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particle filters
相关语句
  粒子滤波器
    New Image Mosaic Method Based on Fast Robust Correlation and Particle Filters
    基于快速鲁棒相关和粒子滤波器的图像拼接方法
短句来源
    Object Tracking Using Robust Appearance Model with Particle Filters
    基于鲁棒外观滤波与粒子滤波器的图像跟踪算法
  粒子滤波
    INFRARED OBJECT TRACKING BASED ON PARTICLE FILTERS
    基于粒子滤波的红外目标跟踪
短句来源
  相似匹配句对
    INFRARED OBJECT TRACKING BASED ON PARTICLE FILTERS
    基于粒子滤波的红外目标跟踪
短句来源
    Object Tracking Using Robust Appearance Model with Particle Filters
    基于鲁棒外观滤波与粒子滤波器的图像跟踪算法
    ON DDA TRANSVERSAL FILTERS
    DDA横向滤波器的研究
短句来源
    CAD of the Highpass and Lowpass Filters
    高低通滤波器的机助设计
短句来源
    Improved Particle Filter for Target Tracking
    一个用于目标跟踪的改进粒子滤波算法(英文)
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  particle filters
The aim of this paper is to propose a unified tracking framework using particle filters to efficiently switch between visual tracking (field of view tracking) and track prediction (non-overlapping region tracking).
      
However, particle filters are not used in practice for these applications mainly because they cannot satisfy real-time requirements.
      
There are many applications in which particle filters outperform traditional signal processing algorithms.
      
Design and Implementation of Flexible Resampling Mechanism for High-Speed Parallel Particle Filters
      
The recent development of Sequential Monte Carlo methods (also called particle filters) has enabled the definition of efficient algorithms for tracking applications in image sequences.
      
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For large errors introduced by nonlinear state-space model in passive locating and tracking problems,various suboptimal recursive filtering algorithms are aralyzed and summarized, such as the extended Kalman filtering(EKF),the modified gain extended Kalman filtering(MGEKF),the second order filtering and the adaptive extended Kalman filtering(AEKF). On this basis a nonlinear filtering technique of sequential Monte Carlo particle filter based on Bayesian approach is emphatically...

For large errors introduced by nonlinear state-space model in passive locating and tracking problems,various suboptimal recursive filtering algorithms are aralyzed and summarized, such as the extended Kalman filtering(EKF),the modified gain extended Kalman filtering(MGEKF),the second order filtering and the adaptive extended Kalman filtering(AEKF). On this basis a nonlinear filtering technique of sequential Monte Carlo particle filter based on Bayesian approach is emphatically disussed which the posterior distribution of the state variables can be represented by a set of weighted particles, so the method hase advantages over the above algorithms in robustness and accuracy for nonlinear non-Gaussian filtering problems.

针对被动定位跟踪中状态空间模型非线性程度较高所引发的滤波精度偏低的问题,分析和总结了已有的包括推广卡尔曼滤波(EKF)、修正增益的推广卡曼滤波(MGEKF)、二阶滤波、自适应推广卡尔曼滤波(AEKF)等各种次优递推滤波算法的特点。在此基础上重点论述了一种基于贝叶斯原理的序贯蒙特卡罗粒子滤波技术,该方法通过粒子的加权和表征后验概率密度,获得状态估值,在处理非线性非高斯系统的状态估计问题时精度逼近最优,鲁棒性更好。

By exploring the time-varying autoregressive models and its stochastically evolving parameters, and introducing reflection coefficient to ensure model strict stability, we present a time-varying autoregressive model based speech enhancement algorithm. The algorithm relies on a particle filter estimation of the TVAR model parameters. The experimental results show that after filtered by our algorithm the signal-to-noise ratio and the quality of speech have been improved obviously.

在分析语音信号的时变自回归TVAR(Time VaryingAutoregressive)模型及其模型参数的随机演化模型的基础上,基于粒子滤波器(ParticleFilter)对TVAR模型参数的序列估计,提出了一种语音增强算法.算法通过引入反射系数,快速简捷实现了模型稳定性的判断,保证了跟踪的模型的稳定性.实验结果表明,算法可以很好地跟踪非平稳信号,采用该方法处理过的语音,信噪比SNR(Signal to NoiseRatio)明显提高,听觉质量得到了很大的改善.

Particle Filtering is used in GPS/DR integrated navigation system. If GPS is disturbed or the vehicle moves quickly, the errors of KF (Kalman Filter) are quite big. Particle Filtering takes account of not only impersonal sample information but also subjective information. It deals well with the abnormity and is robust. The results show that PF is better than KF while the GPS is invalid.

成功地将粒子滤波方法应用于GPS/DR组合导航系统中。如果GPS信号受到干扰或者车辆做大幅度机动时,卡尔曼滤波会有较大的误差。粒子滤波不仅考虑了客观样本信息,还考虑了主观因素,能很好地处理这种观测样本出现异常的情况,具有鲁棒性。实验证明,当GPS信号被遮挡时,粒子滤波优于卡尔曼滤波。

 
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