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A Distributed particle filter Applied in Single Object Tracking

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Journal of Computer and Communications 2024年第8期12卷 99-109页

作者： Di Wang Min Chen Department of Computer Science State University of New York at New Paltz New York USA

Visual object-tracking is a fundamental task applied in many applications of computer vision. particle filter is one of the techniques which has been widely used in object tracking. Due to the virtue of extendability and flexibility on both linear and non-linear environments, various particle filter-based trackers have been proposed in the literature. However, the conventional approach cannot handle very large videos efficiently in the current data intensive information age. In this work, a parallelized particle filter is provided in a distributed framework provided by the Hadoop/Map-Reduce infrastructure to tackle object-tracking tasks. The experiments indicate that the proposed algorithm has a better convergence and accuracy as compared to the traditional particle filter. The computational power and the scalability of the proposed particle filter in single object tracking have been enhanced as well.

关键词： Distributed System particle filter Single Object Tracking

particle filter based on iterated importance density function and parallel resampling

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Journal of Central South University 2015年第9期22卷 3427-3439页

作者：武勇王俊曹运合 National Laboratory of Radar Signal Processing (Xidian University)

The design, analysis and parallel implementation of particle filter(PF) were investigated. Firstly, to tackle the particle degeneracy problem in the PF, an iterated importance density function(IIDF) was proposed, where a new term associating with the current measurement information(CMI) was introduced into the expression of the sampled particles. Through the repeated use of the least squares estimate, the CMI can be integrated into the sampling stage in an iterative manner, conducing to the greatly improved sampling quality. By running the IIDF, an iterated PF(IPF) can be obtained. Subsequently, a parallel resampling(PR) was proposed for the purpose of parallel implementation of IPF, whose main idea was the same as systematic resampling(SR) but performed differently. The PR directly used the integral part of the product of the particle weight and particle number as the number of times that a particle was replicated, and it simultaneously eliminated the particles with the smallest weights, which are the two key differences from the SR. The detailed implementation procedures on the graphics processing unit of IPF based on the PR were presented at last. The performance of the IPF, PR and their parallel implementations are illustrated via one-dimensional numerical simulation and practical application of passive radar target tracking.

关键词： particle filter iterated importance density function least squares estimate parallel resampling graphics processing unit

particle filter for nonlinear systems with multi-sensor asynchronous random delays

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Journal of Systems Engineering and Electronics 2017年第6期28卷 1064-1071页

作者： Junyi Zuo Xiaoping Zhong School of Aeronautics Northwestern Polytechnical UniversityXi’an 710072China

This paper is concerned with the recursive filtering problem for a class of discrete-time nonlinear stochastic systems in the presence of multi-sensor measurement delay. The delay occurs in a multi-step and asynchronous manner, and the delay probability of each sensor is assumed to be known or unknown. Firstly, a new model is constructed to describe the measurement process, based on which a new particle filter is developed with the ability to fuse multi-sensor information in the case of known delay *** addition, an online delay probability estimation module is introduced in the particle filtering framework, which leads to another new filter that can be implemented without the prior knowledge of delay probability. More importantly, since there is no complex iterative operation, the resulting filter can be implemented recursively and is suitable for many real-time applications. Simulation results show the effectiveness of the proposed filters.

关键词： particle filter nonlinear dynamic system state estima tion measurement delay multiple sensors

particle filter initialization in non-linear non-Gaussian radar target tracking

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Journal of Systems Engineering and Electronics 2007年第3期18卷 491-496页

作者： Wang Jian Jin Yonggao Dai Dingzhang Dong Huachun Quan Taifan Dept. of Electronics and Communication Engineering Harbin Inst. of Technology Harbin 150001 P. R. China Dept. of Electronics and Information Engineering Yanbian Univ. Yanbian 133002 P. R. China

When particle filter is applied in radar target tracking, the accuracy of the initial particles greatly effects the results of filtering. For acquiring more accurate initial particles, a new method called “competition strategy algorithm” is presented. In this method, initial measurements give birth to several particle groups around them, regularly. Each of the groups is tested several times, separately, in the beginning periods, and the group that has the most number of efficient particles is selected as the initial particles. For this method, sample initial particles selected are on the basis of several measurements instead of only one first measurement, which surely improves the accuracy of initial particles. The method sacrifices initialization time and computation cost for accuracy of initial particles. Results of simulation show that it greatly improves the accuracy of initial particles, which makes the effect of filtering much better.

