Volume 19, Issue 1 (March 2023)                   IJEEE 2023, 19(1): 2165-2165 | Back to browse issues page

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Adhami M H, Ghazizadeh R. Multiple Target Tracking in Wireless Sensor Networks Based on Sensor Grouping and Hybrid Iterative-Heuristic Optimization. IJEEE 2023; 19 (1) :2165-2165
URL: http://ijeee.iust.ac.ir/article-1-2165-en.html
Multiple Target Tracking in Wireless Sensor Networks Based on Sensor Grouping and Hybrid Iterative-Heuristic Optimization
M. H. Adhami , R. Ghazizadeh
Abstract:   (1921 Views)
A novel hybrid method for tracking multiple indistinguishable maneuvering targets using a wireless sensor network is introduced in this paper. The problem of tracking the location of targets is formulated as a Maximum Likelihood Estimation. We propose a hybrid optimization method, which consists of an iterative and a heuristic search method, for finding the location of targets simultaneously. The Levenberg-Marquardt (LM) algorithm is used for iterative search, while the Particle Swarm Optimization (PSO) is used for the heuristic search. We use the maximum sensors separating distance-grouping algorithm (G-MSSD), which was introduced in our previous work, to generate initial guesses for search algorithms. The estimates of both methods are compared and the best one is selected as the final estimation. We demonstrate the accuracy and performance of our new tracking method via simulations and compare our results with the Gauss-Newton (GN) method.
Keywords: Maximum Likelihood , Multi-Target Tracking , Multiple Target Tracking , Simultaneous Tracking , Wireless Sensor Network
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  • A new hybrid search method consists of a heuristic search algorithm (PSO) and an iterative search method (Gauss-Newton);
  • A new concept for reducing the search region of the PSO algorithm, to decrease the complexity and energy consumption.

Type of Study: Research Paper | Subject: Communication Systems
Received: 2021/04/30 | Revised: 2023/02/23 | Accepted: 2022/09/12
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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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.