Compressed Sensing Using Deterministic Measurement Matrix In WSN
Abstract: Compressive sensing is a sampling method which provides a new approach to efficient signal compression and
recovery by exploiting the fact that a sparse signal can be suitably reconstructed from very few measurements.
One of the most concerns in compressive sensing is the construction of the sensing matrices. While random sensing matrices
have been widely studied, only a few deterministic sensing matrices have been considered. Originated as a technique for
finding sparse solutions to underdetermined linear systems, compressed sensing (CS) has now found widespread applications
in both Signal processing and Communication communities, ranging from data compression, data acquisition, inverse
Problems, and channel coding. An essential idea of CS is to explore the fact that most natural phenomena are Sparse or
compressible in some appropriate basis. By acquiring a relatively small number of samples in the “sparse” domain, the signal
of interest can be reconstructed with high accuracy through well-developed optimization procedures.
These matrices are highly desirable on structure which allows fast implementation with reduced storage requirements. In this
paper, a survey of deterministic sensing matrices for compressive sensing is presented. Some recent results on construction
of the deterministic sensing matrices are discussed.