Error-Ellipse-Resampling-Based Particle Filtering Algorithm for Target Tracking
| Autor: | Jiawang Wan, Cheng Xu, Xinxin Wang, Shihong Duan |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Covariance matrix 010401 analytical chemistry Probability density function Kalman filter Tracking (particle physics) Ellipse 01 natural sciences Upper and lower bounds 0104 chemical sciences Position (vector) Resampling Electrical and Electronic Engineering Particle filter Instrumentation Algorithm |
| Zdroj: | IEEE Sensors Journal. 20:5389-5397 |
| ISSN: | 2379-9153 1530-437X |
| DOI: | 10.1109/jsen.2020.2968371 |
| Popis: | In this paper, an error-ellipse-resampling-based particle filter (EER-PF) algorithm is proposed for target tracking in wireless sensor networks. In order to improve the effectiveness of the particles, in the process of resampling, the error ellipse of different confidence levels is established according to the error covariance matrix of particles. The particles are divided into different levels based on the geometrical position, and then the particles are screened and optimized. The effectiveness of the proposed method in a cumulative error optimization was verified by comparing with the performance of posterior Cramer-Rao lower bound (PCRLB). Experimental results show that the proposed algorithm can effectively solve the problem of sample degeneracy and impoverishment, and has higher positioning accuracy. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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