A Study on Matching Pursuit Based on Genetic Algorithm

Gao, Qiang; Duan, Chang‐Kui; Xiangbo, Fang; Liu, Benchao

doi:10.1109/icmtma.2011.860

Cited by 4 publications

(1 citation statement)

References 3 publications

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“…MP algorithm has already given the range of discretization atomic parameter γ=false(s,u,ν,wfalse) and the calculation formula of optimal atoms before finding the optimal atom. Therefore, applying GA to the MP algorithm can change the searching process of finding the optimal atom from the whole dictionary to the randomly generated smaller subset of the atomic dictionary, which can reduce the amount of computation and improve the matching speed of the optimal atom greatly [23].…”

Section: Composite Chaotic-genetic Mp Reconstruction Methodsmentioning

confidence: 99%

A Random Compressive Sensing Method for Airborne Clustering WSNs

Zhou

Jing

Huang

2015

International Journal of Distributed Sensor Networks

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In order to reduce the energy consumption of the cluster members in WSNs, this paper proposes a random compressive sensing data acquisition scheme for airborne clustering WSNs. In this scheme, hardware resource limited cluster members sample the input signals with random sampling sequence and then transmit the sampling signals to the cluster head or Sink to reconstruct. Aimed at improving the reconstruction performance of this scheme, this paper puts forward a new MP reconstruction method based on composite chaotic-genetic algorithm, which combines the excellent local searching characteristics of chaos theory with the powerful global search ability of genetic algorithm. The experimental result shows that this scheme is very suitable for the hardware resource limited clustering WSNs. On the one hand, the reconstruction precision of the composite chaotic-genetic MP method can reach a magnitude of 10 −15 , and the average search speed is about 37 time that of the MP reconstruction method, which can effectively improve the reconstruction performance of the cluster head or Sink; on the other hand, by diminishing the sampling frequency to 1/8 of the original sampling frequency, the random compressive sensing technique can dramatically reduce the sampling quantity and the energy consumption of the cluster members, with the reconstruction precision reaching a magnitude of 10 −7 .

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