关键词： radar target tracking particle filter initialization.

particle filter based stochastic interference cancellation for frequency-selective MIMO channel equalization

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Journal of Systems Engineering and Electronics 2007年第3期18卷 476-483页

作者： Yang Tao Hu Bo Guo Xinyue Dept. of Electronics Engineering Fudan Univ. Shanghai 200433 P. R. China

To mitigate the effects of the previous symbol decision errors of a decision-feedback （DF） equalizer on the current decision, a particle filter （PF） based DF equalizer for frequency selective multiple-input-multiple-output （MIMO） channel is proposed. On the basis of the analyses of DF equalization for the MIMO wireless system, it is found that a stochastic interference cancellation （IC） scheme can be employed to prevent the error propagation in a severe space-time interference scenario. This is because the random rather than the deterministic scheme can reduce the probability of an error decision even if an error decision occurs. Besides, the signal-to-interference-plus-noise ratio （SINR） based IC order, which is obtained via pilot, can guarantee the optimality of the cancellation. The bit error rate （BER） performance of the proposed scheme is verified through simulation experiments under different multipath interference environment.

关键词： multiple-input-multiple-output particle filter pilot decision-feedback interference cancellation.

particle filter based automatic frequency control scheme by combining the two-step structure

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The Journal of China Universities of Posts and Telecommunications 2012年第2期19卷 9-14页

作者： CHEN Peng LI Ju-hu NIU Kai WU Wei-ling Key Laboratory of Universal Wireless Communications(Minislry of Education) Beijing University of Posts and TelecommunicationsBeijing 100876China

In this paper a new automatic frequency control （AFC） scheme was proposed, which could be used for the receiver of low earth orbit （LEO） satellite communication system in continuous transmitting scenario. By employing the time varying characteristic of particle filter technique, the new scheme combined the preamble based estimating step and data based estimating step to provide initial probability density recursively. Theoretical analysis proved that the proposed AFC scheme could provide better performance than the two-step scheme. The same conclusion was achieved by computer simulations with the criteria of root-mean square （RMS） frequency estimating performance and bit error rate performance.

关键词： satellite communication Doppler effect automatic frequency control particle filter

A direct position determination method with combined TDOA and FDOA based on particle filter

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Chinese Journal of Aeronautics 2018年第1期31卷 161-168页

作者： Zhiyu LU Bin BA Jianhui WANG Wenchao LI Daming WANG China National Digital Switching System Engineering & Technological R&D Cente Zhengzhou 450000 China Jiuquan Satellite Launch Center Jiuquan 735000 China

The localization of a stationary transmitter using moving receivers is considered. The original Direct Position Determination （DPD） methods, with combined Time Difference of Arrival （TDOA） and Frequency Difference of Arrival （FDOA）, do not perform well under low Signal-to-Noise Ratio （SNR）, and worse still, the computation cost is difficult to accept when the computational capabilities are limited. To get better positioning performance, we present a new DPD algorithm that proves to be more computationally efficient and more precise for weak signals than the conventional approach. The algorithm partitions the signal received with the same receiver into multiple non-overlapping short-time signal segments, and then uses the TDOA, the FDOA and the coherency among the short-time signals to locate the target. The fast maximum likelihood estimation, one iterative method based on particle filter, is designed to solve the problem of high computation load. A secondary but important result is a derivation of closed-form expressions of the Cramer-Rao Lower Bound （CRLB）. The simulation results show that the algorithm proposed in this paper outperforms the traditional DPD algorithms with more accurate results and higher computational efficiency, and especially at low SNR, it is more close to the CRLB.

