一种多模自反馈角被动跟踪算法

Multimodel self-feedback Bearings-only tracking algorithm based on data fusion

针对多模贝叶斯“滤波—辨识联合算法”(JEI)多次迭代后存在的滤波器发散问题,提出了一种基于数据融合的多模自反馈被动角跟踪算法(MSPT DF)。特点是利用数据融合理论中的自反馈方法,将基于最小方差准则的反馈机制引入到原有算法中,使得误差较小的“融合”滤波结果能够实时地反馈给子模型,帮助其修正滤波误差,并且根据各模型的方差大小调整模型权重;改进算法大大改善了原有算法稳定性差和跟踪精度不高的缺点,计算机仿真结果证明了文中结论。

A multimodel self-feedback bearings-only passive tracking filter based on data fusion theory is presented to solve the collapse problem of ＂Joint-Identification algorithm＂ （JEI） when it works iteratively many times. Its advantage is that it introduces self-feedback methods into JEI based on data fusion theory which feed the ＂fusion output＂ back to the sub-models to correct their filtering errors. The modified algorithm greatly improves the original algorithm＇s intrinsic drawbacks such as poor stability and low tracking accuracy. Simulation results prove this conclusion.