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首页> 外文会议>2013 IEEE 11th International Conference on Dependable, Autonomic and Secure Computing >A New Gaussian-Like Density Model and Its Application to Object-Tracking
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A New Gaussian-Like Density Model and Its Application to Object-Tracking

机译：高斯样密度新模型及其在目标跟踪中的应用

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摘要

Probability density function (PDF) plays a vital role in many applications involving stochastic process. A good approximation for real-time PDF conditioned on certain performance criterion could help to acquire unknown information about the system. With the help of this kind of information, which was not available earlier, many features of various models that describe the real system can be estimated effectively, especially for non-linear non-Gaussian stochastic system. In this paper, we elucidate some PDFs with only one parameter that have a definite physical meaning based on Tsallis entropy. The PDFs that we calculated here are all Gaussian-like, and Gaussian distribution is attained when the parameter of Tsallis entropy approaches zero. Based on these explicit form of Gaussian-like PDFs we calculated here, an extension of Gaussian particle filter (GPF) called Gaussian-like particle filter (GLPF) is proposed and the simulation results show that the GLPF is a more effective way to estimate the state of non-linear stochastic system compared with the GPF.
机译：概率密度函数（PDF）在涉及随机过程的许多应用中起着至关重要的作用。基于某些性能标准的实时PDF的良好近似值可以帮助获取有关系统的未知信息。借助以前无法获得的此类信息，可以有效地估计描述真实系统的各种模型的许多功能，尤其是对于非线性非高斯随机系统。在本文中，我们基于Tsallis熵阐明了仅具有一个具有一定物理意义的一个参数的PDF。我们在这里计算的PDF都是高斯型的，当Tsallis熵的参数接近零时，便获得了高斯分布。根据我们在此计算出的类似高斯型PDF的显式形式，提出了一种称为高斯型粒子滤波器（GLPF）的高斯粒子滤波器（GPF）的扩展，仿真结果表明GLPF是一种更有效的估算与GPF相比，非线性随机系统的状态

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