Knowledge assisted space-time adaptive processing method integrating generalized symmetrical structure information

Abstract

The invention provides a knowledge assisted space-time adaptive processing method integrating generalized symmetrical structure information. The knowledge assisted space-time adaptive processing method integrating the generalized symmetrical structure information comprises steps of (1) performing linear transformation through an unitary transformation matrix and transforming an original space-time adaptive processing problem to be in a form which is equivalent to the original space-time adaptive processing problem to enable a covariance matrix of the original space-time adaptive processing problem to be transferred into a real symmetric matrix in the same dimension from a generalized symmetric matrix; (2) obtaining an estimation of the transformed covariance matrix according to sample data; (3) solving an optimal real symmetric estimation of a prior covariance matrix under the minimum Euclidean distance; (4) solving a minimum mean square error estimation through a generalized linear combination and convex combination method in combination with training samples and the prior covariance matrix; (5) obtaining detector forms under part of uniform model and random non-uniform model assumption according to a two-step design and achieving target detection. The knowledge assisted space-time adaptive processing method integrating the generalized symmetrical structure information has the advantages of effectively reducing demanded quantity of the training samples during covariance matrix estimation in the space-time adaptive processing, remarkably improving the detector performances and being simple in achievement.

Description

technical field [0001] The invention belongs to the field of space-time adaptive processing (STAP), in particular to a knowledge-aided space-time adaptive processing (KA-STAP) method which fuses generalized symmetrical structure information. Background technique [0002] Space-Time Adaptive Processing (STAP) is the core technology for the moving platform radar system to suppress clutter and interference and improve the target detection capability. The estimation accuracy of the total interference plus noise covariance matrix largely affects the performance of STAP. In the traditional STAP, it is assumed that the training sample data satisfies independent and identical distribution and has the same covariance matrix (homogeneous environment) as the data of the unit under test, and the sampling covariance matrix (SCM) is usually used as the estimate of the total interference plus noise covariance matrix . However, the clutter environment is usually non-uniform, for example, ...

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Problems solved by technology

[0005] The technical problem to be solved by the present invention is: Aiming at the problem that it is difficult to obtain enough uniform samples due to the limitation of non-uniform environment and radar-related parameters in the application of STAP, a knowledge-assisted space-time adaptive processing method integrating generalized symmetric structure information is proposed

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