提出一种基于粒子概率假设密度滤波器(Sequential Monte Carlo probability hypothesis density filter,SMC-PHDF)的部分可分辨的群目标跟踪算法.该算法可直接获得群而非个体的个数和状态估计.这里群的状态包括群的质心状态和形状.为了...提出一种基于粒子概率假设密度滤波器(Sequential Monte Carlo probability hypothesis density filter,SMC-PHDF)的部分可分辨的群目标跟踪算法.该算法可直接获得群而非个体的个数和状态估计.这里群的状态包括群的质心状态和形状.为了估计群的个数和状态,该算法利用高斯混合模型(Gaussian mixture models,GMM)拟合SMC-PHDF中经重采样后的粒子分布,这里混合模型的元素个数和参数分别对应于群的个数和状态.期望最大化(Expectation maximum,EM)算法和马尔科夫链蒙特卡洛(Markov chain Monte Carlo,MCMC)算法分别被用于估计混合模型的参数.混合模型的元素个数可通过删除、合并及分裂算法得到.100次蒙特卡洛(Monte Carlo,MC)仿真实验表明该算法可有效跟踪部分可分辨的群目标.相比EM算法,MCMC算法能够更好地提取群的个数和状态,但它的计算量要大于EM算法.展开更多
Effective implementation of the fast labeled multi-Bernoulli(FLMB)filter is addressed for target tracking with interval measurements.Firstly,a sequential Monte Carlo(SMC)implementation of the FLMB filter,SMC-FLMB filt...Effective implementation of the fast labeled multi-Bernoulli(FLMB)filter is addressed for target tracking with interval measurements.Firstly,a sequential Monte Carlo(SMC)implementation of the FLMB filter,SMC-FLMB filter,is derived based on generalized likelihood function weighting.Then,a box particle(BP)implementation of the FLMB filter,BP-FLMB filter,is developed,with a computational complexity reduction of the SMC-FLMB filter.Finally,an improved version of the BP-FLMB filter,improved BP-FLMB(IBP-FLMB)filter,is proposed,improving its estimation accuracy and real-time performance under the conditions of low detection probability and high clutter.Simulation results show that the BP-FLMB filter has a great improvement of the real-time performance than the SMC-FLMB filter,with similar tracking performance.Compared with the BP-FLMB filter,the IBP-FLMB filter has better estimation performance and real-time performance under the conditions of low detection probability and high clutter.展开更多
A semi-analytical Monte Carlo (SMC) simulation was developed to simulate the propagation of laser-induced fluo- rescence (LIF) in an optically participating spray, which simultaneously exhibits spectrally dependent em...A semi-analytical Monte Carlo (SMC) simulation was developed to simulate the propagation of laser-induced fluo- rescence (LIF) in an optically participating spray, which simultaneously exhibits spectrally dependent emission, anisotropic scattering, absorption, and re-emission. The SMC simulation is described and then applied to an experimental configuration of a cloud of polydisperse droplets composed of water and sulforhodamine B dye. In the SMC simulation, the collected LIF flux on the remote receptor element is calculated as the global contribution from the emissive source, single, twice, … and nth collision events in any sequence. The effects on the fluorescence photons propagation of spray parameters like the dye concentration, droplets concentration, and droplets size are examined. Three spectral bands representing different optical properties are chosen to analyze the interference of absorption, scattering and re-emission on the detected LIF flux. The obtained spectral LIF flux distribution on the receptor demonstrates a “red shift” phenomenon.展开更多
Estimation and detection algorithms for orthogonal frequency division multiplexing (OFDM) systems can be de-veloped based on the sum-product algorithms, which operate by message passing in factor graphs. In this paper...Estimation and detection algorithms for orthogonal frequency division multiplexing (OFDM) systems can be de-veloped based on the sum-product algorithms, which operate by message passing in factor graphs. In this paper, we apply the sampling method (Monte Carlo) to factor graphs, and then the integrals in the sum-product algorithm can be approximated by sums, which results in complexity reduction. The blind receiver for OFDM systems can be derived via Sequential Monte Carlo (SMC) in factor graphs, the previous SMC blind receiver can be regarded as the special case of the sum-product algorithms using sampling methods. The previous SMC blind receiver for OFDM systems needs generating samples of the channel vector assuming the channel has an a priori Gaussian distribution. In the newly-built blind receiver, we generate samples of the virtual-pilots instead of the channel vector, with channel vector which can be easily computed based on virtual-pilots. As the size of the vir-tual-pilots space is much smaller than the channel vector space, only small number of samples are necessary, with the blind de-tection being much simpler. Furthermore, only one pilot tone is needed to resolve phase ambiguity and differential encoding is not used anymore. Finally, the results of computer simulations demonstrate that the proposal can perform well while providing sig-nificant complexity reduction.展开更多
