基于预测残差的自适应Unscented粒子滤波

Adaptive Unscented Particle Filter Based on Predicted Residual

针对经典粒子滤波的样本退化问题,提出了基于预测残差的自适应Unscented粒子滤波。该方法将自适应因子与无迹卡尔曼滤波相结合构造粒子滤波的建议分布,即通过UT变化构造建议分布函数,利用预测残差作为自适应因子,在线调整测量预测协方差、状态测量互协方差和状态更新协方差的实时变化,从而调整其在滤波中的作用。非线性状态估计仿真实验表明,该算法具有较强的自适应性和较高的滤波精度。

Considering particle degradation in the traditional particle filter algorithm, an adaptive unscented particle filter algorithm based on predicted residual is proposed. The algorithm adopts a new proposal distribution combing the unscented kalman filter with the adaptive factor. The algorithm uses Unscented Kalman filter to generate a proposal distribution, in which the covariance of the predicted measurement, the cross-covariance of the state and measurement and the covariance of the state update are online adjusted by predicted residual as adaptive factor. Simulation experiments results of nonlinear state estimation demonstrate that the adaptive unscented particle filter is more adaptive and accuracy is also improved.