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Compressed sensing-oriented block-sparse signal reconfiguring method

A signal reconstruction and compressed sensing technology, applied in the field of block sparse signal reconstruction algorithm, can solve the problems of high optimization complexity of optimization algorithm, block sparse matching pursuit algorithm, orthogonal matching pursuit algorithm over-matching, etc.

Inactive Publication Date: 2010-11-24
HARBIN INST OF TECH
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Problems solved by technology

[0009] In order to solve the problem of mixing in existing reconstruction algorithms using block sparse signals, the present invention l 2 / l 1 The optimization algorithm has high optimization complexity, block sparse matching pursuit algorithm or orthogonal matching pursuit algorithm is easy to cause over-matching phenomenon, and a block sparse signal reconstruction method oriented to compressive sensing is proposed

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specific Embodiment approach 1

[0042] Specific implementation mode one, the following combination figure 1 and figure 2 This embodiment will be specifically described. A block sparse signal reconstruction method oriented to compressed sensing, the process of the method is:

[0043] Step 1. Collect block sparse signal x The observed signal is y， observation signal y is the length of m A real vector of , that is ,

[0044] Set the initial state value of each parameter during block sparse signal reconstruction:

[0045] Among them, the block sparse signal x is the length of N , the block sparsity is K A real vector of , that is ,

[0046] Set the measurement matrix to be yes m Row N A real matrix of columns, that is ,

[0047] Preset iteration error err, block vector Group is of the form:

[0048] ,

[0049] in, N = M x d , M is a block vector Group the number of groups, d is a block vector Group The subblock length of

[0050] Set the initial value of the residual r 0 = ...

specific Embodiment approach 2

[0087] Specific embodiment 2. This embodiment is a further description of step 1 in the compressive sensing-oriented block sparse signal reconstruction method described in specific embodiment 1. In step 1, the preset iteration error err is set to 10 -5 .

specific Embodiment approach 3

[0088] Specific Embodiment 3. This embodiment is a further description of a compressed sensing-oriented block sparse signal reconstruction method described in Embodiment 1 or 2. The measurement matrix described in step 1 is follow a Gaussian distribution.

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Abstract

The invention discloses a compressed sensing-oriented block-sparse signal reconfiguring method, and particularly relates to a block-sparse signal reconfiguring algorithm, which aims to solve the problems that the optimization complexity of a mixed l2 / l1 optimization algorithm in the conventional block-sparse signal reconfiguring algorithm is relatively higher and that overmatching phenomenon is easily caused by a block-sparse matching pursuit algorithm or a block-sparse orthogonal matching pursuit algorithm. The method of the invention comprises the following steps of: correcting labels, in ameasurement matrix, of column vectors of a recovery matrix calculated in the iteration operation of the (l-1)th time by performing the iteration of the lth time, and for a block-sparse signal x with the block sparsity of K, reconfiguring the block-sparse signal x by performing the iteration for not more than K times. The method is applied to the reconfiguration of the block-sparse signal, particularly to the reconfiguration of a binary block-sparse signal.

Description

technical field [0001] The invention belongs to the technical field of compressed sensing, and in particular relates to a reconstruction algorithm for block sparse signals. Background technique [0002] The traditional signal sampling theory is based on the Nyquist sampling theorem, that is, in the process of converting an analog signal to a digital signal, in order to ensure that the information of the source signal is not lost and the source signal is restored without distortion, the sampling frequency should be greater than the analog 2 times the highest frequency in the signal. This makes digitization of broadband analog signals require a high sampling frequency, which increases the burden on physical devices. Moreover, for signals with a large amount of data, there will be higher requirements for the storage capacity and processing speed of the processor. [0003] Compressed Sensing (CS) theory is a brand-new signal sampling theory proposed in recent years. Its idea i...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H03M7/30
Inventor 付宁马云彤邓立宝曹离然彭喜元
Owner HARBIN INST OF TECH
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