Signal source number estimation method based on Gerschgorin circle transformation and modified Rao score inspection

Abstract

The invention discloses a signal source number estimation method based on Gerschgorin circle transformation and modified Rao score inspection. The method comprises the steps: firstly, calculating a sample covariance matrix of an observation signal; then, carrying out Gerschgorin circle transformation on the sample covariance matrix, and by utilizing the estimated value of the characteristic valueof the sample covariance matrix obtained after transformation and on the basis of the modified Rao score inspection thought, detecting the structural characteristics of the large-dimensional covariance matrix; and then, by detecting whether the covariance matrix of the noise part in the observation signal is in direct proportion to a unit matrix, constructing an observation statistical magnitude used for establishing an information theory criterion likelihood function, wherein the statistical magnitude is also the statistical magnitude of a sample characteristic value; and on the basis, carrying out signal source number estimation through a generalized Bayesian information criterion. The method provided by the invention has relatively wide applicability, is suitable for signal source number estimation under a classic asymptotic system, and is also suitable for signal source number estimation under a common asymptotic system; and the method is suitable for signal source number estimation in a white Gaussian noise environment and is also suitable for signal source number estimation in a color noise environment.

Description

technical field [0001] The invention belongs to the technical field of radar and communication reconnaissance, and further relates to a method for estimating the number of sources based on Gail circle transformation and modified Rao score test in the technical field of radar and communication reconnaissance signal processing. Background technique [0002] Estimation of the number of radiation sources has important applications in many fields, such as phased array radar, communication, brain imaging, neural network, speech signal separation and signal direction of arrival estimation. [0003] The source number estimation method is essentially based on the statistical analysis theory of observed data and its moment function. For example, the commonly used hypothesis testing methods and information theory criterion methods in source number estimation mainly use the statistical distribution of observed data and the characteristics of sample eigenvalues. Statistics. At present, ...

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Problems solved by technology

These methods are not only applicable to the source number estimation in the general asymptotic system, but also in the classical asymptotic system. However, these methods are only applicable to the white noise environment, and the source number estimation fails in the colored noise environment.

[0007] A comprehensive analysis of domestic and foreign literature shows that there is still a lack of source number estimation methods that are applicable to both the classical asymptotic system and the general asymptotic system, and no matter in the environment of white noise or colored noise

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