Direction of arrival estimation method based on grid partial refinement

A direction of arrival estimation and grid technology, applied in computing, computer components, special data processing applications, etc., can solve the problem of large amount of calculation of the algorithm, and achieve the goal of ensuring estimation accuracy, reducing computational complexity, and reducing the total number of Effect

Active Publication Date: 2019-05-21
GUILIN UNIV OF ELECTRONIC TECH
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Problems solved by technology

The proposal of compressive sensing theory has made a major breakthrough in the DOA estimation problem. Although the DOA estimation algorithm based on the grid model or the out-of-grid model has certain advantages over the traditional

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  • Direction of arrival estimation method based on grid partial refinement
  • Direction of arrival estimation method based on grid partial refinement
  • Direction of arrival estimation method based on grid partial refinement

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[0026] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to specific examples and accompanying drawings.

[0027] A DOA estimation method based on partial mesh refinement, such as figure 1 As shown, it specifically includes the following steps:

[0028] Step 1: Parameter initialization:

[0029] Based on the initially divided grid points, an off-grid Direction of Arrival (DOA) estimation model for the current observation data is constructed.

[0030] In order to transform DOA estimation into a sparse reconstruction problem, we generally make is the grid points divided at equal intervals in the angle space [-90°, 90°], where N represents the number of grid points.

[0031] For the case of mesh mismatch, the Taylor expansion method is used to calculate the steering vector, namely:

[0032]

[0033] in, represents the distance from θ k near...

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Abstract

The invention discloses a direction of arrival estimation method based on grid partial refinement. Through learning and fission processes, in the fission process, new grid points are generated to refine the grid. The learning process is continuously close to the direction of arrival, grids near the real direction of arrival are finely divided, and grid areas far away from the direction of arrivalare coarsely divided, so that partial refinement of the grids is realized, the estimation precision is ensured, and compared with a previous off-grid DOA estimation algorithm, the number of grid points is greatly reduced, and the calculation amount is reduced accordingly. According to the method, the information source number does not need to serve as a priori, grid division is as sparse as possible, the grid number is reduced, therefore, the calculation complexity is reduced, and the algorithm consumes less time. And under the condition of very sparse initial lattice point division, the estimation precision of the algorithm is ensured through minimum interval threshold self-definition.

Description

technical field [0001] The invention relates to the technical field of array signal processing, in particular to a direction-of-arrival estimation method based on grid partial refinement. Background technique [0002] Direction-of-arrival (DOA) estimation technology is widely used in radar, sonar, meteorology and many other fields. Among the traditional DOA estimation techniques, the most well-known algorithms are MUSIC and ESPRIT, but the performance of these traditional algorithms is restricted by many factors, such as high signal-to-noise ratio and large number of snapshots. The proposal of compressive sensing theory has made a major breakthrough in the DOA estimation problem. Although the DOA estimation algorithm based on the grid model or the out-of-grid model has certain advantages over the traditional algorithm, these sparse signal reconstruction models are based on the spatial angle grid Equally spaced division, in order to pursue the estimation accuracy, the grid i...

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Application Information

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IPC IPC(8): G06F17/50G06K9/62
Inventor 蒋留兵荣书伟车俐姜风伟宋占龙周小龙
Owner GUILIN UNIV OF ELECTRONIC TECH
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