Anti-bias Track Association Algorithm of Radar and Electronic Support Measurements Based on Track Vectors Hierarchical Clustering
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摘要: 针对同平台雷达与电子支援措施(ESM)存在系统误差、上报目标不完全一致等复杂场景下航迹关联鲁棒性和有效性问题，该文提出一种基于航迹矢量分级聚类的雷达与ESM航迹抗差关联算法。首先推导修正极坐标系(MPC)下目标等价测量方程，基于等价测量的近似展开得到目标状态估计分解方程，利用真实状态对消的方法得到航迹矢量，基于高斯随机矢量的统计特性，采用航迹矢量分级聚类的方法提取同源航迹。最后通过实验仿真验证，所提算法在不同系统误差、目标分布密度、检测概率等环境下具有较好的关联效果和鲁棒性。Abstract: To address track-to-track association problem of radar and Electronic Support Measurements (ESM) in the presence of sensor biases and different targets reported by different sensors, an anti-bias track-to-track association algorithm based on track vectors hierarchical clustering is proposed. Firstly, the equivalent measurement is derived in the Modified Polar Coordinates (MPC). Linear relationship between state estimates and real states, sensor biases, measurement errors are established based on the approximate expansion of the equivalent measurement. The track vectors are obtained by the real state cancellation method. The homologous tracks are extracted by the method of track vectors hierarchical clustering, according to the statistical characteristics of Gaussian random vectors. The effectiveness of the proposed algorithm is verified by Monte Carlo simulation experiments in the presence of sensor biases, targets densities and detection probabilities.
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