Radar target fusion detection method based on non-Gaussian parameters

Abstract

The invention discloses a radar target fusion detection method based on non-Gaussian parameters, and belongs to the field of radar signal processing. The method comprises the steps: achieving the design of an adaptive detector in a transition clutter environment and the synchronization control of a corresponding clutter covariance matrix estimation method through a single parameter for the spatio-temporal gradual change of the clutter non-Gaussian degree in an actual environment, and constructing a unified covariance matrix fusion estimation frame, which covers the conventional optimal or second optimal estimation methods for a sampling covariance matrix, a normalized sampling covariance matrix, an approximate maximum likelihood estimation matrix. A proposed detector structure can be compatible with and cover the optimal or second optimal adaptive detectors under the Gaussian and complex Gaussian clutter backgrounds, can adapt to the transition clutter environment between the Gaussian and complex Gaussian clutter backgrounds, is adaptive to the spatio-temporal gradual change of the clutter non-Gaussian degree in the actual environment, and is wide in potential application range.

Description

technical field [0001] The invention belongs to the field of radar signal processing, and in particular relates to a radar target fusion detection method based on non-Gaussian parameters. Background technique [0002] For radars using coherent pulse trains or multi-array elements, the realization of target adaptive detection needs to consider both the detector structure design and the unknown clutter covariance matrix estimation, and the solutions of both are related to the background clutter statistics. The characteristics are closely related, and in practical applications, the estimation of the unknown clutter covariance matrix often requires the use of pure clutter reference data adjacent to the detected unit. [0003] For the traditional low-resolution radar scene, since there are a large number of independent scattering points in a single range unit, according to the central limit theorem, the statistical characteristics of clutter obey the Gaussian distribution, and th...

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Problems solved by technology

Although there exist optimal or suboptimal clutter covariance matrix estimation methods and corresponding point object detector structures for Gaussian and compound Gaussian clutter backgrounds, the non-Gaussian properties of real clutter tend to vary in time and space with the environment While the gradual changes, the above-mentioned optimal or suboptimal clutter covariance matrix estimation methods and corresponding point object detectors in the specific clutter background are difficult to adapt to the rapid changes in the clutter environment, resulting in corresponding detection performance and constant false alarm rate ( CFAR) characteristic deterioration

[0004] In view of the non-Gaussian degree of temporal and spatial gradient of clutter in the actual environment, in the design of the point target detector structure, it is necessary to consider not only the extreme Gaussian and compound Gaussian clutter environment, but also the gap between Gaussian and compound Gaussian. Transitional clutter environment; while the current sampling covariance matrix estimation method under the Gaussian background and the normalized sampling covariance matrix estimation method under the compound Gaussian background only consider a single specific situation of Gaussian or compound Gaussian, it is difficult to adapt to the transitional clutter environment. wave environment; among them, the sampling covariance matrix estimation method is only matched with the generalized likelihood ratio test detector and adaptive matched filter in the Gaussian background, and the normalized sampling covariance matrix estimation method is only compatible with the composite Gaussian background The adaptive normalized matched filter under individually matched

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