基于星-凸形RHM的扩展目标跟踪算法
发布时间：2018-09-17 16:38
【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。
[Abstract]:Aiming at the problem of joint estimation of moving state and shape of extended target, a hybrid probability density filtering algorithm for extended target Gao Si based on star-convex random hypersurface model is proposed. The algorithm uses star-convex random hypersurface model to model the diffusivity of the measurement and uses constraints to limit the target shape parameters. In the framework of Gao Si's mixed probability assumption density, the extended target tracking is realized by solving and updating the parameters of measurement likelihood, innovation and other parameters under the measurement model. Simulation results show that the proposed algorithm not only ensures the effectiveness and feasibility of tracking, but also improves the estimation accuracy of the moving state and shape of the extended target.
【作者单位】： 河南工学院电子通信工程系;新乡学院计算机与信息工程学院;
【基金】：河南省高等学校重点科研项目(14A510025,17B510001)
【分类号】：TN713
，
本文编号：2246508
[Abstract]:Aiming at the problem of joint estimation of moving state and shape of extended target, a hybrid probability density filtering algorithm for extended target Gao Si based on star-convex random hypersurface model is proposed. The algorithm uses star-convex random hypersurface model to model the diffusivity of the measurement and uses constraints to limit the target shape parameters. In the framework of Gao Si's mixed probability assumption density, the extended target tracking is realized by solving and updating the parameters of measurement likelihood, innovation and other parameters under the measurement model. Simulation results show that the proposed algorithm not only ensures the effectiveness and feasibility of tracking, but also improves the estimation accuracy of the moving state and shape of the extended target.
【作者单位】： 河南工学院电子通信工程系;新乡学院计算机与信息工程学院;
【基金】：河南省高等学校重点科研项目(14A510025,17B510001)
【分类号】：TN713
，
本文编号：2246508
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