关键词: 组合导航定位, 约束优化, 自适应衰减记忆, 平方根滤波, 混合阶, 球面单纯形-径向容积卡尔曼滤波, 粒子滤波

Abstract:

To address the issues of particle degradation and the difficulty in selecting the importance density function in particle filtering， a Constrained Optimization-Based Adaptive Fading Memory Square Root Mixed-Degree Spherical Simplex-Radial Cubature Particle Filter （COAFM-MSSRCPF） algorithm was proposed. The advantages of constrained optimization， adaptive fading memory， square root filtering， and Mixed-Degree Spherical Simplex-Radial Cubature Kalman Filtering （MSSRCKF） are combined in this algorithm. By employing the Mixed-Degree Spherical Simplex-Radial sampling criterion， higher accuracy compared to traditional Cubature Kalman Filtering （CKF） and lower computational complexity than High-Degree Cubature Kalman Filtering （HCKF） are achieved. The adaptive fading memory square root strategy is utilized for predicting and updating the covariance matrix， with the weight of current measurement information being enhanced and the influence of historical data reduced. As a result， issues of covariance matrix asymmetry， negative definiteness， and filter divergence are avoided. The noise covariance matrix is dynamically adjusted， and the convergence speed and accuracy of state estimation are improved by constraining the ratio of error covariance to measurement noise covariance. Simulation results demonstrate that the COAFM-MSSRCPF algorithm effectively suppresses filter divergence in SINS/GNSS integrated navigation systems. Filtering accuracy and robustness are significantly improved compared to the Fading Memory Cubature Particle Filtering （FMCPF） and traditional Cubature Particle Filtering （CPF） algorithms.

Key words: integrated navigation positioning, constrained optimization, adaptive fading memory, square root filtering, mixed-degree, spherical simplex-radial cubature kalman filtering, particle filtering