在进行多雷达多目标航迹匹配时,由于测元系统误差随跟踪距离传播,雷达测量精度下降,易出现航迹匹配错误的情况.针对该情况,应用多测元非线性融合模型,采用样条约束的误差模型最佳弹道估计(Error Model Best Estimation of Trajectory,EMBET)方法对雷达测元系统误差进行自校准,将利用欧几里得距离进行航迹匹配的传统方法改为利用精度较高的雷达测距作差比较,有效解决了多雷达航迹匹配时的门限阈值合理设置的难题.仿真结果证明算法有效适用,可极大地提高多雷达多 目标航迹匹配时的准确度,对完成多目标空域分布、目标识别等突防效果分析评估具有重要价值.
Multi-Rader and Multi-Target Track Matching Strategy
In the multi-radar and multi-target track matching,due to the sensor system error spreads with the tracking distance,the radar measurement accuracy decreases,and the track matching error is prone to occur.According to the situation,the multi-meter nonlinear fusion model is applied,the erro model best estimation of trajectory(EMBET)method with spline constraint is adopted to self calibrate the radar measurement element system error.The traditional track matching method using Euclidean distance for track motching is replaced by comparing the differences between rader elements with high accurcy,which effectively solves the problem of reasonable threshold setting of multi-radar track matching.The simulation results prove that the algorithm is ef-fectively applicable,which can greatly improve the accuracy of multi-radar and multi-target track matching,and it has great value for the analysis and evaluation of penetration effects such as multi-target airspace distribution and target recognition.
system errorself calibratietrack matchingspline functionEMBET
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