CS Based Acoustic Source Localization and Sparse Reconstruction Using Greedy Algorithms

Joseph, Jinu; Kumar, Neelesh; Devi, M Rema; Kumar, Krishan

doi:10.1109/icacc.2015.22

Cited by 5 publications

(1 citation statement)

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“…A sparse solution modelling the locations of targets was then reconstructed by the basis pursuit (BP) algorithm [13] with high computational complexity. To reduce computational cost, the orthogonal matching pursuit (OMP) algorithm [14] in a greedy manner was applied to estimate the locations of targets by l0-norm minimization. Another greedy matching pursuit (GMP) algorithm [15] tried to select an optimal 1-sparse vector at each iteration (i.e., the location of a target), which inevitably led to much localization time.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Guan

Wan

et al. 2019

Sensors

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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.

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