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Knowledge-assisted APR non-uniform sample detection method

A technology of knowledge assistance and sample detection, applied to radio wave measurement systems, instruments, etc., can solve problems such as false detection and missed detection, and achieve the effect of improving performance

Inactive Publication Date: 2015-01-21
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to solve the problem of false detection and missed detection when the training sample contains strong interference targets in the existing non-uniform detection method of training samples, and proposes a knowledge-aided (Knowledge-Aided, KA ) APR non-uniform sample detection method

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Embodiment Construction

[0035] Below in conjunction with accompanying drawing and example the present invention is described in detail as follows:

[0036] The knowledge-assisted adaptive power residual (KA-APR) non-uniform sample detection method proposed by the present invention integrates the prior knowledge of the distance unit clutter into the STAP training sample selection strategy, effectively overcoming false detection and missed detection of interference targets Shortcomings. The training samples screened by KA-APR have statistically similar clutter covariance matrices with the distance units to be measured, which can significantly improve STAP performance.

[0037] In-depth analysis of the mathematical meaning of the conventional Adaptive Power Residual (APR) algorithm and the reasons for its performance degradation in strong jamming target detection: In the conventional APR algorithm, due to the training samples polluted by the jamming target participating in the covariance matrix The ca...

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Abstract

The invention discloses a knowledge-assisted APR non-uniform sample detection method. According to the method, noise wave priori knowledge and a self-adaptive power residue non-uniform detector are combined, training samples are effectively chosen, and training samples comprising interference targets can not affect the method. A noise wave covariance matrix based on priori knowledge does not comprises a non-uniform character addition item , compared with a conventional APR method, strong and weak interference target signals can be effectively detected, training samples polluted by interference targets are eliminated, a training sample screened through the method is a noise wave covariance matrix similar to a distance unit to be detected in statistical significance, and STAP performance can be obviously improved. According to the method, priori knowledge is applied to training sample selection, and a selected training sample can satisfy the IID character.

Description

technical field [0001] The invention belongs to the technical field of signal and information processing, and in particular relates to an airborne radar non-uniform clutter sample detection method. Background technique [0002] When airborne radar detects moving targets, the main problem is how to suppress strong ground clutter and various types of interference. Space Time Adaptive Processing (STAP) is the key technology to solve this problem. The key of STAP technology is to correctly estimate the covariance matrix of the detection unit, form space-time adaptive weights, and realize effective suppression of airborne radar clutter and interference. In the maximum likelihood estimation of the covariance matrix, a basic assumption is that the training sample data has the characteristics of independent and identically distributed (IID), that is, the training sample data is considered to be uniform. [0003] In practical applications, the clutter environment faced by airborne r...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01S7/292G01S7/36
Inventor 曹建蜀何明东
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA