Abstract:
Sparse measurement matrices with very few randomly selected +1/-1 non-zero elements are designed for use with Bayesian Approximate Message Passing as a compressed sensing...Show MoreMetadata
Abstract:
Sparse measurement matrices with very few randomly selected +1/-1 non-zero elements are designed for use with Bayesian Approximate Message Passing as a compressed sensing recovery algorithm. Simulations show that such sparse matrices, which allow for large savings in storage and computation time, can achieve a recovery performance that is as good as the benchmark given by random Gaussian matrices.
Date of Conference: 18-20 February 2020
Date Added to IEEE Xplore: 20 May 2020
Print ISBN:978-3-8007-5200-3
Conference Location: Hamburg, Germany