衰减因子自适应滤波及在组合导航中的应用

Adaptive fading Kalman filter with applications in integrated navigation system

从卡尔曼滤波技术的稳定性出发 ,分析了卡尔曼滤波算法发散的原因 ,提出了一组衰减记忆卡尔曼滤波中衰减因子的自适应估计方法 ,并在GPS SINS组合导航系统中进行了计算机仿真 .在计算衰减因子时 ,利用滤波残差序列在最优估计时为零均值白噪声的性质 ,构造了服从 χ2 分布的变量 ,并分别检验滤波残差每一个分量得出衰减因子值 .仿真结果表明 ,该组算法能够自适应地估计出衰减因子的大小 ,有效地抑制滤波发散 。

The reasons of the instability of Kalman filter were analyzed from the stability of Kalman filter. A group of adaptive estimation methods of Kalman filter fading factor were developed and simulated in GPS/SINS (global position system and strapdown inertial navigation system) integrated navigation system. The characteristic that the filter residuals were zero-mean Gaussian white noise vectors was used and a chi-square distribution variable was made while computing the fading factor. The fading factor was computed by testing each element of the filter residual vector. The results show that the proposed algorithms can estimate the fading factor adaptively, restrain filtering divergence and has the less computation burden than other algorithms.