Greedy Orthogonal Matching Pursuit for sparse target detection and counting in WSN. Jellali, Z., Atallah, L. N., & Cherif, S. In *2014 22nd European Signal Processing Conference (EUSIPCO)*, pages 2250-2254, Sep., 2014.

Paper abstract bibtex

Paper abstract bibtex

The recently emerged Compressed Sensing (CS) theory has widely addressed the problem of sparse targets detection in Wireless Sensor Networks (WSN) in the aim of reducing the deployment cost and energy consumption. In this paper, we apply CS approach for both sparse events recovery and counting. We first propose a novel Greedy version of the Orthogonal Matching Pursuit (GOMP) algorithm allowing to account for the decomposition matrix non orthogonality. Then, in order to reduce the GOMP computational load, we propose a two-stages version of GOMP, the 2S-GOMP, which separates the events detection and counting steps. Simulation results show that the proposed algorithms achieve a better tradeoff between performance and computational load when compared to the recently proposed GMP algorithm and its two stages version denoted 2S-GMP.

@InProceedings{6952810, author = {Z. Jellali and L. N. Atallah and S. Cherif}, booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)}, title = {Greedy Orthogonal Matching Pursuit for sparse target detection and counting in WSN}, year = {2014}, pages = {2250-2254}, abstract = {The recently emerged Compressed Sensing (CS) theory has widely addressed the problem of sparse targets detection in Wireless Sensor Networks (WSN) in the aim of reducing the deployment cost and energy consumption. In this paper, we apply CS approach for both sparse events recovery and counting. We first propose a novel Greedy version of the Orthogonal Matching Pursuit (GOMP) algorithm allowing to account for the decomposition matrix non orthogonality. Then, in order to reduce the GOMP computational load, we propose a two-stages version of GOMP, the 2S-GOMP, which separates the events detection and counting steps. Simulation results show that the proposed algorithms achieve a better tradeoff between performance and computational load when compared to the recently proposed GMP algorithm and its two stages version denoted 2S-GMP.}, keywords = {compressed sensing;greedy algorithms;iterative methods;matrix decomposition;signal detection;wireless sensor networks;greedy orthogonal matching pursuit algorithm;sparse target detection;WSN;compressed sensing theory;CS theory;wireless sensor networks;deployment cost;energy consumption;CS approach;sparse event recovery;GOMP algorithm;decomposition matrix nonorthogonality;GOMP computational load reduction;2S-GOMP;event detection;Matching pursuit algorithms;Signal to noise ratio;Compressed sensing;Complexity theory;Vectors;Event detection;Wireless sensor network;rare events detection;Compressed Sensing}, issn = {2076-1465}, month = {Sep.}, url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569924665.pdf}, }

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