Low-angle target two-dimensional DOA estimation method based on smoothing matrix set

Abstract

The invention discloses a low-angle target two-dimensional DOA estimation method based on a smoothing matrix set, and relates to the technical field of radar signals and information processing. In a forward backward smoothing matrix set (Forward Backward SMS, FB-SMS) method, through re-arranging an auto-covariance array of each row subsurface array, to establish a new smoothing matrix. The algorithm can optimize estimation performance through improving information utilization rate of a sample covariance matrix. In a spatial differencing smoothing matrix set method, through respectively performing spatial difference and forward backward smoothing processing on noise and non-noise parts of the row subsurface array, data loss is reduced, and compared with a conventional difference algorithm, color noise suppressing performance of the method is better.

Description

technical field [0001] The invention relates to the technical field of radar signal and information processing, in particular to a two-dimensional DOA estimation method for low-angle targets based on a smooth matrix set. Background technique [0002] Multi-target two-dimensional DOA (pitch angle, azimuth angle) estimation has been widely used in radar, sonar and wireless communication and other fields. As a result, great progress has been made in 2D DOA estimation using array flow patterns such as L-shaped arrays, parallel linear arrays, and uniform planar arrays. Among them, subspace algorithms have been greatly developed, such as MUSIC, ESPRIT and so on. But in the low-altitude environment, the coherent characteristics of the direct signal and the reflected signal make the traditional subspace algorithm invalid. The forward-backward spatial smoothing (FBSS) algorithm can solve the coherence through the spatial summation of the forward-backward subarray covariance matrix,...

AI Technical Summary

Problems solved by technology

[0003] 1. The two-dimensional spatial smoothing algorithm can only use limited data information, especially for uniform arrays, the amount of unused data information is greater

And under the condition of colored noise, the estimation performance is weak

[0004] 2. The two-dimensional spatial difference algorithm produces a large data loss in the difference operation, and it increases with the increase of the array aperture

Therefore, under the condition of large array aperture, the performance of spatial difference algorithm is weaker than that of spatial smoothing algorithm for white noise and colored noise

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