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Abstract:   (35 Views)
The particle filter (PF) is a novel technique that has sufficiently good estimation results for the nonlinear/non-Gaussian systems. However, PF is inconsistent that caused mainly by loss of particle diversity in resampling step and unknown a priori knowledge of the noise statistics. This paper introduces a new modified particle filter called adaptive unscented particle filter (AUPF) to overcome these problems. The proposed method uses an adaptive unscented Kalman filter (AUKF) filter to generate the proposal distribution, in which the covariance of the measurement and process of the state are online adjusted by predicted residual as an adaptive factor based on a covariance matching technique. In addition, it uses the genetic operators based strategy to further improve the particle diversity. The results show the effectiveness of the proposed approach.
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  • Introducing a new modified particle filter.
  • Using an adaptive unscented Kalman filter (AUKF) filter to generate the proposal distribution.
  • Online tuning the covariance of the measurement and process of the state by predicted residual as adaptive factor based on a covariance matching technique.
  • Using the genetic operators based strategy to further improve the particle diversity.

Type of Study: Research Paper | Subject: Filtering , Smoothing & Estimation
Received: 2019/09/17 | Accepted: 2019/11/09 | Published: 2019/11/10

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© 2019 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.