关键词： Direct position determination Cramer-Rao lower bound Frequency difference of arrival Time difference of arrival particle filter

Improving the real-time probabilistic channel flood forecasting by incorporating the uncertainty of inflow using the particle filter

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Journal of Hydrodynamics 2018年第5期30卷 828-840页

作者： Xing-ya Xu Xuesong Zhang Hong-wei Fang Rui-xun Lai Yue-feng Zhang Lei Huang Xiao-bo Liu Changjiang Institute of Survey PlanningDesign and ResearchWuhan 430010China State Key Laboratory of Hydroscience and Engineering Department of Hydraulic EngineeringTsinghua UniversityBeijing 100084China Joint Global Change Research Institute Pacific Northwest National LaboratoryCollege ParkMDUSA Great Lakes Bioenergy Research Center Michigan State UniversityEast LansingMichiganUSA Yellow River Institute of Hydraulic Research Zhengzhou 450003China Development Research Center of the Ministry of Water Resources of People＇s Republic of China Beo＇ing 100038China Department of Water Environment China Institute of Water Resources and Hydropower ResearchBeo＇ing 100038China

An accurate and reliable real-time flood forecast is crucial for mitigating flood disasters. The errors associated with the inflow boundary forcing data are considered as an important source of uncertainties in hydraulic model. In this paper, a real-time probabilistic channel flood forecasting model is developed with a novel function to incorporate the uncertainty of the forcing inflow. This new approach couples a hydraulic model with the particle filter（PF） data assimilation algorithm, a sequential Bayesian Monte Carlo method. The stage observations at hydrological stations are assimilated at each time step to update the model states in order to improve the next time step＇s forecasting. This new approach is tested against a real flood event occurred in the upper Yangtze River. As compared with the open loop simulations, the evaluations of model performance with several deterministic and probabilistic metrics indicate that the accuracy of the ensemble mean prediction and the reliability of the uncertainty quantification are improved pronouncedly as a result of the PF assimilation. Further assessment of the prediction results at different lead times shows that the improvement of model performance deteriorates with the increase of the lead time due to the gradual diminishing of the updating effect for the initial conditions. Based on the analyses of the number of particles and the assimilation frequency, we find that the optimal number of particles can be determined by balancing the model performance and the computation cost, while a high assimilation frequency is preferred to incorporate the emerging observations to update the model states to match the real conditions.

关键词： Channel flood forecasting probabilistic forecast particle filter hydraulic model data assimilation inflow uncertainty

Improved particle filter for Non-Gaussian Forecasting-aided State Estimation

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现代电力系统与清洁能源学报(英文) 2023年第4期11卷 1075-1085页

作者： Lyuzerui Yuan Jie Gu Honglin Wen Zhijian Jin School of Electronic Information and Electrical Engineering Shanghai Jiao Tong UniversityShanghai 200240China

Gaussian assumptions of non-Gaussian noises hinder the improvement of state estimation *** this paper,an asymmetric generalized Gaussian distribution(AGGD),as a unified representation of various unimodal distributions,is applied to formulate the non-Gaussian forecasting-aided state estimation *** address the problem,an improved particle filter is proposed,which integrates a near-optimal AGGD proposal function and an AGGD sampling method into the typical particle *** AGGD proposal function can approximate the target distribution of state variables to greatly alleviate particle degeneracy and promote precise estimation,through considering both state transitions and latest *** rapid particle generation from the AGGD proposal function,an efficient inverse cumulative distribution function(CDF)sampling method is employed based on the derived approximation of inverse CDF of *** simulations are carried out on a modified balanced IEEE 123-bus test *** results validate that the proposed method outperforms other popular state estimation methods in terms of accuracy and robustness,whether in Gaussian,non-Gaussian,or abnormal measurement errors.

关键词： State estimation particle filter asymmetric generalized Gaussian distribution non-Gaussian noise