Adaptive Detection of Range-spread Targets Based on Knowledge in Sea Clutter Background

XU Shuwen; XUE Jian; SHUI Penglang

doi:10.11999/JEIT160905

Volume 38 Issue 12

Jan. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(12): 3004-3010

XU Shuwen, XUE Jian, SHUI Penglang. Adaptive Detection of Range-spread Targets Based on Knowledge in Sea Clutter Background[J]. Journal of Electronics & Information Technology, 2016, 38(12): 3004-3010. doi: 10.11999/JEIT160905

Citation:

XU Shuwen, XUE Jian, SHUI Penglang. Adaptive Detection of Range-spread Targets Based on Knowledge in Sea Clutter Background[J]. Journal of Electronics & Information Technology, 2016, 38(12): 3004-3010. doi: 10.11999/JEIT160905

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Adaptive Detection of Range-spread Targets Based on Knowledge in Sea Clutter Background

doi: 10.11999/JEIT160905

Funds:

The National Natural Science Foundation of China (61201296, 61372136), The Fundamental Research Funds for the Central Universities (JB160224), Young Talent Fund of University Association for Science and Technology in Shaanxi Province (20160205)

Received Date: 2016-09-07
Rev Recd Date: 2016-11-09
Publish Date: 2016-12-19

Abstract

Abstract

For the problem of detection performance loss of adaptive detectors on the condition that the secondary data are limited, the adaptive detection method of range-spread target based on the prior knowledge of clutter is proposed. The texture and the covariance matrix of speckle of clutter are respectively modeled as the random variable which follows the inverse Gamma distribution and the random matrix which follows the inverse complex Wishart distribution. Based on the prior knowledge, the Maximum A Posteriori (MAP) estimation of texture component is obtained and the adaptive detector of range spread target which does not need the secondary data is designed via utilizing the generalized likelihood ratio test. Finally, the detection performances of the proposed detector are evaluated and the experimental results illustrate that the proposed detector is robust in parameters mismatched situation and outperforms the conventional generalized likelihood ratio test detector for range-spread target in limited secondary data scenarios.
- Range-spread target,
- Prior knowledge,
- Compound Gaussian clutter,
- Inverse Gamma distribution,
- Inverse complex Wishart distribution

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References(21)

References

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