系统工程与电子技术 ›› 2024, Vol. 46 ›› Issue (11): 3726-3735.doi: 10.12305/j.issn.1001-506X.2024.11.14

• 传感器与信号处理 • 上一篇    下一篇

组网雷达系统高精度协同跟踪和功率分配方法

张英杰1, 陈洪猛1,*, 高文权1, 兰剑2, 叶春茂1, 陈燕1   

  1. 1. 北京无线电测量研究所, 北京 100854
    2. 西安交通大学自动化科学与工程学院, 陕西 西安 710049
  • 收稿日期:2022-11-03 出版日期:2024-10-28 发布日期:2024-11-30
  • 通讯作者: 陈洪猛
  • 作者简介:张英杰 (1991—), 男, 工程师, 博士, 主要研究方向为目标信息融合、组网雷达资源分配
    陈洪猛 (1986—), 男, 高级工程师, 博士, 主要研究方向为雷达系统总体设计
    高文权 (1981—), 男, 研究员, 硕士, 主要研究方向为雷达系统总体设计
    兰剑 (1983—), 男, 教授, 博士, 主要研究方向为信息融合、目标跟踪、性能评估
    叶春茂 (1981—), 男, 研究员, 博士, 主要研究方向为雷达系统总体技术、雷达成像与识别
    陈燕 (1973—), 女, 研究员, 博士, 主要研究方向为雷达系统总体技术
  • 基金资助:
    国家自然科学基金(U1809202)

High accuracy cooperative tracking and power allocation method in networked radar system

Yingjie ZHANG1, Hongmeng CHEN1,*, Wenquan GAO1, Jian LAN2, Chunmao YE1, Yan CHEN1   

  1. 1. Beijing Institute of Radio Measurement, Beijing 100854, China
    2. School of Automation Science and Engineering, Xi'an Jiaotong University, Xi'an 710049, China
  • Received:2022-11-03 Online:2024-10-28 Published:2024-11-30
  • Contact: Hongmeng CHEN

摘要:

分布式组网雷达系统可充分利用多雷达协同优势提升动目标跟踪性能。然而, 在实际中, 组网雷达系统的发射总功率受限且量测函数的高度非线性, 都会导致目标跟踪精度极大受限。针对上述问题, 提出一种基于不相关转换滤波(uncorrelated conversion based filter, UCF)的高精度目标协同跟踪和资源管理方法, 该滤波方法可充分提取有效量测信息, 提升目标状态估计性能, 且该信息可作为整体框架的反馈信息进一步优化资源分配。首先推导了后验克拉美罗下界作为优化准则, 利用该准则给出当前时刻的最优资源分配; 然后基于分配的功率资源, 进行目标状态估计; 针对强非线性量测函数, 提出一种UCF, 利用不相关转换提取更多原始量测中的信息, 并将其用于线性最小均方误差框架进行状态估计, 从而提高目标状态估计性能。仿真结果验证了所提方法的有效性。

关键词: 组网雷达, 功率分配, 协同跟踪, 不相关转换, 非线性滤波

Abstract:

Distributed networked radar system (NRS) can take full advantage of multi-radar synergistic features to improve the tracking performance of a moving target. However, in fact, the limited total transmitted power and the high nonlinearity of the measurement function of an NRS heavily restrict the target tracking performance. To solve these problems, a high accuracy cooperative tracking and resource allocation method is proposed, in which the uncorrelated conversion based filter (UCF) is utilized to improve the estimation performance by extracting effective measurement information. Moreover, this estimation can be considered as the feedback information for the framework to further optimize the performance of the resource allocation. Firstly, the posterior Cramer-Rao lower bound (PCRLB) is derived as the optimization criterion, which can be utilized to obtain the optimized resource allocation. And then according to the allocated power, the target state can be estimated. A UCF is proposed for strongly nonlinear measurement functions, which utilizes uncorrelated conversion to extract more information from the original measurements and applies it to a linear minimum mean square error framework for state estimation, thereby improving the performance of target state estimation. The simulation results verify the effectiveness of the proposed method.

Key words: networked radar, power allocation, cooperative tracking, uncorrelated conversion, nonlinear filtering

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