中文摘要：

为提高恶劣测量环境下单站红外搜索与跟踪系统的跟踪性能,提出了一种鲁棒的高斯和容积卡尔曼滤波算法.首先,为改善滤波初值模糊问题,在容积卡尔曼滤波框架下将滤波器分为若干不同初值的子滤波器,利用似然函数逐步减小初值偏差较大的子滤波器权值;其次构建非线性程度判别量,在高非线性情况下将预测密度沿最大特征向量方向进行分割,提高滤波精度;最后利用等价权函数改善新息协方差,减小异常误差对滤波准确性和稳定性造成的影响.实验结果表明,不存在异常误差时,所提算法跟踪结果优于传统算法;存在异常误差时,传统滤波方法的精度明显降低,而所提算法依然能够得到准确可靠的跟踪结果.

英文摘要：

In order to improve tracking performance of the single-observer infrared search and tracking（ IRST） system,an algorithm called the robust improved Gaussian-sum cubature Kalman filter（ RIGSCKF） was proposed. For the initial value fuzzy problem,the cubature Kalman filter framework was firstly divided into a set of weighted sub-filters,each with a different initial value,where the weights were determined by the likelihood function. In the measurement update,the predictive density was split in the direction of the maximum eigenvector and was merged according to sub-filters when nonlinear degree exceeded a threshold. It is certified that the method makes the tracking more accurate. Furthermore,to deal with the contaminated Gaussian noise in the measurements,the weights of outliers were reduced according to equivalent weight function which could improve the innovation covariance efficiently. Simulations showshat the RIGSCKF performs superior accuracy when there are no outliers.On the contrary,when outliers appear,the performance of conventional algorithms degrades rapidly,but that of the RIGSCKF is still accurate and robust.