基于自适应压缩感知的复杂弹道群目标跟踪技术

doi:10.3969/j.issn.1001-506X.2020.08.10

摘要/Abstract

摘要：

复杂弹道群目标空间密集程度高,群内目标相互遮挡,在给定雷达工作带宽前提下如何对复杂群目标进行有效分辨、实现航迹的连续稳定跟踪已成为亟待解决的难题。针对该问题,首先通过二体运动预测获得航迹群预测中心,根据预测偏差与分辨阈值关系,自适应开启局部压缩感知群目标分辨算法,提高群目标遮蔽分辨能力。其次，引入航迹片段拼接思想,利用已有航迹档案构建假设检验,对航迹片段进行关联、平滑处理和批号管理,有效减少换批和非线性估计误差。仿真结果表明,所提算法大大增加了可分辨时间,分辨能力可达80%,平均跟踪时长提高一倍,中断期间跟踪均方根误差显著降低,且航迹稳定跟踪并全程未换批,为弹道群目标精确稳定跟踪提供了一种新的思路。

关键词: 群目标, 压缩感知, 航迹片段拼接

Abstract:

The complex ballistic group targets are spaced highly close, and they are mutual occlusive. Hence, how to distinguish and precisely track the complex ballistic target under a presented radar working band has become an urgent problem. To solve this problem, an adaptive local compressed sensing (CS) algorithm based cognition for group targets resolution is proposed according to prediction bias with resolution threshold. The algorithm first predicts the group center by two-body movement, then adaptively using local CS algorithm according the threshold to improve the resolving ability for group targets. Secondly, the track segment stitching (TSS) method is induced using track file to construct test hypothesis, then to correlate and smooth the track segments and finally to manage the batch, which can greatly reduce the batch mismatch and nonlinear estimation error. Simulation results show that the proposed algorithm greatly increases the resolving time and the resolving ability raises up to 80%.The average tracking time is double compared with the traditional algorithm, and the tracking root mean square error (RMSE) is significantly decreased during track interrupt. The group targets are consecutively tracking and keep the unique batch, which provides a new precise tracking method for ballistic group targets.

Key words: group target, compressed sensing (CS), track segment stitching (TSS)

中图分类号:

TN953

黄强, 俞建国, 时鹏飞. 基于自适应压缩感知的复杂弹道群目标跟踪技术[J]. 系统工程与电子技术, 2020, 42(8): 1710-1717.

Qiang HUANG, Jianguo YU, Pengfei SHI. Complex ballistic group targets tracking based on adaptive compressed sensing[J]. Systems Engineering and Electronics, 2020, 42(8): 1710-1717.

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跟踪误差	传统算法	本文算法
距离RMSE/m	37.2	21.6
方位角RMSE/(°)	0.13	0.063
俯仰角RMSE/(°)	0.14	0.071

连续性指标	传统算法	本文算法
平均跟踪时长/s	174.3	367.2
平均航迹批数	6.0	12.0
相关正确率/%	90.3	99.8

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