一种基于M估计的抗差自适应多模型组合导航算法

Robust adaptive multiple model integrated navigation algorithm based on M-estimation

针对复杂环境下因量测噪声统计特性时变及量测粗差而引起的组合导航精度下降的问题,提出了一种基于M估计的抗差自适应多模型组合导航算法。所提算法突破了传统交互式多模型算法定结构的限制,凭借所提出的模型集自适应调整策略,能够快速估计量测噪声统计特性,并利用模型概率信息对模型转移概率矩阵进行实时修正;引入了基于M估计的抗差Kalman滤波算法,以提高滤波抗差能力。以SINS/DVL组合导航系统为例,通过仿真和长江试验对所提算法进行了验证,结果表明所提算法有效降低了量测噪声统计特性时变及量测粗差对滤波精度的影响。在长江试验中,所提算法相比AIMM算法,东、北向速度误差和位置误差的均方根误差分别下降了44%、36%和41%、53%,水平定位精度提升了约45.9%,定位精度提升显著。

A robust adaptive multiple model integrated navigation algorithm based on M-estimation is proposed to solve the problem of integrated navigation accuracy reduction caused by measurement noise change and gross error in complex environment.The proposed algorithm breaks through the limitation of the traditional interactive multi model algorithm.The proposed adaptive adjustment strategy of model set is used to quickly estimate the statistical characteristics of measurement noise,and the model transition probability matrix is modified in real time by using the model probability information.The robust Kalman filtering algorithm based on M-estimation is used to improve the filtering robustness.Taking SINS/DVL integrated navigation system as an example,the algorithm is verified by simulation and the Yangtze river experiment.The results show that the proposed algorithm can effectively reduce the influence of measurement noise change and measurement gross error on filtering accuracy.In the Yangtze river experiment,compared with AIMM algorithm,the proposed algorithm has reduced the root mean square error of the east and north direction velocity error and position error by 44%,36% and 41%,53%respectively,and the horizontal positioning accuracy has increased by about 45.9%.The accuracy has improved significantly.