Method for locating near-field narrowband signal source

Abstract

The invention discloses a method for locating a near-field narrowband signal source. The method includes the following steps: constructing a symmetric uniform linear array, calculating a covariance matrix R of the symmetric uniform linear array according to received data of the symmetric uniform linear array; calculating the noise variance sigma<2> based on the obtained covariance matrix R; calculating a noiseless correlation coefficient (represented by the formula) according to the obtained covariance matrix R and noise variance sigma<2>; calculating an estimated value (represented by the formula) of the direction angle of wave arrival of a near-field signal by calculating K zero phase points of polynomial (represented by the formula), and calculating an estimated value (represented by the formula) of the distance of the near-field signal by calculating K zero phase points of polynomial (represented by the formula) to obtain azimuth information (represented by the formula) of the to-be-located near-field narrowband signal source. The locating method of the invention does not need steps of decomposition of characteristic values and multi-dimensional search, which can reduce computational complexity, thus the method is simple and effective.

Description

technical field [0001] The invention belongs to the technical field of array signal processing, and in particular relates to a positioning method for a near-field narrowband signal source. Background technique [0002] Source location has important applications in radar, sonar, wireless communication and other fields. In some practical applications, such as the microphone array-based voice localization problem, the input signal is a near-field signal, and the one-dimensional direction of arrival and distance information of the near-field signal need to be estimated separately. Although many methods have been proposed to solve the problem of near-field signal source location, such as estimation methods based on high-order statistics, estimation methods based on linear prediction, estimation methods based on MUSIC, etc. However, the current methods have certain defects and deficiencies. The estimation method based on high-order statistics requires high computational complexit...

AI Technical Summary

Problems solved by technology

However, the current methods have certain defects and deficiencies. The estimation method based on high-order statistics requires high computational complexity; the estimation method based on linear prediction reduces the computational complexity by separately estimating the direction of arrival and distance of the near-field signal source. However, when the number of samples is limited, it usually encounters saturation phenomenon; although the MUSIC-based estimation method overcomes the saturation phenomenon, the solution process uses two-dimensional spectral peak search, and the computational complexity increases significantly.

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