Deep learning direction finding method based on phase optimization

Abstract

The invention discloses a deep learning direction finding method based on phase optimization. The method specifically comprises the following steps of constructing a received signal model of an arrayantenna; taking the received signal of one antenna in the array antenna as a reference, normalizing the received signals of other antennas, and calculating the received signal phase of each antenna after normalization; and optimizing the received signal phase of each antenna by adopting an angle optimization method to obtain an optimized phase, constructing a neural network model based on deep learning, taking the optimized phase as the input of the constructed neural network model, and taking the output of the neural network model as the estimated DOA. The method is advantaged in that the phase relation of the signals between the antennas is analyzed through the array signal model, the periodic influence is adjusted through the phase relation of the array signals, the optimized phase relation serves as the input of the deep learning neural network, the neural network is learned through training, and finally effective direction finding of the signals under the condition of low complexity is achieved.

Description

technical field [0001] The invention relates to a deep learning direction finding method based on phase optimization, which belongs to the technical field of array signal processing. Background technique [0002] Estimation of the angle of arrival of signals such as electromagnetic waves and sound waves plays a key role in the fields of radar, sonar, and wireless communication. By estimating the direction of arrival of the signal, it can help subsequent beamforming optimization and target positioning. Commonly used direction finding methods are generally divided into traditional Fourier transform methods and super-resolution methods. In the Fourier transform method, the spatial sampling of the array signal is equivalent to the time domain sampling of the signal, so the direction finding problem is equivalent to It is a spectrum estimation problem, so the spatial spectrum information can be obtained through the Fourier transform of the array received signal, and then the dire...

AI Technical Summary

Problems solved by technology

[0007] The technical problem to be solved by the present invention is to provide a deep learning direction finding method based on phase optimization, which solves the problems of high complexity and poor real-time performance in the existing direction finding methods, by optimizing the input signal of the neural network, combined with deep learning theory to achieve high real-time direction finding performance

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