基于新息的神经网络自适应卡尔曼滤波

卡尔曼滤波是一种基于最小方差的递推式滤波算法,系统模型和噪声统计特性的先验知识决定了滤波的性能和估计的准确性,不精确的先验知识将导致滤波性能的明显下降甚至发散。采用BP神经网络对系统进行辨识,获得精确的系统状态方程,利用新息自适应估计卡尔曼滤波算法中的过程噪声和测量噪声协方差矩阵,提出基于新息的神经网络自适应卡尔曼滤波算法。Matlab仿真结果表明,与传统卡尔曼滤波算法相比,改进的卡尔曼滤波算法获得了与原始信号几乎一致的输出信号,噪声得到明显抑制。同时,改进的算法不需要系统精确的数学模型,在实际应用中具有可行性和普适性。

Innovation-Based Neural Network Adaptive Kalman Filter Algorithm

Kalman filter is a recursive algorithm based on minimum variance estimation, filtering performance and the estimated accuracy depend on the priori knowledge of system model and noise statistical properties, and imprecise priori knowledge can cause significant degradation even disperse in the filtering performance. BP neural network is used for system identification to acquire the precise system equation. The process noise and measurement noise covariance matrix in adaptive estimated Kalman filter algorithm is used to propose a new algorithm of innovation-based neural network adaptive Kalman filter. Matlab simulation results show: compared with the traditional Kalman filter algorithm, the output signal obtained through the improved Kalman filter algorithm is almost identical with the original signal, the noise is significantly suppressed, meanwhile the improved algorithm does not need accurate system mathematical model, which is effective and available in practical application.