引入STF算法的自适应SICKF及其在目标跟踪中的应用

Adaptive SICKF with STF and Its Application to Target Tracking

针对容积卡尔曼滤波在系统状态突变时滤波精度下降的问题,结合均方根嵌入式容积卡尔曼滤波(SICKF)和强跟踪滤波(STF)算法,提出了一种自适应均方根嵌入式容积卡尔曼滤波(ASICKF)方法。采用嵌入式容积准则和均方根滤波方法,以提高算法的滤波精度和稳定性。引入强跟踪滤波,利用渐消因子在线修正预测误差协方差阵,强迫残差序列正交,以增强算法应对系统状态突变等不确定因素的能力。为了解决状态突变未知的目标跟踪问题,采用自适应均方根嵌入式容积卡尔曼滤波算法进行数值仿真,仿真结果表明,ASICKF在系统状态突变时能保证较高的滤波精度,具有较强的鲁棒性和系统自适应能力。

In order to overcome the problem that cubature kalman filter decreases in accuracy when system states suddenly change,combined with the square-root imbedded cubature kalman filter(SICKF) and the strong tracking filter(STF) algorithm,an adaptive square-root imbedded cubature kalman filter(ASICKF) is established. The imbedded family cubature formulae and the method of square-root filter are used to improve the accuracy and the stability of filtering algorithm. Furthermore,strong tracking filter algorithm is introduced to improve the capability of the filter to deal with uncertainty factors by modifying the predicted states’ error covariance with a fading factor and the residual sequence is forced to be orthogonal. A maneuvering target tracking problem with unknown sudden states changes in system states is used to test the performance of ASICKF,the simulation results indicate that ASICKF can achieve better filtering performance when states’ changes suddenly occur,with great robustness and better system adaptive capacity.