基于星-凸形随机超曲面模型的扩展目标GM-PHD滤波器  

A Gaussian Mixture PHD Filter for Extended Target Based on Star-convex Random Hypersurface Model

作　　者：魏帅[1] 冯新喜[1] 王泉[1] 

机构地区：[1]空军工程大学信息与导航学院,西安710077

出　　处：《弹箭与制导学报》2017年第1期147-152,共6页Journal of Projectiles,Rockets,Missiles and Guidance

基　　金：国家自然科学基金(61571458);陕西省自然科学基金(2011JM8023)资助

摘　　要：针对扩展目标联合估计运动状态和目标外形的问题,提出一种基于星-凸形随机超曲面模型的扩展目标高斯混合概率密度滤波算法。该算法利用星-凸随机超曲面模型对量测的扩散程度进行建模,在高斯混合概率假设密度的框架下,通过求解、更新递推量测模型下的量测似然、新息等参数来实现对扩展目标的跟踪。仿真实验表明,该算法在保证跟踪有效性和可行性的同时,提高了对扩展目标运动状态和目标外形的估计精度。A Gaussian mixture PHD filter for extended target tracking based on star-convex random hypersurface model was proposed for the problem of joint estimation of the extended target shape and motion state. The proposed algorithm modelled the diffusion degree of measuration by using the star-convex random hypersurface model. Then, the extended targets were tracked by calculating and updating the measurement likelihood and innovation under the Gaussian mixture probability hypothesis density framework. The simulation results showed that the proposed method could guarantee the tracking availability and feasibility and improve the estimated accuracy of extended target motion state as well as the target shape.

关 键 词：星-凸形 随机超曲面模型 扩展目标 高斯混合概率密度 

分 类 号：TN953[电子电信—信号与信息处理]