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在多传感器数据融合系统中,航迹关联与系统误差估计之间存在着紧密的耦合关系。传感器系统误差容易诱发航迹关联出错;而系统误差的可靠估计又依赖于正确的关联结果。传统算法多忽视模块间耦合关系,对航迹关联与系统误差估计进行独立研究,在实际应用中性能退化严重。该文提出了一种基于稳健交替迭代的联合航迹关联与系统误差估计方法。该方法将错误关联视为系统误差估计的野值,在航迹关联与系统误差估计交替迭代过程中,使用最小平方中值(LMedS)估计器完成系统误差的稳健估计。仿真结果表明该方法在估计性能上具有明显优势。
In the multi-sensor data fusion system, there is a close coupling relationship between trajectory correlation and system error estimation. Sensor system errors easily lead to errors in track correlation; and reliable estimation of system errors depends on correct correlation results. Traditional algorithms mostly neglect the coupling relations between modules, and independently study the correlation of track and system error. In practice, the performance degrades seriously. This paper presents a joint trajectory association and systematic error estimation method based on robust alternate iteration. The method considers the error association as the outlier of system error estimation. During the iterative process of track association and system error estimation, the LMEDS estimator is used to complete the robust estimation of system error. Simulation results show that this method has obvious advantages in estimating performance.