基于增广粒子滤波算法的毫米波雷达/GPS/INS组合导航方法研究  

Millimeter-Wave Radar/GPS/INS Integrated Navigation System and Its Application in UAV Autonomous Landing

作　　者：赵薇[1,2] 王楠[3] 高显忠[4] 

机构地区：[1]湖南警察学院信息技术(网监)系,长沙410073 [2]网络犯罪侦查湖南省普通高等学校重点实验室,长沙410073 [3]国防科学技术大学机电工程与自动化学院,长沙410073 [4]国防科学技术大学航天科学与工程学院,长沙410073

出　　处：《电光与控制》2016年第6期54-59,共6页Electronics Optics & Control

基　　金：湖南省科学计划支撑项目(2014GK3041)

摘　　要：为实现无人机精确自主着舰,需要精确测量无人机相对舰面高度,确定着舰位置。传统的GPS/INS组合导航系统在离舰高度30 m以下测量精度较差,并且难以对着陆区域定位,无法满足要求。为此,提出采用毫米波雷达/GPS/INS多传感器组合导航方案。为解决毫米波雷达与GPS/INS融合过程中对状态量和未知参数的估计问题,提出与增广卡尔曼滤波算法相对应的增广粒子滤波算法。该算法将动态系统中的未知参数作为状态变量对系统状态方程进行增广,采用高斯随机游走模型对未知参数建模,进而利用粒子滤波算法对增广的非线性动态系统进行状态估计,求出系统的未知参数。通过仿真测试验证了所设计算法的有效性以及该组合导航系统应用于无人机自主着舰的可行性。In order to land on aircraft carrier autonomously,the altitude of UAV relative to the carrier must be measured precisely to determine the landing position.Traditional GPS / INS integrated navigation system can not satisfy the requirement since it has low precision on estimating the height of UAV when it is below30 m,and is not capable to locate the target area.Therefore,we proposed a new integrated navigation method combining Millimeter Wave Radar（MWR） with GPS / INS.Aiming to solve the problem of unknown parameter estimation in MWR / GPS / INS integrated navigation system,Extend Particle Filtering（EPF） algorithm was proposed,which was parallel with Extend Kalman Filtering（EKF） algorithm.EPF algorithm modeled unknown parameters by Gaussian random walk process,regarded unknown parameters in dynamic mechanical system as a part of state variations,and then estimated the state variations in extend nonlinear dynamic mechanical system by particle filtering algorithm.Simulation verified the effectiveness of the proposed algorithm and the feasibility of using the integrated navigation system in UAV autonomous landing.

关 键 词：无人机自主着陆 毫米波雷达 组合导航 粒子滤波算法 参数估计 

分 类 号：TN965[电子电信—信号与信息处理]