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Open AccessArticle

Energy-Level Jumping Algorithm for Global Optimization in Compressive Sensing-Based Target Localization

1
School of Computer Science and Technology, Nanjing Tech University, Nanjing 211816, China
2
School of Physical and Mathematical Sciences, Nanjing Tech University, Nanjing 211816, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(11), 2502; https://doi.org/10.3390/s19112502
Received: 19 March 2019 / Revised: 24 May 2019 / Accepted: 28 May 2019 / Published: 31 May 2019
(This article belongs to the Section Sensor Networks)
Target localization is one of the essential tasks in almost applications of wireless sensor networks. Some traditional compressed sensing (CS)-based target localization methods may achieve low-precision target localization because of using locally optimal sparse solutions. Solving global optimization for the sparse recovery problem remains a challenge in CS-based target localization. In this paper, we propose a novel energy-level jumping algorithm to address this problem, which achieves high-precision target localization by solving the globally optimal sparse solution of l p -norm ( 0 < p < 1 ) minimization. By repeating the process of energy-level jumping, our proposed algorithm establishes a global convergence path from an initial point to the global minimizer. Compared with existing CS-based target localization methods, the simulation results show that our localization algorithm obtain more accurate locations of targets with the significantly reduced number of measurements. View Full-Text
Keywords: target localization; compressive sensing; global optimization; locally optimal sparse solution; globally optimal sparse solution; energy-level jumping target localization; compressive sensing; global optimization; locally optimal sparse solution; globally optimal sparse solution; energy-level jumping
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Wang, T.; Guan, X.; Wan, X.; Liu, G.; Shen, H. Energy-Level Jumping Algorithm for Global Optimization in Compressive Sensing-Based Target Localization. Sensors 2019, 19, 2502.

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