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基于卡尔曼粒子滤波的目标跟踪算法 总被引:1,自引:0,他引:1
目标跟踪在计算机视觉领域有着重要的应用。文中在对运动目标跟踪算法进行研究之后,应用卡尔曼粒子滤波算法进行运动目标的跟踪,同时利用Matlab 对卡尔曼滤波算法、粒子滤波算法及卡尔曼粒子滤波算法进行了实验仿真。实验结果表明,运用卡尔曼粒子滤波算法能够更快、更准确地对运动目标进行跟踪,可将其广泛应用于目标跟踪中。 相似文献
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为解决粒子滤波中的粒子退化和枯竭问题,提出一种动态人工鱼群粒子滤波算法,该算法在粒子滤波重采样过程中引入人工鱼群算法的觅食和聚群行为,并依据概率密度的动态比值动态调整人工鱼的移动步长,此算法提升了粒子的多样性,克服了粒子退化及枯竭问题;推动粒子向优选区域逼近,并提高了粒子的全局搜索能力,避免粒子陷入局部最优。将改进的动态人工鱼群粒子滤波在北斗/INS紧组合的模型上进行应用,并通过仿真与人工鱼群粒子滤波及标准粒子滤波算法PF相比较。仿真结果表明,动态人工鱼群粒子滤波可显著提高估算精度,从而为在利用北斗和INS在紧组合导航时提供了新的方法。 相似文献
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针对粒子滤波中重采样过程与优化提议分布的处理方式导致的粒子溃退和算法实时性下降问题,通过广义UT变换原理和卡尔曼滤波预测更新机制的引入,实现当前量测信息对于状态估计结果的直接优化,给出了一种基于广义UT变换的粒子滤波算法。另外,将改进后算法与交互式多模型相结合,进而提出了一种基于广义UT变换的交互式多模型粒子滤波算法.理论分析和仿真结果表明:新算法在计算复杂度方面与标准粒子滤波相近,在滤波精度方面优于标准粒子滤波及其改进算法. 相似文献
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In this paper, we propose novel resampling algorithms with architectures for efficient distributed implementation of particle filters. The proposed algorithms improve the scalability of the filter architectures affected by the resampling process. Problems in the particle filter implementation due to resampling are described, and appropriate modifications of the resampling algorithms are proposed so that distributed implementations are developed and studied. Distributed resampling algorithms with proportional allocation (RPA) and nonproportional allocation (RNA) of particles are considered. The components of the filter architectures are the processing elements (PEs), a central unit (CU), and an interconnection network. One of the main advantages of the new resampling algorithms is that communication through the interconnection network is reduced and made deterministic, which results in simpler network structure and increased sampling frequency. Particle filter performances are estimated for the bearings-only tracking applications. In the architectural part of the analysis, the area and speed of the particle filter implementation are estimated for a different number of particles and a different level of parallelism with field programmable gate array (FPGA) implementation. In this paper, only sampling importance resampling (SIR) particle filters are considered, but the analysis can be extended to any particle filters with resampling. 相似文献
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基于粒子滤波的检测前跟踪方法是检测和估计非线性调频信号的有效方法之一。但此类方法运算量大,难以并行执行。此外,由于粒子滤波算法收敛较慢,基于粒子滤波的检测前跟踪方法的检测和状态估计能力有待提高。针对上述问题,该文首先提出一种代价参考粒子滤波器组。该滤波器组收敛快速,具有完全的并行结构,可快速准确地估计非线性调频信号的瞬时频率。其次,提出基于代价参考滤波器组的检测前跟踪算法,可在给定虚警率下,在各个时刻检测目标和估计目标状态。两类非线性调频信号检测和估计的仿真结果表明,基于代价参考粒子滤波器组的检测前跟踪算法的检测性能、估计性能和运行速率均优于类似的方法,如基于粒子滤波的检测前跟踪方法,基于Rutten粒子滤波的检测前跟踪方法等。 相似文献
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Gaussian sum particle filtering 总被引:17,自引:0,他引:17
We use the Gaussian particle filter to build several types of Gaussian sum particle filters. These filters approximate the filtering and predictive distributions by weighted Gaussian mixtures and are basically banks of Gaussian particle filters. Then, we extend the use of Gaussian particle filters and Gaussian sum particle filters to dynamic state space (DSS) models with non-Gaussian noise. With non-Gaussian noise approximated by Gaussian mixtures, the non-Gaussian noise models are approximated by banks of Gaussian noise models, and Gaussian mixture filters are developed using algorithms developed for Gaussian noise DSS models. As a result, problems involving heavy-tailed densities can be conveniently addressed. Simulations are presented to exhibit the application of the framework developed herein, and the performance of the algorithms is examined. 相似文献
