仅测角机动目标跟踪原始对偶高斯粒子滤波

张宏伟

doi:10.11999/JEIT230413

仅测角机动目标跟踪原始对偶高斯粒子滤波

doi: 10.11999/JEIT230413

张宏伟^,

中山大学航空航天学院深圳 518107

基金项目: 广东省基础与应用基础研究基金(2019A1515111099)，中山大学青年培育项目(20lgpy72)，中国科学院空间精密测量重点实验室开放基金(SPMT2021002, SPMT2022001)

详细信息

作者简介:
张宏伟：女，副研究员，博士，研究方向为目标跟踪、智能信息处理

通讯作者:
张宏伟　zhanghw69@mail.sysu.edu.cn

中图分类号: TN953
计量
- 文章访问数: 185
- HTML全文浏览量: 103
- PDF下载量: 15
- 被引次数: 0
出版历程
- 收稿日期: 2023-05-15
- 修回日期: 2023-06-30
- 网络出版日期: 2023-07-06
- 刊出日期: 2024-04-24

Angle-only Maneuvering Target Tracking Using Primal-dual Gaussian Particle Filtering

ZHANG Hongwei^,

School of Aeronautics and Astronautics, Sun Yat-sen University, Shenzhen 518107, China

Funds: Guang Dong Basic and Applied Fundamental Research Fund Project (2019A1515111099), Sun Yat-sen University Youth Cultivation Project (20lgpy72), The Open Research Fund of CAS Key Laboratory of Space Precision Measurement Technology (SPMT2021002, SPMT2022001)

摘要

摘要: 为消减仅测角机动目标跟踪系统中由时空不一致引起的投影基点偏移和高斯截断两类误差，该文采用映射表示和$ {\ell _1} $-$ {\ell _{2,1}} $稀疏正则表征时空因果一致约束，引入模糊综合贴近度建立次优建议分布，构建因果不变结构传递粒子集合以近似目标后验高斯积分，推导原始对偶高斯粒子滤波(PDGPF)算法。实验结果表明，相比交会测量最小二乘法，PDGPF算法定位旋翼无人机(UAV)的精度提升了18.4%～69.6%。相比于软约束辅助粒子滤波(SCAPF)算法，PDGPF算法在时空映射一致约束下能够自适应地修正粒子的权值，从而更为准确、稳定地跟踪机动点目标，整体计算负担减小了12.9%。
- 机动目标跟踪 /
- 仅测角 /
- 时空不一致 /
- 原始对偶 /
- 因果不变
Abstract: To reduce the mapping basepoint offset and Gaussian truncation errors caused by spatiotemporal inconsistency in angle-only maneuvering target tracking systems, mapping representation and $ {\ell _1} $-$ {\ell _{2,1}} $ sparse regularization to represent spatiotemporal causal consistency constraints are used, the fuzzy comprehensive closeness is introduced to establish the suboptimal proposal distribution, the particle set in a causal invariant structure to approximate the Gaussian integration for target posterior is propagated, and the Primal-Dual Gaussian Particle Filtering (PDGPF) algorithm is derived. Simulation results show that, compared to the intersection measurement method with least squares, the accuracy for the PDGPF to locate a rotor Unmanned Aerial Vehicle (UAV) has improved by 18.4%～69.6%. Compared to the Soft Constrained Auxiliary Particle Filtering (SCAPF) algorithm, the PDGPF algorithm can adaptively correct the particle weights under the spatiotemporal mapping consistent constraints, obtaining more accurate and stable state estimation for tracking a maneuvering point target, reducing the overall computational burden by 12.9%.
- Maneuvering target tracking /
- Angle-only /
- Spatiotemporal inconsistency /
- Primal-dual /
- Causal invariant

HTML全文

图 1 双站方向盘测角系统交会测量定位旋翼无人机

下载: 全尺寸图片幻灯片

图 2 跟踪目标在3轴上的投影距离

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图 3 高斯过程法仿真机动点目标航迹

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图 4 双站测角传感器测量的方位和俯仰角度

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图 5 3种跟踪算法滤波性能比较

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表 1 交会测量最小二乘法和PDGPF算法定位旋翼无人机在3轴上的投影误差

	交会测量最小二乘法		PDGPF算法
	最大值 (m)	方差 (m²)	最大值(m)	方差 (m²)
$ x $轴	4.85	1.18	4.51	1.10
$ y $轴	11.86	4.12	6.04	1.60
$ z $轴	9.33	3.95	3.35	1.20

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表 2 100轮蒙特卡罗实验统计滤波误差(均值、协方差)和1轮蒙特卡罗实验运行时间

算法	位置均方根误差		$ x $轴上滤波误差		$ y $轴上滤波误差		$ z $轴上滤波误差		运行时间 (s)
算法	均值 (m)	协方差 (m²)	最大值 (m)	协方差 (m²)	最大值 (m)	协方差 (m²)	最大值 (m)	协方差 (m²)	运行时间 (s)
RGPMT	32.32	27.74	45.25	12.71	98.00	31.99	39.28	11.42	0.118
SCAPF	18.82	15.59	24.50	6.53	–50.53	15.76	24.58	6.16	1.442
PDGPF	10.72	9.14	12.06	3.33	–30.61	7.76	15.76	3.65	1.278

下载: 导出CSV

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