基于约束优化的自适应衰减记忆平方根混合阶容积粒子滤波在惯性/卫星组合导航中的应用

doi:10.13229/j.cnki.jdxbgxb.20230847

Abstract

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

CLC Number:

Ning WANG,Fan-ming LIU. Application of constrained optimization⁃based adaptive fading memory square root mixed⁃order cubature particle filtering in SINS/GNSS integrated navigation[J].Journal of Jilin University(Engineering and Technology Edition), 2024, 54(12): 3660-3672.

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算法（n=17）	采样点数	平均仿真时间/s
CKF	2n	77.36
HCKF	2n²+1	992.15
MSSRCKF	2n+3	79.68

滤波算法	仿真时间/s	最大纬度误差/m	最大经度误差/m	最大高度误差/m
COAFM-MSSRCPF	600~1 200	2.50	2.46	3.20
FMCPF	600~1 200	4.60	5.10	6.00
CPF	600~1 200	7.20	8.00	9.60

Application of constrained optimization⁃based adaptive fading memory square root mixed⁃order cubature particle filtering in SINS/GNSS integrated navigation

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