Volume 6 Issue 3
Jun.  2017
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Article Navigation >  Journal of Radars  > 2017  >  6(3): 275-284
Ding Hao, Wang Guoqing, Liu Ningbo, Guan Jian. Adaptive Detectors for Two Types of Subspace Targets in an Inverse Gamma Textured Background[J]. Journal of Radars, 2017, 6(3): 275-284. doi: 10.12000/JR16088
Citation: Ding Hao, Wang Guoqing, Liu Ningbo, Guan Jian. Adaptive Detectors for Two Types of Subspace Targets in an Inverse Gamma Textured Background[J]. Journal of Radars, 2017, 6(3): 275-284. doi: 10.12000/JR16088
shu

Adaptive Detectors for Two Types of Subspace Targets in an Inverse Gamma Textured Background

doi: 10.12000/JR16088

  • Department of Electronic and Information Engineering, Naval Aeronautical and Astronautical University, Yantai 264001, China

Funds:

Foundation Items: The National Natural Science Foundation of China 61531020

Foundation Items: The National Natural Science Foundation of China 61501487

The Natural Science Foundation of Shandong 2015ZRA06052

Foundation Items: The National Natural Science Foundation of China 61471381

Foundation Items: The National Natural Science Foundation of China 61471382

Foundation Items: The National Natural Science Foundation of China 61401495

The Aeronautical Science Foundation of China 20150184003

  • Received Date: 2016-07-18
  • Rev Recd Date: 2016-10-24
    • Available Online: 2016-11-23
  • Publish Date: 2017-06-28
    Abstract
  • Considering an inverse Gamma prior distribution model for texture, the adaptive detection problems for both first order Gaussian and second order Gaussian subspace targets are researched in a compound Gaussian sea clutter. Test statistics are derived on the basis of the two-step generalized likelihood ratio test. From these tests, new adaptive detectors are proposed by substituting the covariance matrix with estimation results from the Sample Covariance Matrix (SCM), normalized SCM, and fixed point estimator. The proposed detectors consider the prior distribution model for sea clutter during the design stage, and they model parameters that match the working environment during the detection stage. Analysis and validation results indicate that the detection performance of the proposed detectors out performs existing AMF (Adaptive Matched Filter, AMF) and ANMF (Adaptive Normalized Matched Filter, ANMF) detectors.

     

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