Attractor-Guided Particle Filtering for Lip Contour Tracking

A popular approach for solving the indoor dynamic localization problem based on WiFi measurements consists of using particle filtering. However, a drawback of this approach is that a very large number of particles are needed to achieve accurate results in real environments. The reason for this drawback is that, in this particular application, classical particle filtering wastes many unnecessary particles. To remedy this, we propose a novel particle filtering method which we call maximum likelihood particle filter (MLPF). The essential idea consists of combining the particle prediction and update steps into a single one in which all particles are efficiently used. This drastically reduces the number of particles, leading to numerically feasible algorithms with high accuracy. We provide experimental results, using real data, confirming our claim.

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Structure Identification of Recursive TSK Particle Filtering via Type-2 Intuitionistic Fuzzy Decision

International Journal of Fuzzy Systems ◽

10.1007/s40815-020-01021-6 ◽

2021 ◽

Author(s):

Xiao-li Wang ◽

Wei-xin Xie ◽

Liang-qun Li

Keyword(s):

Particle Filtering ◽

Structure Identification ◽

Fuzzy Decision ◽

Intuitionistic Fuzzy

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Multiple particle filtering for tracking wireless agents via Monte Carlo likelihood approximation

EURASIP Journal on Advances in Signal Processing ◽

10.1186/s13634-019-0643-3 ◽

2019 ◽

Vol 2019 (1) ◽

Cited By ~ 1

Author(s):

Stephan Schlupkothen ◽

Gerd Ascheid

Keyword(s):

Monte Carlo ◽

Computational Complexity ◽

Particle Filtering ◽

Gaussian Approximation ◽

State Of The Art ◽

Approximation Techniques ◽

Accurate Localization ◽

Likelihood Approximation ◽

Current State ◽

Multiple Particle

Abstract The localization of multiple wireless agents via, for example, distance and/or bearing measurements is challenging, particularly if relying on beacon-to-agent measurements alone is insufficient to guarantee accurate localization. In these cases, agent-to-agent measurements also need to be considered to improve the localization quality. In the context of particle filtering, the computational complexity of tracking many wireless agents is high when relying on conventional schemes. This is because in such schemes, all agents’ states are estimated simultaneously using a single filter. To overcome this problem, the concept of multiple particle filtering (MPF), in which an individual filter is used for each agent, has been proposed in the literature. However, due to the necessity of considering agent-to-agent measurements, additional effort is required to derive information on each individual filter from the available likelihoods. This is necessary because the distance and bearing measurements naturally depend on the states of two agents, which, in MPF, are estimated by two separate filters. Because the required likelihood cannot be analytically derived in general, an approximation is needed. To this end, this work extends current state-of-the-art likelihood approximation techniques based on Gaussian approximation under the assumption that the number of agents to be tracked is fixed and known. Moreover, a novel likelihood approximation method is proposed that enables efficient and accurate tracking. The simulations show that the proposed method achieves up to 22% higher accuracy with the same computational complexity as that of existing methods. Thus, efficient and accurate tracking of wireless agents is achieved.

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Particle Filtering Based Remaining Useful Life Prediction for Electromagnetic Coil Insulation

Sensors ◽

10.3390/s21020473 ◽

2021 ◽

Vol 21 (2) ◽

pp. 473

Author(s):

Haifeng Guo ◽

Aidong Xu ◽

Kai Wang ◽

Yue Sun ◽

Xiaojia Han ◽

...

Keyword(s):

Particle Filtering ◽

Remaining Useful Life ◽

Electrical Parameters ◽

Degradation Model ◽

Accelerated Degradation ◽

Insulation Failure ◽

Electromagnetic Coils ◽

Degradation Monitoring ◽

Insulation Degradation ◽

Useful Life

Electromagnetic coils are one of the key components of many systems. Their insulation failure can have severe effects on the systems in which coils are used. This paper focuses on insulation degradation monitoring and remaining useful life (RUL) prediction of electromagnetic coils. First, insulation degradation characteristics are extracted from coil high-frequency electrical parameters. Second, health indicator is defined based on insulation degradation characteristics to indicate the health degree of coil insulation. Finally, an insulation degradation model is constructed, and coil insulation RUL prediction is performed by particle filtering. Thermal accelerated degradation experiments are performed to validate the RUL prediction performance. The proposed method presents opportunities for predictive maintenance of systems that incorporate coils.

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