trans. on Aerospace and Electronic Systems, 2010, 46 (4): 2066- 2078. doi: 10.1109/TAES.2010.5595616
13	LIU W F, CHEN Y M, CUI H L, et al. A nonuniform clutter intensity estimation algorithm for random finite set filters[J]. IEEE Trans. on Aerospace and Electronic Systems, 2018, 54 (6): 2911- 2925. doi: 10.1109/TAES.2018.2832958
14	CHEN X, THARMARASA R, PELLETIER M, et al. Integrated clutter estimation and target tracking using Poisson point processes[J]. IEEE Trans. on Aerospace and Electronic Systems, 2012, 48 (2): 1210- 1235. doi: 10.1109/TAES.2012.6178058
15	马艳琴, 陆耀宾, 李向前, 等. 幅度信息辅助的海面低空多目标多假设跟踪算法[J]. 现代雷达, 2024, 46 (3): 9- 15.
	MA Y Q, LU Y B, LI X Q, et al. Amplitude information aided multiple hypothesis tracking algorithm for low altitude marine multi-target at sea[J]. Modern Radar, 2024, 46 (3): 9- 15.
16	陈华志. 基于雷达/红外数据融合多目标跟踪滤波算法的研究[D]. 成都: 电子科技大学, 2011.
	CHEN H Z. A multitarget tracking algorithm based on radar and infrared sensor data fusion[D]. Chengdu: University of Electronic Science and Technology of China, 2011.
17	江玉玲, 熊振南, 唐基宏. 基于轨迹段DBSCAN的船舶轨迹聚类算法[J]. 中国航海, 2019, 42 (3): 1- 5.
	JIANG Y L, XIONG Z N, TANG J H. Ship trajectory clustering algorithm based on DBSCAN[J]. Navigation of China, 2019, 42 (3): 1- 5.
18	卢鹏博. 基于随机有限集的多目标跟踪算法研究[D]. 哈尔滨: 哈尔滨工程大学, 2022.
	LU P B, Research on multi-target tracking algorithm based on random finite set[D]. Harbin: Harbin Engineering University, 2022.
19	刘政玮, 陈映, 鲁耀兵. 一种强杂波密度环境下的多目标跟踪算法[J]. 现代雷达, 2022, 44 (2): 16- 22.
	LIU Z W, CHEN Y, LU Y B. A multi-target tracking algorithm under strong clutter density environment[J]. Modern Radar, 2022, 44 (2): 16- 22.
20	SCHUHMACHER D, VO B T, VO B N. A consistent metric for performance evaluation of multi-object filters[J]. IEEE Trans. on Signal Processing, 2008, 56 (8): 3447- 3457. doi: 10.1109/TSP.2008.920469

模型	权重	均值/（m, rad, rad）	协方差矩阵/$ \rm ({{m} ^2},{{rad} ^2},{{rad} ^2}) $
1	0.5	—	—
2	0.3	$[1\;000,-\text {π}/3, \text {π}/8] $	$ {\mathrm{diag}}[225,{({\text {π} \mathord{\left/ {\vphantom {\text {π} {24}}} \right. } {24}})^2},{({\text {π} \mathord{\left/ {\vphantom {\text {π} {20}}} \right. } {20}})^2}] $
3	0.2	$ [1\; 400,\text{π}/3,\text{π}/12] $	$ {\mathrm{diag}}[1\;600,{({\text {π} \mathord{\left/ {\vphantom {\text {π} {20}}} \right. } {20}})^2},{({\text {π} \mathord{\left/ {\vphantom {\text {π} {20}}} \right. } {20}})^2}] $

[1]	傅嘉政, 郭玉霞, 张博祥, 柴雷, 易伟, 孔令讲. 基于TPHD与TCPHD滤波器的分布式多目标跟踪方法[J]. 系统工程与电子技术, 2025, 47(9): 2828-2838.
[2]	马晓萌, 邓东明, 沈永健, 丁金闪, 郝国卿. 基于单站无源运动定位的多目标跟踪方法[J]. 系统工程与电子技术, 2025, 47(8): 2549-2557.
[3]	张洪源, 龚柏春, 韩飞, 孙玥, 宁昕. 复杂环境下天基无源协同多目标初始定轨方法[J]. 系统工程与电子技术, 2025, 47(5): 1404-1413.
[4]	潘超凡, 李润生, 胡庆, 包全福, 保永强. 基于惯性预测的遥感场景舰船多目标跟踪模型[J]. 系统工程与电子技术, 2025, 47(1): 41-51.
[5]	嵇志康, 周子楠, 李煊鹏. 面向雷达信号分选的自约束搜索密度聚类算法[J]. 系统工程与电子技术, 2025, 47(1): 62-69.
[6]	单靖原, 卢雨, 凌寒羽. 鲁棒自适应的机载外辐射源雷达多目标跟踪算法[J]. 系统工程与电子技术, 2024, 46(9): 2902-2915.
[7]	吴巍, 薛冰, 刘丹丹. 基于Tri-feature训练的目标与海杂波鉴别算法[J]. 系统工程与电子技术, 2024, 46(9): 2935-2940.
[8]	江林海, 龚柏春, 刘传凯, YANG Yang, 张仁勇. 天基分布式无源探测的空间多目标跟踪方法[J]. 系统工程与电子技术, 2024, 46(8): 2789-2797.
[9]	姚思亦, 李万春, 高林, 张花国, 胡航玮. 基于分布式PMHT的多传感器多目标跟踪[J]. 系统工程与电子技术, 2024, 46(7): 2184-2190.
[10]	安孟昀, 殷加鹏, 黄建开, 李永祯, 王雪松. 一种基于谱连续的极化-多普勒气象雷达信号重构方法[J]. 系统工程与电子技术, 2024, 46(7): 2285-2293.
[11]	杨艺琼, 吴建新, 梁毅. 机载双基雷达波束域杂波抑制方法[J]. 系统工程与电子技术, 2024, 46(6): 1935-1945.
[12]	卓娅玲, 李响, 左磊, 胡娟. 随机数据丢包情况下组网雷达功率分配算法[J]. 系统工程与电子技术, 2024, 46(6): 1957-1966.
[13]	齐美彬, 庄硕, 胡晶晶, 杨艳芳, 胡元奎. 基于联合GLMB滤波器的可分辨群目标跟踪[J]. 系统工程与电子技术, 2024, 46(4): 1212-1219.
[14]	王安安, 谢文冲, 王永良. 基于稀疏恢复的双基地机载雷达杂波抑制方法[J]. 系统工程与电子技术, 2024, 46(2): 517-525.
[15]	曾舒雅, 饶彬. 基于动力学守恒定律的弹道目标关联方法[J]. 系统工程与电子技术, 2024, 46(2): 684-691.