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Sparsity self-adaptive DOA estimation method and system based on pseudo-inverse perception dictionary

A sparsity and self-adaptive technology, applied in the field of signal processing, can solve problems such as limited scope of application, no iterative threshold, and inability to take into account the performance and resolution of signal restoration and reconstruction, etc., to achieve the effect of expanding the scope of application and eliminating threshold errors

Pending Publication Date: 2020-12-15
INST OF ACOUSTICS CHINESE ACAD OF SCI
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Problems solved by technology

[0005] 1) There is no clear selection criterion for the iterative threshold used, and it is often selected based on experience, resulting in signal reconstruction and DOA estimation performance heavily dependent on the selection accuracy of the threshold, and the scope of application is limited;
[0006] 2) There is a problem that the performance of signal restoration and reconstruction and the resolution of DOA estimation cannot be balanced
When the angle grid segmentation is too fine, the restoration and reconstruction effect will be worse, and when the angle grid segmentation is too sparse, the DOA estimation resolution will be reduced

Method used

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  • Sparsity self-adaptive DOA estimation method and system based on pseudo-inverse perception dictionary
  • Sparsity self-adaptive DOA estimation method and system based on pseudo-inverse perception dictionary
  • Sparsity self-adaptive DOA estimation method and system based on pseudo-inverse perception dictionary

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Embodiment 1

[0083] like figure 2 , An embodiment of the present invention proposed a method of adaptive orthogonal matching pursuit based on a pseudo inverse perceptual dictionary sparsity, the specific steps are as follows:

[0084] Input: M × N dimensional matrix A, the observation vector y, step F = 1;

[0085] 1. Initialization: initial iterations t = 1; r residual 0 = Y; matching matrix Atoms selected amount of the first stage L = F, the iteration index t = 1, the phase index j = 1;

[0086] 2. Select atoms: Calculation | [Phi] H rim t-1 |, Where Φ is the pseudo-inverse perception dictionary. Select the maximum value of the product L columns atoms, and these constitute a column number set J 0 ;

[0087] 3. Update matching matrix: Let Λ t = Λ t-1 ∪J 0 , A t = A t-1 ∩a t ;

[0088] 4. Update sparse coefficients:

[0089] 5. atoms backtracking: Select θ t L largest item, and re-form a new matrix A according to a corresponding matching maximum atoms selected items L tL ;

[0090] 6. backtr...

Embodiment 2

[0106] Based on the above method, Embodiment 2 of the present invention proposes a sparse-based adaptive DOA estimation system based on a pseudo-inherent dictionary.

[0107] The system includes M yuan uniform line array and estimation module;

[0108] The M element homogeneous line is used to receive K narrowband signal sources to obtain an observation vector of the noiseless field;

[0109] The estimation module is used to use the observed vector as an initial residual, and the sparse-based adaptive orthogonal matching method based on the pseudo-inverse perception dictionary is used. By continuous iterative steps, the residual value corresponding to this stage is obtained according to different stages. The contrast of the difference converges, obtains the minimum residual value, and thus obtains the corresponding sparse coefficient, and thereby obtains the direction of non-zero elements in the k narrowband signal source.

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Abstract

The invention discloses a sparse adaptive DOA estimation method and system based on a pseudo-inverse perception dictionary, and the method comprises the steps: receiving K narrow-band signal sources through an M-element uniform linear array, and obtaining an observation vector of a noise-free field; taking the observation vector as an initial residual error, adopting a sparsity adaptive orthogonalmatching pursuit method based on a pseudo-inverse perception dictionary, obtaining a residual error value corresponding to the current stage by continuously iterating the step length, obtaining a minimum residual error value according to the convergence comparison of the residual error values of different stages, and obtaining a corresponding sparse coefficient; and obtaining the directions of the non-zero elements in the K narrowband signal sources. According to the method, the convergence value of each step iteration is compared, the step corresponding to the minimum convergence value is used as the sparsity, the threshold error is effectively eliminated, and the application range is expanded; the advantages of the perception dictionary are combined, and a new design criterion of the pseudo-inverse perception dictionary is provided, so the error influence caused by atom strong correlation under the condition of high resolution is overcome, and the direction of the incoming wave is positioned more accurately.

Description

Technical field [0001] The present invention relates to signal processing, and particularly relates to a method and system for adaptive DOA estimation based on sparsity of the pseudo-inverse perceptual dictionary. Background technique [0002] In recent years, compressive sensing theory has been studied in depth, which is likely to break out the Nyquist sampling rate of sparse signal reconstruction, more and more scholars to apply them to the signal processing field. Since the target signal throughout the airspace grid number is limited, its sparsity own model is consistent with the theory of compressed sensing, it has a large number of scholars began to study the estimates based on compressed sensing DOA. This class model of coherent signals can be processed directly and does not require a large number of snapshots you can make a correct estimate of the signal, has a good real-time processing capabilities. [0003] Greedy algorithm is compressed sensing algorithms most common re...

Claims

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Application Information

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IPC IPC(8): H03M7/30
CPCH03M7/55
Inventor 郝程鹏钟一宸闫晟朱东升徐达
Owner INST OF ACOUSTICS CHINESE ACAD OF SCI
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