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A hybrid parameter estimation method under the framework of multi-channel compressed sensing

A technology of compressed sensing and mixed parameters, applied in electrical components, code conversion, etc., can solve the problem of low efficiency of mixed parameters, and achieve the effect of reducing the amount of calculation and the amount of calculation.

Active Publication Date: 2016-01-20
HARBIN INST OF TECH
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  • Application Information

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Problems solved by technology

[0003] In order to solve the problem that the mixed signal must be reconstructed first and the efficiency of estimating the mixed parameters is low in the general method under the existing multi-channel compressed sensing framework for estimating the mixed parameters, the present invention provides a mixed parameter estimation method under the multi-channel compressed sensing framework

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  • A hybrid parameter estimation method under the framework of multi-channel compressed sensing
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  • A hybrid parameter estimation method under the framework of multi-channel compressed sensing

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specific Embodiment approach 1

[0030] Specific implementation mode 1. Combination figure 2 This specific embodiment will be described. A hybrid parameter estimation method under a multi-channel compressed sensing framework, which includes the following steps:

[0031] Step 1: Collect mixed signal x i The compressed observation signal of y is i , 1≤i≤m;

[0032] where x i is the i-th mixed signal, m is the number of mixed signals, mixed signal x i The length of is N, the observed signal y i The length of is M, that is And M<

[0033] Suppose: the anti-mixing matrix W is a real array of m rows and m columns, that is

[0034] The measurement matrix Ф is a real number matrix with M rows and N columns, namely

[0035] The initial value of the algorithm iteration number l is 1, the total number of iterations is L, and the initial value of the anti-mixing matrix is ​​W 0 , the update step size is η;

[0036] Step 2, select any non-linear function g(·) from the monotonically increasing functions...

specific Embodiment approach 2

[0047] Embodiment 2. This embodiment is different from Embodiment 1 in that the m mixed signals x i in the form of , then m observed signals y i in the form of .

specific Embodiment approach 3

[0048] Embodiment 3. The difference between this embodiment and Embodiment 1 or 2 is that the mixed signal x i for:

[0049] x 1 ( t ) = a 11 s 1 ( t ) + a 21 s 2 ( t ) + · · · + a m 1 ...

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Abstract

The invention discloses a hybrid parameter estimation method for use under a multi-channel compressed sensing framework, which relates to the technical field of multi-channel compressed sensing, and is used for solving the problem of low source signal reconstruction efficiency caused by the fact that reconstruction of a hybrid signal must be firstly finished in the conventional hybrid parameter estimation calculation. The method comprises the following steps of: acquiring a compressed observation signal yi of a hybrid signal xi; selecting a non-linear function g(.), wherein the input of the function g(.) is yWl while the output is Y; calculating the entropy of the Y; calculating the gradient of the entropy H(Y), and updating a reverse hybrid matrix Wl+1 along the gradient direction of the entropy H(Y) to make the entropy H(Y) increase gradually, wherein a formula for updating the reverse hybrid matrix W is: Wl=Wl-1+eta*nabla h adding 1 to the value of an iteration number l, namely, l=l+1; judging whether the iteration number l is larger than a set total iteration number t; and calculating an estimated value of a hybrid matrix A according to a reverse hybrid matrix Wt obtained by performing t times of iteration update. The hybrid parameter estimation method can be applied widely to the calculation of hybrid parameter estimation.

Description

technical field [0001] The invention relates to the technical field of multi-channel compressed sensing. Background technique [0002] Traditional signal acquisition is based on the Nyquist sampling theorem, that is, when the sampling rate of the signal must be greater than or equal to twice the highest frequency of the signal, the source signal can be recovered from the collected data without distortion. As people's demand for information increases, the bandwidth of the signal increases. When the acquisition of the signal is still based on the Nyquist sampling theorem, it will bring great challenges to signal sampling and data storage. The new sampling theory proposed in 2004 - Compressed Sensing (CS) points out that when the signal satisfies sparsity, the signal can be observed at a speed much lower than the Nyquist sampling rate, and then through a suitable reconstruction algorithm from The source signal is recovered from a small number of projected values ​​of the signa...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H03M7/30
Inventor 付宁徐红伟乔立岩于伟殷聪如
Owner HARBIN INST OF TECH