基于序列Monte Carlo方法的非线性滤波技术研究
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摘要
现实世界中的随机动态系统大都是非线性非高斯的,因此非线性滤波问题是极为普遍的,许多领域都涉及到,目标跟踪是其重要应用之一。目标跟踪是基于具有不确定性测量的传感器数据,对来自未知目标的运动学特征做出估计。近年来粒子滤波,也称序列Monte Carlo方法,在目标跟踪方面得到广泛关注,其原因可以理解为这种方法的简洁性、处理复杂情况的鲁棒性及易操作性,更重要的是它对非线性非高斯估计问题的强大处理能力。这种非线性滤波技术是一种基于仿真的数值方法,其利用离散隐马尔可夫链建模,通过系统模型描述目标未知状态随时间的演化规律,通过测量模型把可利用的目标观测值与状态联系起来,并鉴于过去和目前的测量值,在先验信息已知的基础上,进行预测和更新,提供一个目标状态的近似分布。
本文对基于贝叶斯框架下的序列Monte Carlo方法及目标跟踪原理进行了综述,对粒子滤波的改进方法进行了全面的概括,分析了粒子滤波方法的收敛性,其收敛特性保证了收敛率独立于状态空间的维数,而且是Lp收敛的。通过仿真实验,表明了对于线性高斯系统,精确的估计方法比粒子滤波的跟踪效果要好;但对于非线性非高斯模型,粒子滤波具有较大的优势,可以显著提高滤波的效果,并应用语音增强的实例进行了比较说明。
针对多传感器目标跟踪问题,本文论述了数据融合技术,提出了一种交叉传感器交叉特征(CSCM)数据融合算法,可以对种类不同、模型不同的多个传感器数据进行融合,并应用粒子滤波来进行非线性估计,完成一移动机器人目标跟踪任务,为准确地定义目标位置的状态,我们分别采用最佳粒子、加权均值和鲁棒均值三种估计方法,并对三种基本的重采样策略进行了比较,实验结果证明了这种数据融合算法的可行性和有效性。
在视觉跟踪方面,本文论述了视觉跟踪技术、摄像机系统以及颜色分布的相关内容,提出一种基于颜色直方图的粒子滤波算法用来跟踪运动目标,所提出的方法可以处理旋转、尺寸变换和光照条件的变化以及目标的部分遮挡等问题,从而可以鲁棒地跟踪目标。这种方法是把颜色直方图结合到粒子滤波的观测模型中,应用二阶自回归模型作为系统模型,跟踪的目标既可以是刚性目标,也可以是非刚性目标,并且跟踪算法可以实时实现。
针对复杂背景环境下的多目标跟踪问题,本文论述了主要的数据关联技术,将目标检测算法和粒子滤波结合起来,利用颜色直方图作为观测模型,利用GNN算法进行数据关联,提出了一种基于粒子滤波的多目标跟踪算法,实现了视频场景中的多个目标跟踪。该算法对目标在场景中的频繁出现和消失、相似外表、交叉运动和短暂遮挡等有较好的处理效果。
关于粒子滤波目标跟踪的性能评价方面,本文提出了定量化的解决方案,以度量跟踪算法的品质。这种方案以精度和召回率为理论基础,相互补充,可以对视频目标跟踪的性能作出较合理的判断,通过设计实验,我们可以选择出最佳的参数,虽然所有的实验是在单目标跟踪的环境下完成的,但可以推广到多目标跟踪的场合。
In reality, stochastic dynamic systems are mostly nonlinear and non-Gaussian.So the nonlinear filtering problem is very popular and many applications are concerned with it. Target tracking is one important field among its applications. Target tracking is the estimation of unknown target kinematics state based on uncertain measurements from sensors. Recently, particle filter, also known as Sequential Monte Carlo Methods, has become popular tools to solve tracking problems. The popularity stems from their simplicity, flexibility, and ease of implementation, especially their powerful ability to deal with general nonlinear and non-Gaussian estimation problems. This nonlinear filtering technique is a numerical method based on simulation and to use discrete hidden Markov chain for modeling, the system model describes the evolvement of the unknown target state over time and the measurement model associates the available measurements to the target state. Using the past and present measurements, based on prior information, the prediction and update step are performed, an approximate distribution for the target state is attained.
