雷达组网中联合数据关联与偏差估计方法研究

针对雷达组网目标跟踪系统中,单雷达系统偏差严重影响多雷达航迹数据关联及融合跟踪质量的问题,提出了一种联合数据关联与系统偏差估计的方法。该方法利用对雷达系统偏差不敏感的新特征量———目标参照拓扑对多雷达航迹进行自适应的预关联,然后根据关联质量选择可靠的关联航迹对作为雷达系统偏差估计的先验信息,最后应用递归最小二乘算法进行偏差估计,估计结果可为预关联过程提供依据。在无需外界提供关联先验信息的情形下,该方法实现了对静态系统偏差的在线估计,从而可以进行及时的校准,保证了后续数据处理的有效性,具有很高的工程应用价值。仿真结果表明了该方法的有效性。

Research on joint data association and bias estimation method in radar networks

A joint data association and bias estimation method is proposed to handle the negative effect of individual radar bias to track association and fusion in radar networks for target tracking.The method utilizes a new feature,target reference topology,which is robust to radar biases to perform an adaptive pre-association.Then after selecting the reliable counterpart of tracks reported by diverse radars as the prior information,the bias estimation can be eventually made by a recursive least square estimator and the results are useful to adjust the pre-association process.Without the need of providing the extra association information,the online estimation of constant bias of radars is achieved.Therefore a timely correction to the bias of radars which are being enrolled in a network can be easily implemented.The great value of the method in real applications is obvious beacause the following data process can also be carried out effectively.Simulation result shows the effectiveness of the method.