顾及时变非高斯噪声的高斯和滤波及其导航应用

Gaussian Sum Filter Considering Time-Varying Non-GaussianNoise and Its Application in Navigation

高斯和滤波可利用高斯混合模型精化非高斯噪声随机模型来提高估计精度,但导航测量环境的动态性和复杂性使非高斯噪声具有时变性特征,若GMM不随之调整会导致滤波解算失真。针对该问题,本文提出一种基于位移参数自适应估计的高斯和滤波算法。首先分析GMM位移参数对非高斯噪声拟合精度的影响,然后利用位移参数自适应技术修正GMM,进而改善高斯和滤波性能。实验结果表明,当GNSS/SINS量测模型存在时变非高斯噪声时,本文算法的滤波结果较传统高斯和滤波算法的波动小,抗干扰能力强,在实际应用中可进一步改善估计精度和稳定性。

The Gaussian sum filter refines the non-Gaussian noise stochastic model by Gaussian mixture model(GMM)to improve estimation accuracy.However,the dynamic and complex of the navigation measurement environment brings time-varying characteristics to non-Gaussian noise,resulting in distortion in the filter solution.To solve this problem,we propose a Gaussian sum extended Kalman filter(GSEKF)algorithm,which is improved by adaptive estimation of displacement parameters.We discuss the influence of GMM displacement parameters on the fitting accuracy of non-Gaussian noise,and GMM is refined by displacement parameter adaptive method.These improvements make the GSEKF algorithm more stable.The experimental results show that the proposed algorithm has less fluctuation and stronger anti-interference ability compared to the traditional Gaussian sum filtering algorithm when there is time-varying non-Gaussian noise in GNSS/SINS measurement model,which can further improve the estimation accuracy and stability in practical application.