In this dissertation, Sequential Monte Carlo methods and the principle of target tracking under the Bayesian framework are studied. The methods for improving the particle filter are discussed in detail. Also the convergence properties of particle filter are analysed. The simulation experiments are conducted to verify, for linear and Gaussian system, the exact estimation methods have better results than particle filter. However, for nonlinear and non-Gaussian dynamic system, particle filter has obvious dominance and it could improve the effect of filtering evidently. Also we demonstrate Rao-Blackwellized particle filter has good results for voice enhancement.
For the multi-sensor target tracking problems, the data fusion techniques are discussed and the Cross-Sensor and Cross-Modality (CSCM) data fusion algorithm is presented, which can deal with multiple sensors with different types and modalities. And particle filter is applied for nonlinear estimation to track a moving mobile robot. In order to find the state which describes the target position accurately, 3 different methods are used for estimation: the best particle, the weighted mean and the robust mean. Also 3 basic resampling schemes are compared. The experimental results show the feasibility and the effectiveness of the data fusion algorithm.
On visual tracking, the visual tracking techniques, including camera systems, color distribution and the other correlated knowledge are discussed. And a particle filter algorithm is presented based on color histogram to track a moving target which can deal with rotation, scale changes, variations in the light source and partial occlusions. So it can track the target with robustness. The proposed method is based on particle filter, integrated with color histogram in the measurement model, and the system model is a second-order autoregressive process. The tracked target can be rigid or non-rigid. Also the method can run in real-time.
For the multi-target tracking problems with complex background, the data association techniques are discussed, we combine the target detection algorithm with particle filter, use color histogram as observation model, and the global nearest neighbor (GNN) performs data association. A multi-target tracking algorithm is presented based on particle filter. The proposed algorithm is robust to the problems of the appearance and disappearance of targets, the similar appearance of targets, the cross movement of targets and short-time occlusion.
For the performance evaluation of target tracking based on particle filter, a quantity approach is developed which can evaluate the quality of tracking algorithm. The strategy is theoretically based on precision and recall, complementary to the logical assessment of a visual tracking system’s performance. Via designing the experiments, the optimal parameters are chosen. All experiments have solely used single-target tracking. However, the change from single-target tracking to multi-target tracking is also evident.
本文对基于贝叶斯框架下的序列Monte Carlo方法及目标跟踪原理进行了综述,对粒子滤波的改进方法进行了全面的概括,分析了粒子滤波方法的收敛性,其收敛特性保证了收敛率独立于状态空间的维数,而且是Lp收敛的。通过仿真实验,表明了对于线性高斯系统,精确的估计方法比粒子滤波的跟踪效果要好;但对于非线性非高斯模型,粒子滤波具有较大的优势,可以显著提高滤波的效果,并应用语音增强的实例进行了比较说明。
针对多传感器目标跟踪问题,本文论述了数据融合技术,提出了一种交叉传感器交叉特征(CSCM)数据融合算法,可以对种类不同、模型不同的多个传感器数据进行融合,并应用粒子滤波来进行非线性估计,完成一移动机器人目标跟踪任务,为准确地定义目标位置的状态,我们分别采用最佳粒子、加权均值和鲁棒均值三种估计方法,并对三种基本的重采样策略进行了比较,实验结果证明了这种数据融合算法的可行性和有效性。
在视觉跟踪方面,本文论述了视觉跟踪技术、摄像机系统以及颜色分布的相关内容,提出一种基于颜色直方图的粒子滤波算法用来跟踪运动目标,所提出的方法可以处理旋转、尺寸变换和光照条件的变化以及目标的部分遮挡等问题,从而可以鲁棒地跟踪目标。这种方法是把颜色直方图结合到粒子滤波的观测模型中,应用二阶自回归模型作为系统模型,跟踪的目标既可以是刚性目标,也可以是非刚性目标,并且跟踪算法可以实时实现。
针对复杂背景环境下的多目标跟踪问题,本文论述了主要的数据关联技术,将目标检测算法和粒子滤波结合起来,利用颜色直方图作为观测模型,利用GNN算法进行数据关联,提出了一种基于粒子滤波的多目标跟踪算法,实现了视频场景中的多个目标跟踪。该算法对目标在场景中的频繁出现和消失、相似外表、交叉运动和短暂遮挡等有较好的处理效果。
关于粒子滤波目标跟踪的性能评价方面,本文提出了定量化的解决方案,以度量跟踪算法的品质。这种方案以精度和召回率为理论基础,相互补充,可以对视频目标跟踪的性能作出较合理的判断,通过设计实验,我们可以选择出最佳的参数,虽然所有的实验是在单目标跟踪的环境下完成的,但可以推广到多目标跟踪的场合。
