自适应CS模型的强跟踪平方根容积卡尔曼滤波算法

对于目标跟踪过程中的强机动问题,基于当前统计(current statistical, CS)模型和改进的强跟踪平方根容积卡尔曼滤波器(square root cubature Kalman filter, SCKF),提出新的跟踪算法。在CS模型和改进输入估计算法的基础上,引入加加速度估计,使得状态过程噪声与状态协方差矩阵相联系,实现模型的自适应调整。从正交性原理出发,重新确定了渐消因子的引入位置,并提出了新的渐消因子计算形式,以克服传统渐消因子在雷达量测坐标系中的失效问题,从而构造强跟踪平方根容积卡尔曼滤波器。另外,构造强机动检测函数,利用SCKF的输出来调整自适应CS模型中的机动频率。仿真结果表明,相比基于CS模型的多重渐消因子强跟踪SCKF算法、改进CS模型的强跟踪SCKF(SCKF STF)算法和交互式多模型(interacting multiple model, IMM)SCKF算法,所提算法具有更佳的目标机动适应性和跟踪精度;相比于IMM SCKF算法,实时性有明显改善。

Strong tracking square root cubature Kalman filter overadaptive current statistical model

A tracking algorithm is proposed by the integration of the adaptive current statistical (CS) model and the modified square root cubature Kalman filter (SCKF) aiming at the maneuvering target tracking problem. Firstly, the Jerk input estimation is introduced based on the CS model and the modified input estimation (MIE) algorithm in order to make the connection of the state process noise and the state covariance matrix as well as to realize the adaptive adjustment of the CS model. Secondly, the introduced position of the fading factor is relocated from orthogonality principle and a calculation formula of the fading factor is put forward in order to overcome the invalidation problem of the traditional fading factor in radar measurement coordinate. Thereby, the strong tracking SCKF (ST SCKF) filter is structured. Additionally, the strong maneuvering detection function is established by utilizing the output of the ST SCKF to adjust the maneuvering frequency of the adaptive CS model. The simulation results show that the proposed algorithm shows better adaptability and tracking accuracy than the SCKF algorithm based on the CS model and the multiple fading factors, the SCKF STF algorithm based on the modified CS model as well as the interactive multiple model SCKF (IMM SCKF) algorithm. Moreover, the real time property is significantly improved compared with the IMM SCKF algorithm.