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  

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

作　　者：王宁[1] 刘繁明 WANG Ning;LIU Fan-ming(College of Intelligent Systems Science and Engineering,Harbin Engineering University,Harbin 150001,China)

机构地区：[1]哈尔滨工程大学智能科学与工程学院,哈尔滨150001

出　　处：《吉林大学学报(工学版)》2024年第12期3660-3672,共13页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家自然科学基金项目(61633008)。

摘　　要：针对粒子滤波中粒子退化和重要性密度函数选择难题,本文提出一种基于约束优化的自适应衰减记忆平方根混合阶球面单纯形-径向容积粒子滤波算法。该算法结合约束优化、自适应衰减记忆、平方根滤波和混合阶球面单纯形-径向容积卡尔曼滤波的优势,通过混合阶球面单纯形-径向容积准则采样,算法在精度上优于传统的容积卡尔曼滤波,计算复杂度低于高阶容积卡尔曼滤波。自适应衰减记忆平方根策略用于预测和更新协方差矩阵,增强当前量测信息权重,减弱历史信息影响,避免协方差矩阵的不对称性、负定性和滤波发散问题。算法动态调整噪声协方差矩阵,并通过约束误差协方差与测量噪声协方差比值,提高状态估计收敛速度和精度。仿真结果表明,基于约束优化的自适应衰减记忆平方根混合阶球面单纯形-径向容积粒子滤波算法在SINS/GNSS组合导航系统中能有效抑制滤波发散,与衰减记忆容积粒子滤波和传统容积粒子滤波算法相比,显著提高滤波精度和鲁棒性。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.

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 

分 类 号：TP2[自动化与计算机技术—检测技术与自动化装置]