In reality, stochastic dynamic systems are mostly nonlinear and non-Gaussian.So the nonlinear filtering problem is very popular and many applications are concerned with it. Target tracking is one important field among its applications. Target tracking is the estimation of unknown target kinematics state based on uncertain measurements from sensors. Recently, particle filter, also known as Sequential Monte Carlo Methods, has become popular tools to solve tracking problems. The popularity stems from their simplicity, flexibility, and ease of implementation, especially their powerful ability to deal with general nonlinear and non-Gaussian estimation problems. This nonlinear filtering technique is a numerical method based on simulation and to use discrete hidden Markov chain for modeling, the system model describes the evolvement of the unknown target state over time and the measurement model associates the available measurements to the target state. Using the past and present measurements, based on prior information, the prediction and update step are performed, an approximate distribution for the target state is attained.
In this dissertation, Sequential Monte Carlo methods and the principle of target tracking under the Bayesian framework are studied. The methods for improving the particle filter are discussed in detail. Also the convergence properties of particle filter are analysed. The simulation experiments are conducted to verify, for linear and Gaussian system, the exact estimation methods have better results than particle filter. However, for nonlinear and non-Gaussian dynamic system, particle filter has obvious dominance and it could improve the effect of filtering evidently. Also we demonstrate Rao-Blackwellized particle filter has good results for voice enhancement.
For the multi-sensor target tracking problems, the data fusion techniques are discussed and the Cross-Sensor and Cross-Modality (CSCM) data fusion algorithm is presented, which can deal with multiple sensors with different types and modalities. And particle filter is applied for nonlinear estimation to track a moving mobile robot. In order to find the state which describes the target position accurately, 3 different methods are used for estimation: the best particle, the weighted mean and the robust mean. Also 3 basic resampling schemes are compared. The experimental results show the feasibility and the effectiveness of the data fusion algorithm.
On visual tracking, the visual tracking techniques, including camera systems, color distribution and the other correlated knowledge are discussed. And a particle filter algorithm is presented based on color histogram to track a moving target which can deal with rotation, scale changes, variations in the light source and partial occlusions. So it can track the target with robustness. The proposed method is based on particle filter, integrated with color histogram in the measurement model, and the system model is a second-order autoregressive process. The tracked target can be rigid or non-rigid. Also the method can run in real-time.
For the multi-target tracking problems with complex background, the data association techniques are discussed, we combine the target detection algorithm with particle filter, use color histogram as observation model, and the global nearest neighbor (GNN) performs data association. A multi-target tracking algorithm is presented based on particle filter. The proposed algorithm is robust to the problems of the appearance and disappearance of targets, the similar appearance of targets, the cross movement of targets and short-time occlusion.
For the performance evaluation of target tracking based on particle filter, a quantity approach is developed which can evaluate the quality of tracking algorithm. The strategy is theoretically based on precision and recall, complementary to the logical assessment of a visual tracking system’s performance. Via designing the experiments, the optimal parameters are chosen. All experiments have solely used single-target tracking. However, the change from single-target tracking to multi-target tracking is also evident.
引文
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