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DOA (Direction-of-Arrival) estimation method based on grid-less compressive sensing in background of super-Gaussian noise

A technology of noise background and compressed sensing, which is applied in radio wave direction/deviation determination systems, direction finders using radio waves, etc., can solve problems that have not yet been discovered, and achieve the effect of easy implementation, high precision, and good application prospects

Active Publication Date: 2018-03-20
ARMY ENG UNIV OF PLA
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Problems solved by technology

At present, no DOA estimation technology based on mesh-free compressive sensing theory has been found in the super-Gaussian noise environment

Method used

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  • DOA (Direction-of-Arrival) estimation method based on grid-less compressive sensing in background of super-Gaussian noise
  • DOA (Direction-of-Arrival) estimation method based on grid-less compressive sensing in background of super-Gaussian noise
  • DOA (Direction-of-Arrival) estimation method based on grid-less compressive sensing in background of super-Gaussian noise

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Embodiment Construction

[0030] combine figure 1 , to illustrate the antenna array signal model of the present invention: apply gridless compressed sensing technology to DOA estimation. Consider such a problem: there are K targets with unknown angles randomly distributed in the region θ∈[-90°,90°]. In order to determine their angles, the present invention receives signals through a linear antenna array (Uniform Linear Array, ULA) with the number of elements M, and then realizes DOA estimation.

[0031] The present invention is based on a uniform linear array, wherein each array element is an omnidirectional antenna, assuming that the number of array elements is M, and the distance between array elements is d. Assume K narrow-band far-field point source signals from the direction θ k , k=1,...,K, incident to the antenna array, namely θ k is the angle of incidence of the signal. Then at a certain moment, the signal received by the antenna array is expressed as

[0032]

[0033] where y=[y1,...,...

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Abstract

The invention discloses a DOA (Direction-of-Arrival) estimation method based on grid-less compressive sensing in the background of super-Gaussian noise, which comprises the steps of firstly determining an antenna array signal model, that is, receiving signals through a linear antenna array with the number of array elements being M and the spacing between the array elements being equal, selecting anorm of lp to perform constraint according a noise distribution type measured in the actual environment, then determining a function expression for solving a noiseless signal x, describing a problemof recovering the noiseless signal x into a problem of minimizing an atomic norm, and solving by adopting a theoretical method of semi-definite programming; then solving a primitive solution accordingto an ADMM algorithm, then solving a dual solution z^, finally solving the DOA, enabling the DOA to more approach to an angle support set of the primitive signal so as to complete DOA estimation. According to the invention, a noise item is effectively constrained by adopting an appropriate norm through using statistical characteristics of the noise, and finally the angle support set of the primitive signal is solved by using a relation between the primitive solution and the dual solution so as to achieve effective and accurate DOA estimation.

Description

technical field [0001] The invention belongs to the direction-of-arrival (DOA) estimation technology of Grid-less Compressive Sensing (Grid-less Compressive Sensing, GCS), in particular, a method based on Grid-less Compressive Sensing under the background of super-Gaussian noise. DOA estimation method. Background technique [0002] As we all know, direction of arrival estimation plays an irreplaceable role in communication, voice, radar, sonar and many other aspects. Due to its unique advantages, compressive sensing technology has been widely used in spatial spectrum estimation problems. For DOA estimation, compressive sensing technology utilizes the sparsity of the signal in the space domain, and only needs a small amount of observation data to realize the reconstruction of the signal, and the signal to noise ratio (Signal to Noise Rate, SNR) is low, the source When the correlation is high, the algorithm is still robust. However, traditional super-resolution algorithms, ...

Claims

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Application Information

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IPC IPC(8): G01S3/14
CPCG01S3/14
Inventor 郭艳张星航李宁杨思星余东平王萌荣凤娟
Owner ARMY ENG UNIV OF PLA
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