利用伯努利滤波的多目标机动雷达误差配准方法  

Mobile Radar Registration with Multiple Targets Based on Bernoulli Filter

作　　者：邓洪高[1,4] 余润华 纪元法 吴孙勇[2,3] 孙希延 DENG Honggao;YU Runhua;JI Yuanfa;WU Sunyong;SUN Xiyan(School of Information and Communication,Guilin University of Electronic Technology,Guilin 541004,China;Guangxi Key Laboratory of Precision Navigation Technology and Application,Guilin University of Electronic Science and Technology,Guilin 541004,China;School of Mathematics and Computing Science,Guilin University of Electronic Science and Technology,Guilin 541004,China;National&Local Joint Engineering Research Center of Satellite Navigation Positioning and Location Service,Guilin 541004,China;GUET-Nanning E-Tech Research Institute Co.,Ltd.,Nanning 530031,China)

机构地区：[1]桂林电子科技大学信息与通信学院,桂林541004 [2]桂林电子科技大学广西精密导航技术与应用重点实验室,桂林541004 [3]桂林电子科技大学数学与计算科学学院,桂林541004 [4]卫星导航定位与位置服务国家地方联合工程研究中心,桂林541004 [5]南宁桂电电子科技研究院有限公司,南宁530031

出　　处：《电子与信息学报》2024年第10期4035-4043,共9页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(U23A20280,62061010,62161007);广西科技厅项目(AB23026120)。

摘　　要：传统的组网雷达多目标误差配准方法通常假设数据关联关系已知,但在平台机动的情况下,系统同时存在雷达测量偏差和平台姿态角偏差,且雷达观测过程中会受到杂波干扰,导致数据关联尤为困难。针对这一问题,该文提出一种基于伯努利滤波的多目标机动雷达误差配准方法。首先建立系统偏差的量测与状态方程,然后将系统偏差建模成伯努利随机有限集,利用公共坐标系下的原始量测可实现系统偏差在伯努利滤波框架下的递推估计,有效避免了数据关联问题。同时,为了充分利用多目标量测信息,提出一种修正的贪婪量测划分方法,在每个滤波时刻挑选出系统偏差对应的最优量测子集,利用量测子集中的多量测信息实现系统偏差的集中式融合估计,提高了系统偏差的估计精度和收敛速度。仿真实验表明,所提方法能够在数据关联未知的多目标多杂波场景下对雷达测量偏差和平台姿态角偏差进行有效估计,在平台姿态角变化率较低时,所提方法具有较强的适应性。Traditional methods for multi-target bias registration in networked radar system typically assume that the data association relationship is known.However,in the case of platform maneuvering,there are simultaneously radar measurement biases and platform attitude angle biases,and the radar observation process is prone to clutter interference,resulting in difficulties in data association.To address this issue,a multi-target mobile radar bias registration method based on Bernoulli filter is proposed.Firstly,the measurement and state equations for the system biases are established,and then the system biases are modeled as a Bernoulli random finite set.The recursive estimation of the system biases under the Bernoulli filtering framework is achieved using the original measurements in a common coordinate system,effectively avoiding the data association.Additionally,to fully utilize multi-target measurement information,a modified greedy measurement partitioning method is proposed to select the optimal measurement subset corresponding to the system biases at each filtering time step,and the centralized fusion estimation of the system biases is performed using the multi-measurement information in the measurement subset,improving the estimation accuracy and convergence speed of the system biases.Simulation experiments show that the proposed method can effectively estimate radar measurement biases and platform attitude angle biases in multi-target and cluttered scenarios with unknown data association.Moreover,this method demonstrates strong adaptability when the platform attitude angle variation rate is low.

关 键 词：误差配准 数据关联 伯努利滤波 集中式融合 量测划分 

分 类 号：TN959.1[电子电信—信号与信息处理] V243.2[电子电信—信息与通信工程]