Bidirectional feedback dynamic particle filter with big data for the particle degeneracy problem

With the revolutionary development of cloud computing and internet of things, the integration and utilization of “big data” resources is a hot topic of the artificial intelligence research. Face recognition technology information has the advantages of being non-replicable, non-stealing, simple and intuitive. Video face tracking in the context of big data has become an important research hotspot in the field of information security. In this paper, a multi-feature fusion adaptive adjustment target tracking window and an adaptive update template particle filter tracking framework algorithm are proposed. Firstly, the skin color and edge features of the face are extracted in the video sequence. The weighted color histogram are extracted which describes the face features. Then we use the integral histogram method to simplify the histogram calculation of the particles. Finally, according to the change of the average distance, the tracking window is adjusted to accurately track the tracking object. At the same time, the algorithm can adaptively update the tracking template which improves the accuracy and accuracy of the tracking. The experimental results show that the proposed method improves the tracking effect and has strong robustness in complex backgrounds such as skin color, illumination changes and face occlusion.

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Effect of Resampling on the Performance and Execution Speed of Adaptive Marginalized Particle Filter

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c5359.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 4005-4012

Keyword(s):

Particle Filter ◽

State Estimation ◽

Execution Time ◽

Adaptive Filters ◽

Mean Square ◽

Different Types ◽

Execution Speed ◽

And Performance ◽

Major Factors ◽

Degeneracy Problem

One of the major factors that affects the performance of adaptive filters like Particle Filter (PF), Marginalized Particle Filter (MPF) and Adaptive Marginalized Particle Filter (AMPF) is sample degeneracy. Sample degeneracy occurs when the weights associated with particles converges to zero making them useless in state estimation. Resampling is the most common method used to avoid sample degeneracy problem, in which a new set of particles are generated and weights are assigned. Performance and execution time of these filter depends a lot on what type of resampling technique is employed. AMPF is the modified version of MPF which is typically faster than PF and MPF. The main aim of this paper is to find the effect of different types of resampling on the performance and execution time of AMPF. For this, a typical target tracking problem is simulated using MATLAB. AMPF with different types of resampling techniques is used for state estimation for the above-mentioned problem and the best in terms of performance and execution speed will be found out. From the simulation, it will be clear that AMPF with systematic resampling is found to be best in terms of execution speed and performance i.e. minimum Root Mean Square Error.

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Breeding Estimated Particle Filter

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.740.332 ◽

2013 ◽

Vol 740 ◽

pp. 332-337

Author(s):

Yu Bao Hou ◽

Shu Yan Tang

Keyword(s):

Particle Filter ◽

True State ◽

Normal Particle ◽

Propagation Prediction ◽

Degeneracy Problem ◽

Filter Experiment

As the normal particle filter has an expensive computation and degeneracy problem, a propagation-prediction particle filter is proposed. In this scheme, particles after transfer are propagated under the distribution of state noise, and then the produced filial particles are used to predict the corresponding parent particle referring to measurement, in which step the newest measure information is added into estimation. Therefore predicted particle would be closer to the true state, which improves the precision of particle filter. Experiment results have proved the efficiency of the algorithm and the great predominance in little particles case.

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A Fault Predication Algorithm Based on Artificial Immune Particle Filter

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.44-47.3459 ◽

2010 ◽

Vol 44-47 ◽

pp. 3459-3463 ◽

Cited By ~ 1

Author(s):

Yu Kun Qiao ◽

Qi Zhang ◽

Jin Sheng Zhang

Keyword(s):

Particle Filter ◽

Immune Algorithm ◽

Artificial Immune ◽

Artificial Immune Algorithm ◽

Large Weight ◽

Tracking Ability ◽

Simulation Results ◽

Degeneracy Problem ◽

Sir Particle Filter

Degeneracy problem is an inevitable result of sequential importance re-sampling (SIR) particle filter, and a mass of degenerated particles will influence the tracking ability of particle filter seriously. As a result, SIR particle filter based predication algorithm can’t predict system faults accurately. Artificial immune algorithm is characterized by a global ability to search for optimum, so it is introduced into the particle filter, named artificial immune particle filter (AIPF). Particles are regarded as antibodies in AIPF and particles with large weight aberrance and are cloned, and then the better particles are selected for states evaluation. A fault predication algorithm based on AIPF is proposed to improve the predication accuracy, and simulation results have demonstrated the feasibility of the proposed algorithm.

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A Particle Filter Algorithm Based on Mixing of Prior Probability Density and UKF as Generate Importance Function

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.743.403 ◽

2015 ◽

Vol 743 ◽

pp. 403-406

Author(s):

Liu Lu ◽

J. Wang ◽

M. Yang ◽

S. Geng

Keyword(s):

Particle Filter ◽

Prior Probability ◽

Negative Influence ◽

Nonlinear Filter ◽

Comprehensive Method ◽

Importance Function ◽

Particle Filter Algorithm ◽

Technical Features ◽

Key Steps ◽

Degeneracy Problem

As an important nonlinear filter theory, particle filter is a heated issue in domestic and foreign researches. The option of importance density is one of the key steps of particle filter algorithm. The proper option of importance density can minish the negative influence of filter algorithm caused by degeneracy problem. This paper introduces several widely-used options of importance density systemically, and analyzes their features and applied perspectives respectively. The paper also advances a comprehensive method of importance density, analyzes its technical features, explores the adjudgement and improvement of this method based on various performance, and finally puts forward the necessary further study according to the engineer requirements.

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Big Data Local-Loop Particle Filter Based on the Artificial Fish Algorithm

Computer Science and Artificial Intelligence ◽

10.1142/9789813220294_0065 ◽

2017 ◽

Author(s):

Jian Yu ◽

Yu Yan

Keyword(s):

Big Data ◽

Particle Filter ◽

Local Loop

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A Novel Strong Tracking Fault Prognosis Algorithm

Mathematical Problems in Engineering ◽

10.1155/2015/676289 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10

Author(s):

Qi Zhang ◽

Wei Jiang ◽

Tian-Mei Li ◽

Jian-Fei Zheng

Keyword(s):

Particle Filter ◽

Immune Algorithm ◽

Artificial Immune ◽

Artificial Immune Algorithm ◽

Fault Prognosis ◽

Artificial Immunity ◽

Simulation Results ◽

Strong Tracking Filter ◽

Tracking Filter ◽

Degeneracy Problem

Improving the ability to track abruptly changing states and resolving the degeneracy are two difficult problems to particle filter applied to fault prognosis. In this paper, a novel strong tracking fault prognosis algorithm is proposed to settle the above problems. In the proposed algorithm, the artificial immunity algorithm is first introduced to resolve the degeneracy problem, and then the strong tracking filter is introduced to enhance the ability to track abruptly changing states. The particles are updated by strong tracking filter, and better particles are selected by utilizing the artificial immune algorithm to estimate states. As a result, the degeneracy problem is resolved and the accuracy of the proposed fault prognosis algorithm is improved accordingly. The feasibility and validity of the proposed algorithm are demonstrated by the simulation results of the standard validation model and the DTS200 system.

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