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车载GNSS/SINS/里程计分布式弹性融合导航方法

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为提升复杂环境下低成本车载导航系统的容错性能,本文研究了基于次优增益融合(SGF)算法的GNSS/SINS/里程计分布式弹性融合方法.该方法首先根据阿克曼转向几何建立了四轮里程计测速补偿模型,提升了惯性测量单元(IMU)安装中心处的前向和侧向测速精度;然后设计了基于卡方检验统计量的故障检测与分类准则,充分利用了可获取的观测信息;最后构建了随机模型和信息分配因子(ISF)弹性优化模型,分别从传感器层和决策层减少了异常观测的影响,实现了车载多源信息的弹性融合.通过实际跑车数据对GNSS/SINS/里程计分布式弹性融合方法进行测试验证.试验结果表明,本文方法能有效减少子系统故障对全局状态估计的影响,提升复杂环境下系统的容错性能.此外,与经典的联邦卡尔曼滤波(FKF)算法相比,SGF算法全局融合精度损失有限,计算效率却显著提升,有利于多源信息弹性融合的实际工程应用.
A distributed GNSS/SINS/odometer resilient fusion navigation method for land vehicle
To improve the fault-tolerance of a low-cost land vehicle navigation system in the complex environment,this paper proposes a distributed GNSS/SINS/odometer resilient fusion method based on the suboptimal gain fusion algorithm.First,a velocity compensation model for each odometer on four wheels is established according to the Ackermann steering geometry,which improves the accuracy of forward and lateral velocity measurement at the inertial measurement unit center.Then,a fault detection and classification criteria based on Chi-square test statistics is designed to make full use of the available observation in-formation.Last,a resilient adjustment model for the stochastic model and information sharing factors(ISF)are proposed to mitigate the influence of abnormal observation from the sensor layer and the decision layer respectively and realize the resilient fusion of multi-source information.A real car test is carried out to verify the effectiveness of the distributed GNSS/SINS/o-dometer resilient fusion method.The experiment results demonstrate that the proposed method can effectively reduce the im-pact of subsystem faults on the global state estimation and improve the fault tolerance performance of the system in complex environments.Moreover,compared with the traditional federated Kalman filtering(FKF),the SGF algorithm can achieve the equivalent accuracy with significant computational efficiency improvement,which is conducive to the practical engineering ap-plication of multi-source information resilient fusion.

multi-information resilient fusionGNSS/SINS/odometer fusionodometer velocity compensation modelsubopti-mal gain fusiondistributed filter

穆梦雪、赵龙

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北京航空航天大学自动化科学与电气工程学院,北京 100191

北京航空航天大学数字导航中心,北京 100191

北京航空航天大学飞行器控制科学与技术实验室,北京 100191

多源信息弹性融合 GNSS/SINS/里程计融合 里程计测速补偿模型 次优增益融合 分布式滤波

国家自然科学基金航空科学基金国家重点研发计划

422740372022Z0220510012020YFB0505804

2024

10.11947/j.AGCS.2024.20220349

测绘学报
中国测绘学会

测绘学报

CSTPCD北大核心
影响因子：1.602
ISSN：1001-1595
年,卷(期)：2024.53(3)
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