基于雷达/红外测量的期望最大化容积卡尔曼滤波

Expectation maximization cubature Kalman filter based on radar/infrared measurement

为解决相应的目标跟踪问题,该文针对二维空间雷达/红外测量系统提出了一种期望最大化容积卡尔曼滤波(EMCKF)。在期望最大化框架下计算出量测噪声自适应因子,修正量测噪声协方差,以解决跟踪过程中时变量测噪声场景造成的量测模型失配的问题。仿真结果表明,与容积卡尔曼滤波(CKF)相比,在时变量测噪声环境下,该文EMCKF可以得到更准确的结果,并且该算法对量测信息丢失具有更好的鲁棒性。

An expectation maximization cubature Kalman filter(EMCKF)is proposed for two-dimensional radar/infrared measurement system to solve the corresponding target tracking problem.An adaptive factor for the measurement noise is given to modify the measurement noise covariance under the framework of expectation maximization for the problem of measurement model mismatch caused by the time-varying characteristics during the tracking process of the measurement noise.The simulation results show that under the time-varying measurement noise environment,the EMCKF can achieve better estimation accuracy than cubature Kalman filter(CKF),and the proposed filter has better robustness for the loss of measurement than CKF.