A novel particle filter bandwidth adaption for kernel particle filter （BAKPF） is proposed. Selection of the kernel bandwidth is a critical issue in kernel density estimation （KDE）. The plug-in method is adopted to...A novel particle filter bandwidth adaption for kernel particle filter （BAKPF） is proposed. Selection of the kernel bandwidth is a critical issue in kernel density estimation （KDE）. The plug-in method is adopted to get the global fixed bandwidth by optimizing the asymptotic mean integrated squared error （AMISE） firstly. Then, particle-driven bandwidth selection is invoked in the KDE. To get a more effective allocation of the particles, the KDE with adap- tive bandwidth in the BAKPF is used to approximate the posterior probability density function （PDF） by moving particles toward the posterior. A closed-form expression of the true distribution is given. The simulation results show that the proposed BAKPF performs better than the standard particle filter （PF）, unscented particle filter （UPF） and the kernel particle filter （KPF） both in efficiency and estimation precision.展开更多
In order to improve the performance of the probability hypothesis density(PHD) algorithm based particle filter(PF) in terms of number estimation and states extraction of multiple targets, a new probability hypothesis ...In order to improve the performance of the probability hypothesis density(PHD) algorithm based particle filter(PF) in terms of number estimation and states extraction of multiple targets, a new probability hypothesis density filter algorithm based on marginalized particle and kernel density estimation is proposed, which utilizes the idea of marginalized particle filter to enhance the estimating performance of the PHD. The state variables are decomposed into linear and non-linear parts. The particle filter is adopted to predict and estimate the nonlinear states of multi-target after dimensionality reduction, while the Kalman filter is applied to estimate the linear parts under linear Gaussian condition. Embedding the information of the linear states into the estimated nonlinear states helps to reduce the estimating variance and improve the accuracy of target number estimation. The meanshift kernel density estimation, being of the inherent nature of searching peak value via an adaptive gradient ascent iteration, is introduced to cluster particles and extract target states, which is independent of the target number and can converge to the local peak position of the PHD distribution while avoiding the errors due to the inaccuracy in modeling and parameters estimation. Experiments show that the proposed algorithm can obtain higher tracking accuracy when using fewer sampling particles and is of lower computational complexity compared with the PF-PHD.展开更多
粒子滤波(Partic le F ilter)是一种处理非线性和非高斯动态系统状态估计的有效技术.提出了一种基于粒子滤波的红外目标稳健跟踪新方法.在粒子滤波理论框架下,红外目标的状态后验概率分布用加权随机样本集表示,通过这些随机样本的Bayes...粒子滤波(Partic le F ilter)是一种处理非线性和非高斯动态系统状态估计的有效技术.提出了一种基于粒子滤波的红外目标稳健跟踪新方法.在粒子滤波理论框架下,红外目标的状态后验概率分布用加权随机样本集表示,通过这些随机样本的Bayesian迭代进化实现红外目标的跟踪.系统状态转移模型选择为简单的二阶自回归模型,并自适应地确定系统噪声方差.红外目标的描述利用目标区域的灰度分布,该灰度分布通过核概率密度估计建立.通过计算参考目标的灰度分布和目标样本的灰度分布之间的Bhattacharyya距离,建立系统观测概率模型.实验结果表明该方法是有效的和稳健的.展开更多
为提高移动机器人在拥挤、混杂的室内环境中的定位能力,提出了在图像显著特征区域内提取积分不变特征-LSRII(local salient region integral invariant)特征的方法,并将LSRII特征应用到粒子滤波定位中,实现机器人在室内环境下的全局定...为提高移动机器人在拥挤、混杂的室内环境中的定位能力,提出了在图像显著特征区域内提取积分不变特征-LSRII(local salient region integral invariant)特征的方法,并将LSRII特征应用到粒子滤波定位中,实现机器人在室内环境下的全局定位。实验结果表明,所提出的定位方法在拥挤、混杂的室内环境中能够实现可靠的定位。展开更多
基金supported by the National Natural Science Foundation of China (60736043 60805012)the Fundamental Research Funds for the Central Universities (K50510020032)
文摘A novel particle filter bandwidth adaption for kernel particle filter （BAKPF） is proposed. Selection of the kernel bandwidth is a critical issue in kernel density estimation （KDE）. The plug-in method is adopted to get the global fixed bandwidth by optimizing the asymptotic mean integrated squared error （AMISE） firstly. Then, particle-driven bandwidth selection is invoked in the KDE. To get a more effective allocation of the particles, the KDE with adap- tive bandwidth in the BAKPF is used to approximate the posterior probability density function （PDF） by moving particles toward the posterior. A closed-form expression of the true distribution is given. The simulation results show that the proposed BAKPF performs better than the standard particle filter （PF）, unscented particle filter （UPF） and the kernel particle filter （KPF） both in efficiency and estimation precision.
