Novel high-resolution orientation-estimating method based on Cauchy Gaussian model

Abstract

The invention relates to a novel high-resolution orientation-estimating method based on a Cauchy Gaussian model. By the method, the requirements of the underwater wideband high-frequency three-dimensional imaging of a buried mine detecting sonar on a target orientation resolving power are met. The method comprises the steps of: by using the Cauchy-Gaussian signal model, expressing an orientation spectrum as a regularized space-domain Fourier transform constraint optimizing problem; providing a spectral estimator tolerant to model parameters; by seeking sparse distribution features of a source signal in a space, realizing high-resolution spectral orientation estimation within a single-frequency-domain snapshot; and resolving coherent sources without decorrelation. The method has the advantages that: by the method, the high orientation resolution is achieved within the single snapshot through the constraint optimization of a cost function; the tolerance to parameters of a signal probability distributing model is high, so that the method is practical in engineering; the numerical simulation and sea trial data analyzing results prove that the method is applicable for the high target orientation resolution of a small-aperture subarray in shortage of the snapshot.

Description

technical field [0001] The invention relates to the field of sensor array signal processing, and mainly relates to a novel high-resolution orientation estimation method based on a Cauchy-Gaussian model. Background technique [0002] The problem of signal bearing estimation has attracted extensive research interest in the fields of sonar, radar and communication. Conventional beamforming detects and estimates target azimuth through spatially matched filtering, but the target azimuth resolution depends on the aperture size of the array wavelength unit. Adaptive beamforming [1] and subspace high-resolution direction of arrival (DOA) estimation algorithms, such as MUSIC, can obtain high-resolution performance, but require sufficient signal-to-noise ratio conditions, and require long time averaging to Improve the precision of covariance matrix estimation. The signal subspace decomposition algorithm needs to pre-estimate the number of signal sources, and when there are coherent ...

AI Technical Summary

Problems solved by technology

Therefore, conventional high-resolution processing methods are difficult to effectively apply when the aperture of the array is limited and under high dynamic conditions.

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