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Signal estimation method in non-reconstruction framework

A signal estimation and framework technology, applied in channel estimation, baseband systems, digital transmission systems, etc., and can solve problems such as slow reconstruction speed and poor accuracy

Inactive Publication Date: 2017-02-15
HARBIN INST OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to solve the problems of slow reconstruction speed and poor accuracy when the existing reconstruction algorithm is used to restore the signal. The present invention provides a signal estimation method under the framework of non-reconstruction

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  • Signal estimation method in non-reconstruction framework
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  • Signal estimation method in non-reconstruction framework

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Experimental program
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specific Embodiment approach 1

[0074] Specific implementation mode one: see figure 1 Describe this implementation mode, a signal estimation method under a non-reconfiguration framework described in this implementation mode, the method includes the following steps:

[0075] Step 1: Establish the cyclic spectrum vector of the sampled signal and sampled signal circular autocorrelation vector r x contact;

[0076] Step 2: Establish the sampling signal compression measurement value autocorrelation vector r z and sampled signal circular autocorrelation vector r x contact;

[0077] Step 3: According to the relationship obtained in Step 1 and Step 2, establish the autocorrelation vector r of the compressed measurement value of the sampled signal z and the sampled signal cyclic spectrum vector Relationship;

[0078] Step 4: Delete the cyclic spectrum vector of the sampled signal Redundant elements in , to obtain the simplified sampled signal cyclic spectrum vector

[0079] Step 5: Use the sampling sig...

specific Embodiment approach 2

[0087] Specific embodiment 2: The difference between this embodiment and the signal estimation method under the non-reconstruction framework described in the specific embodiment 1 is that the cyclic spectrum vector of the sampled signal is established in the step 1 and sampled signal circular autocorrelation vector r x The specific steps of the contact are;

[0088] Step 11: Establish the sampled signal autocorrelation matrix R according to the sampled signal x ,in,

[0089]

[0090] Sampled signal autocorrelation matrix R x Satisfy n+vx After de-redundancy, it is converted into a vector form to obtain the sampled signal circular autocorrelation vector r x ,and

[0091]

[0092] in, express yes Find the mean, E{} means find the mean, x t represents the sampled signal, Indicates the transpose of the sampled signal, r x Indicates the sampling signal autocorrelation vector, r x (n, ν) represents the autocorrelation value with index (n, ν), n represents the time,...

specific Embodiment approach 3

[0113] Specific Embodiment 3: The difference between this embodiment and the signal estimation method under the non-reconfiguration framework described in Specific Embodiment 2 is that in the step 2, the autocorrelation vector r of the compressed measurement value of the sampled signal is established. z and sampled signal circular autocorrelation vector r x The specific steps for contacting are:

[0114] Step 21: First, compress the sampled signal to obtain the compressed measurement value z t , then for the original signal x t and the compression measure z t Perform an autocorrelation operation to obtain the sampled signal autocorrelation matrix R x and sampled signal compression measurement value autocorrelation matrix R z , where z t =Ax t ,

[0115] Define the sampling signal autocorrelation matrix R x The circular autocorrelation vector r with the sampled signal x The mapping relationship between them is:

[0116] vec{R x}=PNr x (formula ten),

[0117] Defin...

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Abstract

The invention relates to a signal estimation method in a non-reconstruction framework, belonging to the field of cognitive radio parameter identification and estimation. In order to solve the problems of slow reconstruction speed and poor accuracy in using an existing reconstruction algorithm to restore a signal, the method comprises a step of establishing an association between a sample signal cyclic spectrum vector Sx(c) and a sample signal cyclic autocorrelation vector rx, a step of establishing an association between a sampling signal compression measurement value autocorrelation vector rz and the sample signal cyclic autocorrelation vector rx, a step of establishing the relation between the sampling signal compression measurement value autocorrelation vector rz and the sample signal cyclic spectrum vector Sx(c), a step of deleting the redundant elements in the sample signal cyclic spectrum vector Sx(c), and obtaining a simplified sample signal cyclic spectrum vector Sxs(c), a step of reconstructing the simplified sample signal cyclic spectrum vector Sxs(c) by using the sampling signal compression measurement value autocorrelation vector rz and an orthogonal matching tracking algorithm based on block sparse, and obtaining an original signal cyclic spectrum, and a step of extracting the parameter information of the original signal according to the original signal cyclic spectrum, and a step of extracting the parameter information of the original signal according to the original signal cyclic spectrum. The method is mainly used for extracting the signal parameter information.

Description

technical field [0001] The invention belongs to the field of cognitive radio parameter identification and estimation. Background technique [0002] According to the compressive sensing theory, its main research contents include sparsely decomposing and representing the signal, designing a suitable measurement matrix, and reconstructing the algorithm to restore the signal content. Assuming that the signal can be sparsely represented in a certain transform domain, and the sampled measurement matrix is ​​not correlated with the sparse matrix of the signal, it is possible to restore the original Signal. Reconstruction algorithms occupy a large amount of computing resources when recovering signal content. Therefore, the reconstruction algorithm has become the bottleneck of the practical application of compressed sensing, and it has become a problem that needs to be broken through and solved urgently. [0003] The signal parameter estimation problem is a reasoning problem, whic...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L25/02
CPCH04L25/0202H04L25/0242
Inventor 高玉龙王松陈艳平许康马永奎
Owner HARBIN INST OF TECH
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