一种双重积累自反馈优化的三维多目标检测前跟踪算法

薄钧天; 张嘉毫; 王国宏; 于洪波; 张翔宇; 王万田; 王衡峰

doi:10.11999/JEIT240057

一种双重积累自反馈优化的三维多目标检测前跟踪算法

doi: 10.11999/JEIT240057

1.
海军工程大学电磁能技术全国重点实验室武汉 430033
2.
海军航空大学航空作战勤务学院信息融合研究所烟台 264001
3.
海军航空大学青岛校区航空电子工程与指挥系青岛 266041
4.
中北大学信息与通信工程学院太原 030023

基金项目: 国家杰出青年科学基金(52025072)，中国科协青年人才托举工程项目(2021-JCJQ-QT-009)，国家自然科学基金(62271498)，山东省自然科学基金面上项目(ZR2020MF015, ZR2020QF010)

详细信息

作者简介:
薄钧天：男，博士生，研究方向为抗干扰技术，目标检测与跟踪等

张嘉毫：男，副教授，研究方向为抗干扰技术，天线设计技术等

王国宏：男，教授，研究方向为目标检测与跟踪、信息融合与雷达组网等

于洪波：男，副教授，研究方向为信息融合技术、信号与信息处理等

张翔宇：男，讲师，研究方向为信息融合技术、目标检测与跟踪等

王万田：男，讲师，研究方向为雷达抗干扰技术、目标检测与识别等

王衡峰：男，讲师，研究方向为阵列信号处理、雷达抗干扰技术等

通讯作者:
张嘉毫　jiahao.z@hotmail.com

中图分类号: TN957
计量
- 文章访问数: 111
- HTML全文浏览量: 37
- PDF下载量: 15
- 被引次数: 0
出版历程
- 收稿日期: 2024-01-26
- 修回日期: 2024-04-17
- 网络出版日期: 2024-05-18

A 3D Multi Targets Track before Detect Algorithm with Self-feedback Optimization of Dual Accumulation

1.
National Key Laboratory of Electromagnetic Energy, Naval University of Engineering Wuhan 430033, China
2.
Aviation Combat Service College, Naval Aviation University, Yantai 264001, China
3.
Department of Avionics Engineering and Commad in Qingdao Campus, Naval Aviation University, Qingdao 266041, China
4.
College of Information and Communication Engineering, North University of China, Taiyuan 030023, China

Funds: The National Fund for Distinguished Young Scholars (52025072), The Young Elite Scientists Sponsorship Program by CAST (2021-JCJQ-QT-009), The National Natural Science Foundation of China (62271498), Shandong Provincial Natural Science Foundation, China (ZR2020MF015, ZR2020QF010)

摘要

摘要: 针对3维微弱多目标检测问题，该文提出一种双重积累自反馈优化的3级平行线坐标变换(PT)检测前跟踪(TBD)算法。通过将平行线坐标变换引入至TBD技术，依次在规格化的径向距离-时间、方位角-时间和俯仰角-时间平面对量测点进行投影变换和双重非相参积累，同时利用功率累积结果反馈优化二值积累，有效缓解强目标淹没弱目标和编队目标串扰问题。仿真结果表明，当整体信杂比达到10 dB时，所提算法的全局检测概率接近80%，证明了该算法的有效性。
- 雷达 /
- 检测前跟踪 /
- 多目标 /
- 平行线坐标变换 /
- 自反馈优化
Abstract: Considering the problem of 3D weak multi target detection, a 3-level Parallel-line-coordinate Transformation (PT) Track Before Detect (TBD) algorithm based on the dual accumulation self-feedback optimized is proposed in this paper. By introducing PT into TBD technology, the measurement points are transformed and accumulated sequentially on the normalized radial distance-time, azimuth angle-time and elevation angle-time planes, then the power accumulation are used to feedback the optimized binary accumulation, effectively mitigating the mutual interference between strong targets overwhelming weak targets and formation targets. Simulation results show that when the overall signal-to-clutter ratio reaches 10 dB, the overall detection probability of the proposed algorithm is close to 80%, demonstrating the effectiveness of the algorithm.
- Radar /
- Track Before Detect (TBD) /
- Multiple targets /
- Parallel-line-coordinate Transformation (PT) /
- Self-feedback optimized

HTML全文

图 1 优化积累结果

下载: 全尺寸图片幻灯片

图 2 3级检测结果

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图 3 最终检测结果

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图 4 不同目标检测概率

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图 5 不同目标数目检测概率

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表 1 目标参数

序号	初始位置(km)	初始速度(m/s)	加速度(m/s²)	RCS(m²)	信杂比(dB)
1	$ \left({15,25}，{5}\right) $	$\left( {{{180, - 20,5}}} \right)$	$\left( {{{0,0,0}}} \right)$	${{0}}{{.2}}$	${{10}}{{.2}}$
2	$ \left({13,18}，{7}{.5}\right) $	$\left( {{{180, - 200, - 10}}} \right)$	$\left( {{{2,1}}{{.5,0}}} \right)$	${{0}}{{.15}}$	${{13}}{{.2}}$
3	$ \left({25,17}，{4}\right) $	$\left( {{{ - 30,200,12}}} \right)$	$\left( {{{ - 1,3,0}}} \right)$	${{0}}{{.2}}$	${{9}}{{.6}}$
4	$ \left({18,19}{.5}，{6}\right) $	$\left( {{{200,180,0}}} \right)$	$ \left( {{{0,0,0}}} \right) $	${{0}}{{.1}}$	${{8}}{{.5}}$
5	$ \left({18,20}，{6}\right) $	$\left( {{{200,180,0}}} \right)$	$\left( {{{0,0,0}}} \right)$	${{0}}{{.1}}$	${{8}}{{.0}}$
6	$ \left({18,20}{.5}，{6}\right) $	$\left( {{{200,180,0}}} \right)$	$\left( {{{0,0,0}}} \right)$	${{0}}{{.1}}$	${{7}}{{.7}}$

下载: 导出CSV

表 2 算法运行时间随杂波密度变化

杂波密度	30	60	90	120	150
时间(s)	11.0	16.4	26.4	37.3	46.9
杂波密度	180	210	240	270	300
时间(s)	53.8	59.6	68.7	75.2	80.2

下载: 导出CSV

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