Signal arrival direction estimation method of spectral function second derivative based on minimum variance method

Abstract

The present invention belongs to the field of the radar signal processing technology, and discloses a signal arrival direction estimation method of the spectral function second derivative based on the minimum variance method. The method comprises: setting a radar uniform array, obtaining radar receiving data from the radar uniform line, and obtaining the steering vectors according to the radar uniform array; calculating the covariance matrix of the radar receiving data according to the radar receiving data, performing inversion of the covariance matrix, and obtaining the covariance inverse matrix of the radar receiving data; determining a Capon spatial spectrum function according to the steering vectors and the covariance inverse matrix of the radar receiving data; solving the second derivative of the Capon spatial spectrum function, and constructing a new spatial spectrum function according to the second derivative of the Capon spatial spectrum function; performing maximum likelihood estimation of the signal arrival direction according to the new spatial spectrum function, and obtaining the estimated value of the signal arrival direction; and improving the angular resolution and the robustness of the measure direction performance.

Description

technical field [0001] The invention relates to the technical field of radar signal processing, in particular to a direction-of-arrival estimation method based on the second-order derivative of the spectral function of the minimum square variance method, which can be used for target positioning and tracking. Background technique [0002] Subspace algorithms represented by multiple signal classification MUSIC and rotation invariant subspace ESPRIT are one of the most important methods for signal direction of arrival DOA estimation. This type of algorithm uses the orthogonality between signal subspace and noise subspace to estimate DOA according to the number of known signals. Since the signal subspace and the noise subspace are completely orthogonal under the noise-free model, the subspace-based algorithm can theoretically distinguish between two objects that are infinitely close. [0003] Although subspace algorithms have excellent super-resolution estimation performance, a...

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Problems solved by technology

At this time, the performance of these super-resolution direction-finding algorithms will seriously deteriorate, or even fail.

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