具有未知参数的GPS/DR组合导航系统跟踪与辨识  

Tracking and Identification for GPS/DR Integrated Navigation System with Unknown Parameters

作　　者：李江[1] 钱富才[1,2] 刘丁[1] 胡绍林[1] 

机构地区：[1]西安理工大学自动化与信息工程学院,西安710048 [2]西安交通大学系统工程国家重点实验室,西安710054

出　　处：《电子与信息学报》2013年第4期921-926,共6页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(61273127;61074077);高等学校博士学科点专项科研基金(20116118110008)资助课题

摘　　要：该文针对GPS/DR(Global Positioning System/Dead-Reckoning)组合导航系统中存在参数不确定性问题,构建自组织状态空间模型,应用蒙特卡罗粒子滤波方法对新构建的系统模型进行滤波。并且对于在自组织模型中粒子滤波易使未知参数搜索陷入初始取样子集的问题,提出一种人工鱼群-粒子滤波算法。该算法不仅可以估计系统状态,而且还能使未知参数的取样分布向真实参数分布"移动",最终辨识出未知参数的真值。仿真结果表明该方法的有效性。This paper propses a filtering method for GPS/DR （Global Positioning System/Dead-Reckoning） integrated navigation system with unknown parameters. This method firstly structures a self-organizing state space model, and then estimates the state vector by using Monte Carlo filtering method for this new system model. Because particle filter is easy to make a search of the unknown parameters into a subset of the initial sampling for the self-organization model an artificial fish swarm-partical filter algorithm is put forward. The algorithm not only can estimate the system state, but also can make the sampling distribution of the unknown parameters move to the true parameter distribution. Ultimately, the true value of the unknown parameters are identified. The simuliation results show the effectiveness of the proposed method.

关 键 词：GPS DR组合导航系统 自组织状态空间模型 蒙特卡罗粒子滤波 人工鱼群算法 参数辨识 

分 类 号：TN966[电子电信—信号与信息处理]