基于星-凸形RHM的扩展目标跟踪算法

摘要

针对扩展目标联合估计运动状态和目标外形的问题,提出了一种基于星-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-凸形随机超曲面模型对量测的扩散程度进行建模,同时利用约束对目标外形参数进行限制.在高斯混合概率假设密度的框架下,通过对量测模型下的量测似然、新息等参数的求解和更新递推实现扩展目标的跟踪.仿真实验表明,所提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.%To the issue of joint estimation of the extended target's shape and kinematic state,a Gaussian mixture PHD filter based on star-convex Random Hypersurface Models (RHM) is proposed for extended target tracking.The proposed algorithm describes the extension of measurements by the star-convex RHM and uses the sampling constraint to limit the shape parameters of targets.Then,under the Gaussian mixture probability hypothesis density framework,the extended targets are tracked by calculating and updating the likelihood and new information.Simulation results show that the proposed method can guarantee the availability and feasibility of the tracking and improve the accuracy of extended target kinematic state and shape estimation.