基于采样卡尔曼滤波的水下被动目标跟踪

Unscented Kalman Filter for Passive Underwater Target Tracking

针对水下被动目标跟踪问题中,采用直角坐标系容易出现滤波发散,而修正极坐标系下过程模型强非线性的问题,研究了一种修正极坐标系下的采样卡尔曼滤波算法。采样卡尔曼滤波比传统的扩展卡尔曼滤波更好地逼近状态方程和测量方程的非线性特性,给出更精确的均值和协方差的估计,并且适用于过程噪声与状态估计非线性耦合的情况。在修正极坐标系下,采用3种滤波方法求解被动目标跟踪问题,仿真结果表明,采样卡尔曼滤波的滤波精度优于传统的扩展卡尔曼滤波方法和自适应扩展卡尔曼滤波方法。

To solve the problem of the instability, low accuracy of passive filter in Cartesian coordinate, and the strong nonlinearity in modified polar coordinate, the unscented Kalman filter is applied in the passive underwater target tracking in modified polar coordinate. This approach combines the advantages of modified polar coordinate, which is stable and asymptotically unbiased, and unscented Kalman filter, which can deal with the problem of coupled state estimates and noises in nonlinear process model. In simulation, we compare the performance of three different methods solving the passive tracking in modified polar coordinate. And the results indicate that the unscented Kalman filter is superior to both extended Kalman filter and adaptive extended Kalman filter.