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弱小目标检测与跟踪是一个具有挑战性的研究课题,传统的数据处理方法主要是基于单传感器的备类检测前跟踪算法,其中由粒子滤波实现的算法因其稳健性高、适用范围广,受到了越来越多的关注。本文正是试图将该类算法推广应用于多传感器的目标检测与跟踪,以提高系统对弱小目标的检测概率与跟踪精度。仿真试验验证了所提方法的有效性。 相似文献
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非线性滤波算法性能对比 总被引:1,自引:0,他引:1
随着目标运动的多样性和复杂化,雷达非线性目标跟踪算法越来越受到重视。本文对目前非线性滤波的主要算法即扩展卡尔曼滤波、不敏卡尔曼滤波、粒子滤波的滤波模型、适用条件、性能进行了分析比较,通过一个非线性非高斯模型进行了仿真,验证了这些算法的性能,仿真结果表明非线性条件下粒子滤波算法要明显优于其它两种滤波算法。 相似文献
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智能粒子滤波通过借鉴遗传算法思想能够减轻粒子退化现象。在基于遗传算法的智能粒子滤波基础上,该文提出对低权值粒子的改进的智能粒子滤波(IIPF)处理策略。在对粒子进行分离、交叉后,优化遗传算子,对低权值粒子进行自适应处理。低权值粒子根据权值大小自行判断是否为底层粒子;底层粒子将直接进行变异,其余低权值粒子将根据变异概率随机变异。仿真结果表明,改进的智能粒子滤波(IIPF)性能优于智能粒子滤波、一般粒子滤波算法和拓展卡尔曼滤波。在1维仿真实验中,改进的智能粒子滤波误差较一般粒子滤波算法和智能粒子滤波分别降低了10.5%和8.5%,且具有更好的收敛性;在多维仿真实验中,改进的智能粒子滤波较智能粒子滤波在高度均方根误差和平均误差上分别降低了8.5%和7.5%,在速度均方根误差和平均误差上分别降低了11.5%和7.6%;在乘性噪声和非高斯随机噪声中,改进的智能粒子滤波依旧有10%以上的性能优势。 相似文献
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视频跟踪技术是智能视频分析中的一个基础研究方向,现已有一些实用的算法,现重点研究了粒子滤波算法.通过改进,解决了多目标间互相干扰的问题.测试结果表明:在目标被部分甚至完全遮挡时,仍然能够给出正确的结果. 相似文献
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基于正则化观测矢量的H无穷粒子滤波红外目标跟踪方法 总被引:1,自引:1,他引:1
提出了一种新颖和鲁棒的红外图像序列中的目标跟踪方法。由于H无穷滤波器在系统噪声源不能确定或是未知的情况下具有较好的预测性能,所以以其估计得到的预测信息来分配粒子滤波算法的粒子。为解决粒子滤波的“采样枯竭”问题,正则化了H无穷粒子滤波器的观测矢量。同时,通过计算每个目标的亮度和局部标准差分布构成级联核的目标模型,以用于计算粒子集中各个粒子的加权值。对于目标的尺寸和表观信息变化的情况,以目标区域像素灰度值零阶矩的函数来调整跟踪窗口的大小,模型更新则通过更新目标模型的每个量化阶来实现。实验结果证明了所提出的红外图像目标跟踪方法是有效的,并且优于所比较的算法。 相似文献
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Particle filters for positioning, navigation, and tracking 总被引:21,自引:0,他引:21
Gustafsson F. Gunnarsson F. Bergman N. Forssell U. Jansson J. Karlsson R. Nordlund P.-J. 《Signal Processing, IEEE Transactions on》2002,50(2):425-437
A framework for positioning, navigation, and tracking problems using particle filters (sequential Monte Carlo methods) is developed. It consists of a class of motion models and a general nonlinear measurement equation in position. A general algorithm is presented, which is parsimonious with the particle dimension. It is based on marginalization, enabling a Kalman filter to estimate all position derivatives, and the particle filter becomes low dimensional. This is of utmost importance for high-performance real-time applications. Automotive and airborne applications illustrate numerically the advantage over classical Kalman filter-based algorithms. Here, the use of nonlinear models and non-Gaussian noise is the main explanation for the improvement in accuracy. More specifically, we describe how the technique of map matching is used to match an aircraft's elevation profile to a digital elevation map and a car's horizontal driven path to a street map. In both cases, real-time implementations are available, and tests have shown that the accuracy in both cases is comparable with satellite navigation (as GPS) but with higher integrity. Based on simulations, we also argue how the particle filter can be used for positioning based on cellular phone measurements, for integrated navigation in aircraft, and for target tracking in aircraft and cars. Finally, the particle filter enables a promising solution to the combined task of navigation and tracking, with possible application to airborne hunting and collision avoidance systems in cars 相似文献