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Showing 2 results for Havangi
A New Modified Particle Filter With Application in Target Tracking
R. Havangi,
Volume 16, Issue 4 (December 2020)
Abstract
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.

Effects of Moving Landmark’s Speed on Multi-Robot Simultaneous Localization and Mapping in Dynamic Environments
S. Badalkhani, R. Havangi,
Volume 17, Issue 1 (March 2021)
Abstract
Even when simultaneous localization and mapping (SLAM) solutions have been broadly developed, the vast majority of them relate to a single robot performing measurements in static environments. Researches show that the performance of SLAM algorithms deteriorates under dynamic environments. In this paper, a multi-robot simultaneous localization and mapping (MR-SLAM) system is implemented within a dynamic environment. A probabilistic approach based on extended Kalman filter (EKF) is proposed to detect moving landmarks and consequently improve the performance of SLAM in dynamic environments. The expected landmark area (ELA) is introduced. This concept allows identifying and filtering the moving landmarks. Several experiments are performed varying the speed and number of moving landmarks within the environment to investigate the effect of dynamism level and landmark speed on. The root mean square error (RMSE) is used as a form of measuring the performance of the algorithm. Results show moving landmarks, degrade the performance of classical EKF-SLAM. However, the proposed method is robust to environmental changes and is less affected by the increasing speed of the moving landmarks.

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© 2022 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.