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Improved coherent signal DOA estimation method based on subarray transfer invariance

A coherent signal, DOA technology, applied in direction finders using radio waves, radio wave direction/bias determination systems, etc. The effect of improving the estimation accuracy

Pending Publication Date: 2022-04-12
XIDIAN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] However, for the MC compensation method of ML in literature 1, MC parameters are estimated in an iterative manner, and a large number of unknown parameter optimizations make the algorithm complex and computationally intensive; while the algorithm for DOA estimation using LS criterion and subspace theory in literature 2 , which uses uncorrelated signals to calculate MC coefficients, which is not suitable for purely coherent signal source scenarios

Method used

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  • Improved coherent signal DOA estimation method based on subarray transfer invariance
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  • Improved coherent signal DOA estimation method based on subarray transfer invariance

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Effect test

Embodiment 1

[0061] See figure 1 , figure 1 It is a schematic flowchart of an improved coherent signal DOA estimation method based on subarray transfer invariance provided by an embodiment of the present invention, which includes:

[0062] Step 1: Establish a received signal model with mutual coupling effects.

[0063] First, the equidistant linear array composed of M omnidirectional array elements is divided into K overlapping sub-arrays, each sub-array contains array elements, consider L coherent far-field narrowband signals at an angle θ 1 ,θ 2 ,,θ L acting on the array, then the received signal x of the kth sub-array k (t) can be expressed as:

[0064]

[0065] in, Yes Dimensional steering vector matrix, θ=[θ 1 ,θ 2 ,…,θ L ] is the direction of arrival of l signals, s(t)=[s 1 (t), s(t) 2 …s, L (t)] T =, [s(t)αs(t 1 )…α L s,(t 2 )] T Indicates the echo signal, (·) T represents a transpose operation, is the steering vector of the lth signal, λ is the wavelengt...

Embodiment 2

[0108] In order to further verify the beneficial effects of the present invention, the performance of the present invention will be compared and described below through a simulation test.

[0109] 1. Simulation conditions:

[0110] The simulation experiment adopts an equidistant linear array with 12 array elements, a half-wavelength spacing between array elements, and a length of 3 mutual coupling matrices. In order to measure the performance of DOA estimation, the root mean square error (RMSE) of direction of arrival estimation is defined as:

[0111]

[0112] Among them, K is the number of Monte Carlo experiments.

[0113] 2. Simulation content and result analysis

[0114] 2.1 Simulation test one

[0115] Assuming that three coherent signals act on the one-dimensional equidistant linear array at angles of {-10°, 0°, 20°}, the SNR changes from -5dB to 20dB, and compares the 500 snapshots passed under different signal-to-noise ratios (via 500 Monte Carlo experiments) si...

Embodiment 3

[0121] On the basis of the first embodiment above, this embodiment provides an improved device for estimating DOA of coherent signals based on subarray transfer invariance. See Figure 4 , Figure 4 It is a schematic structural diagram of an improved coherent signal DOA estimation device based on subarray transfer invariance provided by an embodiment of the present invention, which includes:

[0122] Model building module 1, used to build a received signal model with mutual coupling effects;

[0123] The first calculation module 2 is used to calculate the covariance matrix of each sub-array according to the received signal model;

[0124] The second calculation module 3 is used to calculate the noise covariance matrix of each sub-array;

[0125] The third calculation module 4 is used to obtain the noise-free covariance matrix of the sub-array according to the covariance matrix and the noise covariance matrix of each sub-array;

[0126] The fourth calculation module 5 is us...

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Abstract

The invention discloses an improved coherent signal DOA (Direction of Arrival) estimation method based on subarray transfer invariance. The method comprises the following steps: establishing a received signal model with a mutual coupling effect; calculating a covariance matrix of each sub-array according to the received signal model; calculating a noise covariance matrix of each subarray; obtaining a noise-free covariance matrix of the subarray according to the covariance matrix and the noise covariance matrix of each subarray; calculating a smooth signal covariance matrix according to the noiseless covariance matrixes of all the subarrays; and carrying out eigenvalue decomposition on the smooth signal covariance matrix so as to estimate the DOA. According to the method, the non-uniform noise can be eliminated, so that the non-uniformity of the noise of the sensor is reduced; and the DOA of the coherent signal can be directly determined without considering mutual coupling and spectrum search between array sensors, so that the DOA estimation precision is improved, the calculation cost is reduced, and meanwhile, the method can be suitable for a pure coherent signal source scene.

Description

technical field [0001] The invention belongs to the technical field of radar signal processing, and in particular relates to an improved coherent signal DOA estimation method based on subarray transfer invariance. Background technique [0002] With the development of science and technology, adaptive arrays are widely used in the fields of mobile communication, radar, sonar, navigation, earthquake detection and medical imaging. As one of the key technologies of adaptive arrays, direction-of-arrival (DOA) estimation is a subject that many experts and scholars in the fields of information, control and communication are dedicated to research. [0003] At present, there are some classic high-resolution algorithms in the field of DOA estimation, such as the DOA estimation algorithm based on subspace, which can show good detection performance in the case of Gaussian white noise. However, in practical systems, the array manifold will inevitably be distorted by non-ideal factors suc...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01S3/14
Inventor 朱圣棋房云飞曾国强许京伟李西敏兰岚刘永军贺雄鹏
Owner XIDIAN UNIV