Detection and tracking of multi-target with unknown and varying number is a challenging issue, especially under the condition of low signal-to-noise ratio(SNR). A modified multi-target track-before-detect(TBD) method ...Detection and tracking of multi-target with unknown and varying number is a challenging issue, especially under the condition of low signal-to-noise ratio(SNR). A modified multi-target track-before-detect(TBD) method was proposed to tackle this issue using a nonstandard point observation model. The method was developed from sequential Monte Carlo(SMC)-based probability hypothesis density(PHD) filter, and it was implemented by modifying the original calculation in update weights of the particles and by adopting an adaptive particle sampling strategy. To efficiently execute the SMC-PHD based TBD method, a fast implementation approach was also presented by partitioning the particles into multiple subsets according to their position coordinates in 2D resolution cells of the sensor. Simulation results show the effectiveness of the proposed method for time-varying multi-target tracking using raw observation data.展开更多
考虑到存活目标与新生目标在动态演化特性上的差异性,提出了面向快速多目标跟踪的协同概率假设密度(collaborative probability hypothesis density,CoPHD)滤波框架。该框架利用存活目标的状态信息,将量测动态划分为存活目标量测集与新...考虑到存活目标与新生目标在动态演化特性上的差异性,提出了面向快速多目标跟踪的协同概率假设密度(collaborative probability hypothesis density,CoPHD)滤波框架。该框架利用存活目标的状态信息,将量测动态划分为存活目标量测集与新生目标量测集,在两个量测集分别运用PHD组处理更新基础上建立了处理模块的交互与协同机制,力图在保证跟踪精度的同时提高计算效率。该框架由于采用PHD组处理方式而具有状态自动提取功能。进一步给出了该框架的序贯蒙特卡罗算法实现。仿真结果表明,该算法在计算效率以及状态提取精度上具有明显优势。展开更多
针对航空制造业中,当容差分配问题中含有装配成功率等随机约束时,常用的数值算法往往难以处理。为提高产品制造精度,提出了混合蒙特卡洛(Hybrid Monte Carlo,HMC)算法,即把动态蒙特卡洛(Dynamic Monte Carlo,DMC)算法和静态蒙特卡罗(SMC...针对航空制造业中,当容差分配问题中含有装配成功率等随机约束时,常用的数值算法往往难以处理。为提高产品制造精度,提出了混合蒙特卡洛(Hybrid Monte Carlo,HMC)算法,即把动态蒙特卡洛(Dynamic Monte Carlo,DMC)算法和静态蒙特卡罗(SMC)算法结合起来,将DMC用于容差分配的优化仿真运算,把SMC用来处理装配成功率约束。通过仿真验证了该方案的可行性,混合蒙特卡洛法既合理地处理了随机约束,证明装配准确度计算和容差分配的一致性。结果说明求解这类问题是最佳算法。展开更多
文摘提出一种基于粒子概率假设密度滤波器(Sequential Monte Carlo probability hypothesis density filter,SMC-PHDF)的部分可分辨的群目标跟踪算法.该算法可直接获得群而非个体的个数和状态估计.这里群的状态包括群的质心状态和形状.为了估计群的个数和状态,该算法利用高斯混合模型(Gaussian mixture models,GMM)拟合SMC-PHDF中经重采样后的粒子分布,这里混合模型的元素个数和参数分别对应于群的个数和状态.期望最大化(Expectation maximum,EM)算法和马尔科夫链蒙特卡洛(Markov chain Monte Carlo,MCMC)算法分别被用于估计混合模型的参数.混合模型的元素个数可通过删除、合并及分裂算法得到.100次蒙特卡洛(Monte Carlo,MC)仿真实验表明该算法可有效跟踪部分可分辨的群目标.相比EM算法,MCMC算法能够更好地提取群的个数和状态,但它的计算量要大于EM算法.
基金supported by the National Natural Science Foundation of China(61871301)the Postdoctoral Science Foundation of China(2018M633470,2020T130494)the Fundamental Research Funds for the Central Universities(XJS210211).
文摘Effective implementation of the fast labeled multi-Bernoulli(FLMB)filter is addressed for target tracking with interval measurements.Firstly,a sequential Monte Carlo(SMC)implementation of the FLMB filter,SMC-FLMB filter,is derived based on generalized likelihood function weighting.Then,a box particle(BP)implementation of the FLMB filter,BP-FLMB filter,is developed,with a computational complexity reduction of the SMC-FLMB filter.Finally,an improved version of the BP-FLMB filter,improved BP-FLMB(IBP-FLMB)filter,is proposed,improving its estimation accuracy and real-time performance under the conditions of low detection probability and high clutter.Simulation results show that the BP-FLMB filter has a great improvement of the real-time performance than the SMC-FLMB filter,with similar tracking performance.Compared with the BP-FLMB filter,the IBP-FLMB filter has better estimation performance and real-time performance under the conditions of low detection probability and high clutter.
基金Project supported by the National Natural Science Foundation of China (No. 60534030)the Scholarship of French Embassy in China and the Doctoral Grant from French Embassy in China
文摘A semi-analytical Monte Carlo (SMC) simulation was developed to simulate the propagation of laser-induced fluo- rescence (LIF) in an optically participating spray, which simultaneously exhibits spectrally dependent emission, anisotropic scattering, absorption, and re-emission. The SMC simulation is described and then applied to an experimental configuration of a cloud of polydisperse droplets composed of water and sulforhodamine B dye. In the SMC simulation, the collected LIF flux on the remote receptor element is calculated as the global contribution from the emissive source, single, twice, … and nth collision events in any sequence. The effects on the fluorescence photons propagation of spray parameters like the dye concentration, droplets concentration, and droplets size are examined. Three spectral bands representing different optical properties are chosen to analyze the interference of absorption, scattering and re-emission on the detected LIF flux. The obtained spectral LIF flux distribution on the receptor demonstrates a “red shift” phenomenon.
基金Project supported by the National Hi-Tech Research and Develop-ment Program (863) of China (No. 2003AA123310) and the National Natural Science Foundation of China (No. 60332030)
文摘Estimation and detection algorithms for orthogonal frequency division multiplexing (OFDM) systems can be de-veloped based on the sum-product algorithms, which operate by message passing in factor graphs. In this paper, we apply the sampling method (Monte Carlo) to factor graphs, and then the integrals in the sum-product algorithm can be approximated by sums, which results in complexity reduction. The blind receiver for OFDM systems can be derived via Sequential Monte Carlo (SMC) in factor graphs, the previous SMC blind receiver can be regarded as the special case of the sum-product algorithms using sampling methods. The previous SMC blind receiver for OFDM systems needs generating samples of the channel vector assuming the channel has an a priori Gaussian distribution. In the newly-built blind receiver, we generate samples of the virtual-pilots instead of the channel vector, with channel vector which can be easily computed based on virtual-pilots. As the size of the vir-tual-pilots space is much smaller than the channel vector space, only small number of samples are necessary, with the blind de-tection being much simpler. Furthermore, only one pilot tone is needed to resolve phase ambiguity and differential encoding is not used anymore. Finally, the results of computer simulations demonstrate that the proposal can perform well while providing sig-nificant complexity reduction.
基金Projects(61002022,61471370)supported by the National Natural Science Foundation of China
文摘Detection and tracking of multi-target with unknown and varying number is a challenging issue, especially under the condition of low signal-to-noise ratio(SNR). A modified multi-target track-before-detect(TBD) method was proposed to tackle this issue using a nonstandard point observation model. The method was developed from sequential Monte Carlo(SMC)-based probability hypothesis density(PHD) filter, and it was implemented by modifying the original calculation in update weights of the particles and by adopting an adaptive particle sampling strategy. To efficiently execute the SMC-PHD based TBD method, a fast implementation approach was also presented by partitioning the particles into multiple subsets according to their position coordinates in 2D resolution cells of the sensor. Simulation results show the effectiveness of the proposed method for time-varying multi-target tracking using raw observation data.
文摘考虑到存活目标与新生目标在动态演化特性上的差异性,提出了面向快速多目标跟踪的协同概率假设密度(collaborative probability hypothesis density,CoPHD)滤波框架。该框架利用存活目标的状态信息,将量测动态划分为存活目标量测集与新生目标量测集,在两个量测集分别运用PHD组处理更新基础上建立了处理模块的交互与协同机制,力图在保证跟踪精度的同时提高计算效率。该框架由于采用PHD组处理方式而具有状态自动提取功能。进一步给出了该框架的序贯蒙特卡罗算法实现。仿真结果表明,该算法在计算效率以及状态提取精度上具有明显优势。
文摘针对航空制造业中,当容差分配问题中含有装配成功率等随机约束时,常用的数值算法往往难以处理。为提高产品制造精度,提出了混合蒙特卡洛(Hybrid Monte Carlo,HMC)算法,即把动态蒙特卡洛(Dynamic Monte Carlo,DMC)算法和静态蒙特卡罗(SMC)算法结合起来,将DMC用于容差分配的优化仿真运算,把SMC用来处理装配成功率约束。通过仿真验证了该方案的可行性,混合蒙特卡洛法既合理地处理了随机约束,证明装配准确度计算和容差分配的一致性。结果说明求解这类问题是最佳算法。
基金supported by National Key Technology Research and Development Program of China(No.2011BAA07B07)National Natural Science Foundation of China(No.51077041)
