Near-field signal source positioning method based on deep neural network regression model

Abstract

The invention discloses a near-field signal source positioning method based on a deep neural network regression model. The method comprises the following steps: carrying out calculation based on a covariance matrix R to obtain a feature extraction matrix r; constructing a deep neural network regression model; generating a training set of the deep neural network regression model; determining various parameters required by training the deep neural network regression model; training the constructed deep neural network regression model by using the determined parameters and the training set to obtain a trained deep neural network regression model; and inputting the feature extraction matrix r into the trained deep neural network regression mode, and outputting the direction of arrival and thedistance of the near-field signal through the deep neural network regression model to complete near-field signal source positioning. According to the method disclosed by the invention, with introduction of the deep neural network regression model, the estimation precision of the direction of arrival angle is improved by ten times under the conditions that the signal-to-noise ratio is lower than 15dB and the snapshot number is smaller than 200; and the estimation precision of the distance is close to a theoretically optimal solution.

Description

technical field [0001] The invention belongs to the technical field of array signal processing, and in particular relates to a near-field signal source location method based on a deep neural network regression model. Background technique [0002] Signal source localization is a fundamental problem in the field of signal processing, and has a wide range of applications in radar, sonar, wireless communication, speech recognition, and robotics. For DOA estimation of far-field signals, some scholars have proposed high-precision estimation methods, such as MUSIC and ESPRIT. When the signal is in the near field, that is, in the Fresnel region of the array aperture, the signal incident on the array has a spherical wave surface, so it must be represented by the direction of arrival and the distance at the same time. Therefore, the above-mentioned high precision with the far-field assumption is no longer suitable for near-field signal source positioning; scholars have proposed many ...

AI Technical Summary

Problems solved by technology

Although the idea is simple and straightforward, the angle and distance in the signal location are different from the labels in the classification problem and are continuous physical variables, so the classification model lacks a reasonable physical explanation

In addition, considering the computational cost, the classification model has limitations in the number of categories, resulting in low classification accuracy, some only 5° or even 10°, which is far from meeting the accuracy requirements in practical applications

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