2012/11/28 by Thakshila Wimalajeewa, Wimalajeewa, Thakshila, Pramod K. Varshney +1
Computer Science · Engineering · #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #FOS: Mathematics #Information Theory (cs.IT) #Microwave Imaging and Scattering Analysis #Numerical Analysis (math.NA) #Sparse and Compressive Sensing Techniques
paper · pdf · doi:10.48550/arxiv.1211.6719
openalex publication_date 2012/11/28 · openalex created_date 2022/10/04 · openalex updated_date 2026/07/28
In this paper, we consider the problem of collaboratively estimating the\nsparsity pattern of a sparse signal with multiple measurement data in\ndistributed networks. We assume that each node makes Compressive Sensing (CS)\nbased measurements via random projections regarding the same sparse signal. We\npropose a distributed greedy algorithm based on Orthogonal Matching Pursuit\n(OMP), in which the sparse support is estimated iteratively while fusing\nindices estimated at distributed nodes. In the proposed distributed framework,\neach node has to perform less number of iterations of OMP compared to the\nsparsity index of the sparse signal. Thus, with each node having a very small\nnumber of compressive measurements, a significant performance gain in support\nrecovery is achieved via the proposed collaborative scheme compared to the case\nwhere each node estimates the sparsity pattern independently and then fusion is\nperformed to get a global estimate. We further extend the algorithm to estimate\nthe sparsity pattern in a binary hypothesis testing framework, where the\nalgorithm first detects the presence of a sparse signal collaborating among\nnodes with a fewer number of iterations of OMP and then increases the number of\niterations to estimate the sparsity pattern only if the signal is detected.\n