航空学报 > 2014, Vol. 35 Issue (4): 1079-1090   doi: 10.7527/S1000-6893.2013.0427

地空协同防空目标抗差跟踪算法

崔亚奇, 熊伟, 何友   

  1. 海军航空工程学院 信息融合研究所, 山东 烟台 264001
  • 收稿日期:2013-05-24 修回日期:2013-10-16 出版日期:2014-04-25 发布日期:2013-11-14
  • 通讯作者: 熊伟,Tel.:0535-6635685 E-mail:xiongweimail@tom.com E-mail:xiongweimail@tom.com
  • 作者简介:崔亚奇男,博士研究生。主要研究方向:雷达数据处理、雷达组网。 E-mail:cui_yaqi@126.com;熊伟男,博士,教授。主要研究方向:雷达数据处理、雷达组网、指挥控制。Tel:0535-6635685 E-mail:xiongweimail@tom.com
  • 基金资助:

    国家自然科学重点基金(61032001)

Target Robust Tracking Algorithm in Ground-air Collaborative Defense System

CUI Yaqi, XIONG Wei, HE You   

  1. Research Institute of Information Fusion, Naval Aeronautical and Astronautical University, Yantai 264001, China
  • Received:2013-05-24 Revised:2013-10-16 Online:2014-04-25 Published:2013-11-14
  • Supported by:

    National Natural Science Foundation of China (61032001)

摘要:

针对现有系统误差配准算法以已知系统误差变化模型为前提条件、相应的目标状态估计易受系统误差配准结果影响等不足之处,在机载雷达与地基雷达协同防空预警体系下,对系统误差存在情况下的目标跟踪问题进行了研究,并提出了有效的地空协同防空目标抗差跟踪算法。仿真结果表明所提算法可得到无偏、稳定、有效的目标状态估计,并且相对于系统误差目标状态联合估计算法,所提算法计算量小,对系统误差变化有很强的鲁棒性,可适应实际工程应用中可能出现的异常情况,为后续决策提供稳定有效的目标信息。

关键词: 系统误差, 传感器配准, 协同防空, 抗差跟踪, 目标状态估计, 雷达

Abstract:

The existing systematic bias registration algorithms require that systematic bias models should be known before estimation; therefore subsequent target state estimate is susceptible to the estimation results of systematic biases. To cope with the above deficiencies, the problem of how to effectively track targets in the presence of systematic biases is studied in this paper for the airborne and shore-based radar collaborative defense system. An effective robust target tracking algorithm in a ground-air collaborative defense system is proposed. The simulation result shows that the algorithm proposed in this paper can obtain unbiased, stabilized and effective estimate of the target state. And it is robust to changes in systematic biases, adaptive to abnormal situations that may arise in practical application, and can provide effective target information for follow-up decision making.

Key words: systematic bias, sensor registration, collaborative defense, robust tracking, target state estimate, radar

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