基金Project(61101185) supported by the National Natural Science Foundation of ChinaProject(2011AA1221) supported by the National High Technology Research and Development Program of China
文摘In order to improve the performance of the probability hypothesis density(PHD) algorithm based particle filter(PF) in terms of number estimation and states extraction of multiple targets, a new probability hypothesis density filter algorithm based on marginalized particle and kernel density estimation is proposed, which utilizes the idea of marginalized particle filter to enhance the estimating performance of the PHD. The state variables are decomposed into linear and non-linear parts. The particle filter is adopted to predict and estimate the nonlinear states of multi-target after dimensionality reduction, while the Kalman filter is applied to estimate the linear parts under linear Gaussian condition. Embedding the information of the linear states into the estimated nonlinear states helps to reduce the estimating variance and improve the accuracy of target number estimation. The meanshift kernel density estimation, being of the inherent nature of searching peak value via an adaptive gradient ascent iteration, is introduced to cluster particles and extract target states, which is independent of the target number and can converge to the local peak position of the PHD distribution while avoiding the errors due to the inaccuracy in modeling and parameters estimation. Experiments show that the proposed algorithm can obtain higher tracking accuracy when using fewer sampling particles and is of lower computational complexity compared with the PF-PHD.
基金National Natural Science Foundation of China(No.61205106)Scientific and Technological Research Program of Chongqing Municipal Education Commission(Nos.KJ120827)
文摘粒子滤波(Partic le F ilter)是一种处理非线性和非高斯动态系统状态估计的有效技术.提出了一种基于粒子滤波的红外目标稳健跟踪新方法.在粒子滤波理论框架下,红外目标的状态后验概率分布用加权随机样本集表示,通过这些随机样本的Bayesian迭代进化实现红外目标的跟踪.系统状态转移模型选择为简单的二阶自回归模型,并自适应地确定系统噪声方差.红外目标的描述利用目标区域的灰度分布,该灰度分布通过核概率密度估计建立.通过计算参考目标的灰度分布和目标样本的灰度分布之间的Bhattacharyya距离,建立系统观测概率模型.实验结果表明该方法是有效的和稳健的.
文摘为提高移动机器人在拥挤、混杂的室内环境中的定位能力,提出了在图像显著特征区域内提取积分不变特征-LSRII(local salient region integral invariant)特征的方法,并将LSRII特征应用到粒子滤波定位中,实现机器人在室内环境下的全局定位。实验结果表明,所提出的定位方法在拥挤、混杂的室内环境中能够实现可靠的定位。
文摘针对辅助粒子滤波算法计算量大,滤波效率较低的问题,提出了一种基于快速高斯变换(Fast Gaussian transform,FGT)的辅助边缘粒子滤波算法。该算法假设状态噪声是加性的,并且是高斯的,这样非线性滤波的Chapman-Kolmogorov方程的求解近似于执行了核密度估计(Kerner density estimation,KDE),从而可将KDE中的快速算法FGT引入,以提高算法的计算效率和实时性。仿真结果表明,该算法利用少数粒子就可以获得与常规粒子滤波相似的误差,大大提高了计算效率。
