Far-field narrow-band DOA estimation method based on covariance matrix sparse representation

A technology of covariance matrix and sparse representation, which is applied to direction finders, direction finders using radio waves, measuring devices, etc., which can solve the problems of estimation performance deterioration and height correlation.

Inactive Publication Date: 2014-08-13
UNIV OF ELECTRONICS SCI & TECH OF CHINA
View PDF3 Cites 26 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, these algorithms based on sparse representation usually assume that all the true DOA are located on a pre-set discretized grid, that is, the grid matching model, which leads to such algorithms when the DOA is not on the grid Estimated performance deteriorates sharply when running
On the other hand, although a denser grid can theoretically reduce the reconstruction error, too dense a discretized grid will make the over-complete dictionary atoms highly correlated

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Far-field narrow-band DOA estimation method based on covariance matrix sparse representation
  • Far-field narrow-band DOA estimation method based on covariance matrix sparse representation
  • Far-field narrow-band DOA estimation method based on covariance matrix sparse representation

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0049] The simulation condition of embodiment 1 and embodiment 2 is the model of mesh matching, so estimated value That is, the final direction of arrival estimated value, without correction. The simulation conditions of Embodiment 3 and Embodiment 4 are mesh mismatch models. In the embodiment, root mean square error (RMSE) is used to evaluate the performance of each algorithm, which is defined as: RMSE = 1 Mon Σ m = 1 Mon 1 K Σ k = 1 K ( θ ^ k , m - ...

Embodiment 2

[0059] In the case of grid matching, the root mean square error of the estimated value of the present invention changes with the number of snapshots:

[0060] The receiving array that embodiment 1 adopts is as attached figure 2The half-wavelength uniform linear array consisting of 8 array elements is shown. The reference array element is the array element antenna numbered 1. Four signal sources with the same power are incident on the array according to the incident direction [-35°, -10°, 15°, 40°]. In order to make the corners of the incident direction on the grid, the discretization grid is set to {-90°,-89°,...,89°} with an interval of 1°. The reference signal-to-noise ratio SNR is fixed at 10dB. The number of snapshots ranges from 100 to 600, with an interval of 100, and 1000 Monte Carlo experiments are performed for each snapshot.

[0061] DOA estimation method in embodiment 2 comprises the following steps:

[0062] Obtain the covariance matrix R according to the arr...

Embodiment 3

[0067] In the case of grid mismatch, the root mean square error of the estimated value of the present invention varies with the signal-to-noise ratio simulation:

[0068] The receiving array that embodiment 3 adopts is as attached figure 2 The half-wavelength uniform linear array consisting of 8 array elements is shown. The reference array element is the array element antenna numbered 1. Two signal sources with the same power are incident on the array according to the incident direction [-14.5°, 36.3°]. In order to prevent the incident direction angle from falling on the grid, the discretization grid is set to {-90°,-88°,...,88°} with an interval of 2°. The number of sampling snapshots is 200. The reference signal-to-noise ratio SNR was varied from -4dB to 20dB with an interval of 4dB, and 1000 Monte Carlo experiments were performed for each SNR.

[0069] DOA estimation method in embodiment 3 comprises the following steps:

[0070] Obtain the covariance matrix R accordin...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention provides a far-field narrow-band DOA estimation method based on covariance matrix sparse representation. Based on the sparsity on a space domain in the wave arrival direction, a covariance matrix is changed into a sparse representation model, under a module with a matched gridding, a sparse space power spectrum is solved through an optimization minimization method, and the point, corresponding to a support set of the power spectrum, on the gridding is the wave arrival direction angle obtained through estimation. For the condition that the actual wave arrival direction angle is not on the gridding, namely under a model with the mismatched gridding, a first-order Taylor expansion method is adopted to approach the guide vector of the actual wave arrival direction angle, and then the point, obtained through estimation, on the gridding is corrected through a least-square method to achieve higher estimation accuracy. The far-field narrow-band DOA estimation method based on covariance matrix sparse representation can achieve high-accuracy DOA estimation performance on the rough gridding.

Description

technical field [0001] The invention belongs to the field of array signal processing, and mainly relates to far-field narrowband DOA estimation. Background technique [0002] Direction of Arrival (DOA) estimation has always been an important research field in array signal processing, and it has a wide range of applications in radar, sonar, wireless communication, electronic countermeasures and reconnaissance. How to realize DOA estimation quickly and with high precision has always been the direction of continuous research and efforts in array signal processing. The classic algorithms include: Multiple Signal Classification (MUSIC) algorithm, Rotation Invariant Subspace (Estimation of Signal Parameters via Rotational Invariance Technique, ESPRIT) algorithm and other subspace algorithms and maximum likelihood estimation algorithm (Maximum Likelihood Estimation) Likelihood, ML) and so on. However, although the DOA estimation method based on the subspace theory can achieve sup...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G01S3/12
CPCG01S3/46
Inventor 费晓超罗晓宇甘露廖红舒
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap