量测点迹空间聚类的多传感器多帧检测算法

doi:10.12305/j.issn.1001-506X.2021.06.10

系统工程与电子技术 ›› 2021, Vol. 43 ›› Issue (6): 1533-1540.doi: 10.12305/j.issn.1001-506X.2021.06.10

量测点迹空间聚类的多传感器多帧检测算法

张佳琦¹, 陶海红¹, 张修社^2,*

1. 西安电子科技大学雷达信号处理国家重点实验室, 陕西西安 710071
2. 西安导航技术研究所, 陕西西安 710068

收稿日期:2020-12-31 出版日期:2021-05-21 发布日期:2021-05-28
通讯作者: 张修社
作者简介:张佳琦(1987—), 男, 高级工程师, 博士研究生, 主要研究方向为多传感器组网融合、多帧联合检测与估计|陶海红(1976—), 女, 教授, 博士, 主要研究方向为雷达系统、阵列信号处理|张修社(1965—), 男, 研究员, 硕士, 主要研究方向为雷达系统、协同作战系统
基金资助:
JKW创新项目(-H863-XJ-XXX-02)

Multi-sensor multi-frame detection algorithm based on measurement plots space clustering

Jiaqi ZHANG¹, Haihong TAO¹, Xiushe ZHANG^2,*

1. National Key Lab of Radar Signal Processing, Xidian University, Xi'an 710071, China
2. Xi'an Research Institute of Navigation Technology, Xi'an 710068, China

Received:2020-12-31 Online:2021-05-21 Published:2021-05-28
Contact: Xiushe ZHANG

摘要/Abstract

摘要：

针对强干扰环境下微弱目标检测算法运算复杂度高、虚假目标数量多等问题, 利用目标量测点迹在多传感器之间的分布特性及目标能量的可累加性, 提出一种量测点迹聚类的多帧检测算法。该算法首先利用同源检测对多传感的器量测点迹的有效性进行判断，实现杂波/噪声剔除；其次在空间和时间两个维度对目标的能量进行积累实现微弱目标检测。仿真结果和性能分析表明, 该算法能够大幅降低运算复杂度, 提高虚假目标的抑制能力, 并能够提升微弱目标的检测概率, 验证了该算法的有效性和工程的可行性。

关键词: 多目标检测, 多传感器融合, 量测点迹空间聚类, 多帧检测算法

Abstract:

In order to reduce the high computational complexity and the number of false targets in strong interference environment, using the distribution characteristics of target measurement plots among multiple sensors and accumulation of target energy, a multi-frame detection algorithm based on measurement plots space clustering is proposed. In the proposed algorithm, homology detection is used to judge the validity of measurement plots from multiple sensors to eliminate clutter/noise firstly. And then, the target energy is accumulated in space and time to detect weak target. The simulation result and performance analysis show that the proposed algorithm can greatly reduce the computational complexity and enhance the ability of false target suppression, and also can improve the detection probability of weak target, so effectiveness and engineering feasibility of the proposed algorithm are verified.

Key words: multiple targets detection, multi-sensor fusion, measurement plots space clustering, multi-frame detection algorithm

中图分类号:

TN953

张佳琦, 陶海红, 张修社. 量测点迹空间聚类的多传感器多帧检测算法[J]. 系统工程与电子技术, 2021, 43(6): 1533-1540.

Jiaqi ZHANG, Haihong TAO, Xiushe ZHANG. Multi-sensor multi-frame detection algorithm based on measurement plots space clustering[J]. Systems Engineering and Electronics, 2021, 43(6): 1533-1540.

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算法	FAR
算法	10^-1	10^-2	10^-3
CF-MFD	46.246	15.079	1.459
DF-MFD	14.596	3.865	0.338
MPC-MFD	12.428	3.886	0.348

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量测点迹空间聚类的多传感器多帧检测算法

Multi-sensor multi-frame detection algorithm based on measurement plots space clustering

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算法		步骤
算法	聚类	多帧检测	融合
CF-MFD	—	$O\left(K P L N_{\text {plot }}^{2}\right)+\sum\limits_{i=1}^{N_{\text {plot }} / w} O\left(K P L\left(N_{\text {plot }}-i w\right)^{2}\right)$	—
DF-MFD	—	$O\left(P L N_{\text {plot }}^{2}\right)+\sum\limits_{i=1}^{N_{\text {plot }} / w} O\left(P L\left(N_{\text {plot }}-i w\right)^{2}\right)$	$\sum\limits_{i=1}^{K} O\left[L(K-i)\left(N_{\text {track }}^{\prime}-\right.\right.\left.\left.(i-1) \frac{N_{\text {track }}^{\prime}}{C_{\text {track }}}\right)^{2}\right]$
MPC-MFD	$\sum\limits_{i=1}^{K} O\left[L(K-i)\left(N_{\text {plot }}-\right.\right.\left.\left.(i-1) \frac{N_{\text {plot }}}{C_{\text {plot }}}\right)^{2}\right]$	$O\left(P L N_{\text {plot }}^{\prime 2}\right)+\sum\limits_{i=1}^{N_{\text {ploot }}^{\prime} / w} O\left(P L\left(N_{\text {plot }}^{\prime}-i w\right)^{2}\right)$	—