Reconfiguration method for compression sensing signal

Abstract

The invention provides a reconfiguration method for a compression sensing signal, which comprises the following steps: A) calculating an inner product (g1=phi Ty) of a signal y subjected to compression sensing treatment and each row of a sensing matrix phi, and taking the inner product as an inner product result of first iteration; B) judging if stopping iteration according to the inner product result gt of the iteration; if not, searching an index value lambda t corresponding to an element with the maximum absolute value from the inner product result gt of the iteration, adding the index value lambda t into an index set, calculating the inner product of the next iteration and starting the next iteration process, and returning to the step B, wherein t is the iteration index value and is the No. lambda t row of the matrix X=phi T phi; and if stopping, forming a matrix phi lambda by rows of the sensing matrix corresponding to different index values in the present index set, and reconfiguring the compression sensing signal according to the formed matrix phi lambda and the signal y, wherein the rows in the matrix phi lambda are arrayed according to the sequence of the index values from low to high, and lambda is the set composed of different index values in the present index set. According to the reconfiguration method provided by the invention, the calculating time and resource are saved.

Description

technical field [0001] The invention relates to a signal processing technology in a communication system, in particular to a reconstruction method of a compressed sensing signal. Background technique [0002] Compressive Sensing is a theory of signal reconstruction using sparse or compressible signals. The theory points out that: for a compressible signal, the signal can be non-adaptively measured and coded at a rate much lower than the Nyquist sampling rate. The measured value is not the signal itself, but a high-dimensional to low-dimensional projection value. From a mathematical point of view, each measurement value is a combination function of each sample signal under the traditional theory, that is, a measurement value already contains a small amount of information of all sample signals. [0003] The outstanding advantage of compressed sensing signals is that for signals that can be sparsely represented, it can combine traditional data acquisition and data compression ...

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Problems solved by technology

[0012] but minimum l 0 The norm problem is an NP-hard problem that requires exhaustive enumeration of all non-zero values ​​in s permutations are possible, so it is impossible to solve

The reconstruction effect of this series of metho

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