A Fusion Method for Target Tracking Measurement Data of Active and Passive Radar

doi:10.3969/j.issn.1009-086x.2024.04.011

Abstract

Abstract:

From the perspective of active and passive radar target tracking data fusion， this paper summarizes the measurement error distribution characteristics of discrete space distributed active and passive radars for the same air target through a qualitative analysis method of radar measurement error combing mathematical approximation processing and geometric figure analysis. A novel fusion method for active and passive radar target measurement data is proposed when the angle between the target and the deployment point of the active and passive radar is small. By optimizing the weighting coefficient allocation criteria to fully combine the advantages of the each radar measurement accuracy， the target tracking data is fused in the geosphere coordinate system to improve measurement accuracy， the target fusion data is smoothed using the extended Kalman filter algorithm. The simulation results show that this method can combine the advantages of active and passive radar target measurement accuracy， reduce the measurement error ellipse after target data fusion， and overall improve the measurement accuracy of fused target tracking data.

Key words: active radar, passive radar, target measurement error characteristics, target data fusion, measurement error ellipse, extended Kalman filter （EKF）

摘要：

从主动雷达和被动雷达对空中目标跟踪数据融合的角度，通过一种数学近似处理与几何图形解析相结合的雷达测量误差定性分析方法，归纳空间离散分布主、被动雷达对同一目标的测量误差分布特征。在满足目标与主、被动雷达部署点所成张角较小时，提出一种新颖的主、被动雷达目标测量数据融合方法，通过优化加权系数分配准则以充分结合2种雷达各自的测量精度优势，对目标跟踪数据在地理球坐标系中进行融合处理以提高测量精度，最后采用扩展卡尔曼滤波算法对目标融合数据进行平滑处理。仿真结果表明，本方法可以结合主动雷达和被动雷达的目标测量精度优势，能够缩小目标数据融合后的测量误差椭圆，整体提高融合后的目标跟踪数据测量精度。

关键词: 主动雷达, 被动雷达, 目标测量误差特性, 目标数据融合, 测量误差椭圆, 扩展卡尔曼滤波

CLC Number:

TN958

Qiang LÜ, Haijun LIU, Hui ZHONG, Dezhong LIU. A Fusion Method for Target Tracking Measurement Data of Active and Passive Radar[J]. Modern Defense Technology, 2024, 52(4): 108-116.

吕强, 刘海军, 钟珲, 刘德忠. 一种主被动雷达目标跟踪测量数据融合方法[J]. 现代防御技术, 2024, 52(4): 108-116.

Figures/Tables 7

Fig. 1 Two approximate plane error ellipses in target measurement by active and passive radar

Fig.2 Two approximate plane error ellipses distribution feature at small angle phi

Fig. 3 Relative position of the active and passive radar and the target’s true route on the Oxz plane

Fig. 4 Oxz plane distribution of target data before fusion

Fig. 5 Oxz plane distribution of target data after fusion

Fig. 6 Statistical analysis of maximum measurement error before and after target data fusion

Fig. 7 Statistical analysis of maximum measurement error for target fusion data using three methods

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