Low-altitude target direction-of-arrival estimating method for radar

Abstract

The invention belongs to the technical field of estimation of the direction of arrival of radar and discloses a low-altitude target direction-of-arrival estimating method for radar. The low-altitude target direction-of-arrival estimating method for the radar comprises the steps that an antenna array of the radar receives echo signals, the echo signals are sampled to obtain a data vector matrix, singular value decomposition is conducted on the data vector matrix to enable a corresponding dimensionality-reduction data vector and a corresponding noise subspace matrix to be generated, a guide vector matrix is obtained, a corresponding multi-path attenuation vector is obtained according to the noise subspace matrix and the guide vector matrix, a composite guide vector is constructed, a sensing matrix is constructed, a regularization parameter is obtained, and the direction of arrival is obtained by solving a corresponding mathematical model.

Description

technical field [0001] The invention belongs to the technical field of radar direction-of-arrival estimation, in particular to a method for estimating the direction-of-arrival of a low-altitude target used by radar. Background technique [0002] When the radar is used for long-range early warning, the effective distance is relatively long. Since the flying altitude of long-distance low-altitude targets is mostly in the low-altitude field below several kilometers, the strong ground-sea clutter will reduce the signal-to-clutter ratio of the echo signal, making it difficult to accurately Get the direction of arrival (DOA). At the same time, when the radar detects and tracks low-altitude targets, it not only receives the direct wave signal, but also receives the multipath reflected echo signal, resulting in multipath effect. Since the direct wave signal and the reflected signal are highly correlated, and most of the incident angles of the two echoes differ by less than a half-p...

AI Technical Summary

Problems solved by technology

Although the maximum likelihood estimation algorithm and the generalized MUSIC algorithm have high resolution for coherent signals, they both require two-dimensional spatial spectrum search and a large amount of calculation; the L1-SVD angle estimation algorithm based on multi-snapshot sparse matrix reconstruction and the matrix bundle Although the decoherence algorithm reduces the amount of computation, these two algorithms are only suitable for high signal-to-noise ratio conditions, and their resolution capabilities are still poor at low signal-to-noise ratios

Images