针对无味卡尔曼滤波器(UKF)存在的缺陷,提出一种能对多通道数据进行渐消的带多重次优渐消因子的UKF滤波算法(SMFUKF)。该方法基于强跟踪滤波器的概念,通过引入多重次优渐消因子到UKF滤波器,自适应的在线调整UKF滤波器的状态预测误差协方差矩阵、量测预测协方差阵、状态和量测之间的互协方差阵及相应的增益矩阵,从而达到对快速变化的状态进行强有力的跟踪。实验结果表明多重次优渐消因子的引人使得UKF滤波器有可能更多的利用系统的先验知识,SMFUKF滤波器对快速变化的状态将具有更强的跟踪能力。

Aimming at limitation of unscented Kalman filter (UKF), a suboptimal multiple fading unscented kalman filter (SMFUKF) that can perform fading on data of Stamultiple channels is proposed. The algorithm is based on the concept of strong tracking filter. By introducing suboptimal multiple fading into UKF filtering, and adaptively perform real-time adjustment on the error covariance of the predicted state, the error covariance of the predicted measurement, the associate covariance of the predicted state and measurement, and its gain matrix, strong tracking of fast changing targets can be realized. Experimental results show that SMFUKF can make better use of prior information and it has stronger tracking ability for highly maneuvering targets.