Optimum Scatterer Estimation and Adaptive Detection of Sparse Range-spread Target

Gu Xinfeng; Hao Xiaolin; Xu Rong; Huang Kun

doi:doi:10.11648/j.sd.20160401.16

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Optimum Scatterer Estimation and Adaptive Detection of Sparse Range-spread Target

Gu Xinfeng, Hao Xiaolin, Xu Rong, Huang Kun

Published in Science Discovery (Volume 4, Issue 1)

Received: 15 April 2016 Accepted: Published: 16 April 2016

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Abstract

This paper addresses sparse range-spread target detection in non-Gaussian clutter model as spherically invariant random vector. An optimum scatterers integrator (OSI) is proposed for the problem that it is hard to accurately estimate the number of scatters of the spare range-spread target. The OSI is obtained by integrating the energy of strong scatterers estimated based on the method of maximum detection probability. Moreover, the covariance matrix structure is estimated based on the clutter-cluster estimation and the technology of recursive estimation. An adaptive OSI (AOSI) is obtained by replacing the covariance matrix structure with the estimated one. The AOSI has the Constant false alarm ratio (CFAR) property with respect to the covariance matrix structure and the texture component of the clutter. Finally, the simulation results show the validity of proposed methods.

Published in	Science Discovery (Volume 4, Issue 1)
DOI	10.11648/j.sd.20160401.16
Page(s)	31-38
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Aadaptive Detection, Range-Spread Target, Non-Gaussian Clutter, Covariance Matrix Structure

References

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[4]	Bandiera F, De Maio A, Greco A S et al. Adaptive radar detection of distributed targets in homogeneous and partially homogeneous noise plus subspace interference [J]. IEEE Transactions on Signal Processing, 2007, 55(4): 1223-1237.
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[9]	简涛,何友,苏峰,等.非高斯杂波下修正的SDD距离扩展目标检测器[J].电子学报,2009,37(12):2662-2667.[Jian Tao, He You, Su Feng, et al. Modified SDD-GLRT detector for range-spread targets in non-Gaussian clutter [J]. Acta Electronica Sinica. 2009, 37(12): 2662-2667.]
[10]	简涛,何友,苏峰,等.非高斯背景下基于动态阈值的距离扩展目标检测器[J].电子学报,2011,39(1):59-63.[Jian Tao, He You, Su Feng, et al. Range-spread target detector with dynamic threshold for non-Gaussian clutter [J]. Acta Electronica Sinica. 2011, 39(1): 59-63.]
[11]	Kelly E J. An adaptive detection algorithm [J]. IEEE Transactions on Aerospace and Electronic Systems, 1986, 22: 115-127.
[12]	Gini F, Michels J H. Performance analysis of two covariance matrix estimators in compound-Gaussian clutter [J]. IEE Pro.-Radar, Sonar Navig., 1999, 146(3): 133:140.
[13]	Stinco P, Greco M, Gini F. Adaptive Detection in Compound-Gaussian Clutter with Inverse-Gamma texture [C] Proceedings of 2011 IEEE CIE International Conference on Radar, Radar 2011: 434-437.
[14]	Pascal F, Chitour Y, Ovarlez J P, et al. Covariance structure maximum likelihood estimates in compound Gaussian noise: Existence and algorithm analysis [J]. IEEE Transactions on Signal Processing, 2008, 56(1): 34-48.
[15]	Conte E, De Maio A, Ricci G. Covariance matrix estimation for adaptive CFAR detection in compound-Gaussian clutter [J]. IEEE Transactions on Aerospace and Electronic Systems, 2002, 38 ( 2 ): 415 - 426.
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[17]	Gini F, Greco M. Covariance matrix estimation for CFAR detection in correlated heavy tailed clutter. Signal Process, 2002, 82: 1847-1859.

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APA Style

Gu Xinfeng, Hao Xiaolin, Xu Rong, Huang Kun. (2016). Optimum Scatterer Estimation and Adaptive Detection of Sparse Range-spread Target. Science Discovery, 4(1), 31-38. https://doi.org/10.11648/j.sd.20160401.16

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ACS Style

Gu Xinfeng; Hao Xiaolin; Xu Rong; Huang Kun. Optimum Scatterer Estimation and Adaptive Detection of Sparse Range-spread Target. Sci. Discov. 2016, 4(1), 31-38. doi: 10.11648/j.sd.20160401.16

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AMA Style

Gu Xinfeng, Hao Xiaolin, Xu Rong, Huang Kun. Optimum Scatterer Estimation and Adaptive Detection of Sparse Range-spread Target. Sci Discov. 2016;4(1):31-38. doi: 10.11648/j.sd.20160401.16

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@article{10.11648/j.sd.20160401.16,
  author = {Gu Xinfeng and Hao Xiaolin and Xu Rong and Huang Kun},
  title = {Optimum Scatterer Estimation and Adaptive Detection of Sparse Range-spread Target},
  journal = {Science Discovery},
  volume = {4},
  number = {1},
  pages = {31-38},
  doi = {10.11648/j.sd.20160401.16},
  url = {https://doi.org/10.11648/j.sd.20160401.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20160401.16},
  abstract = {This paper addresses sparse range-spread target detection in non-Gaussian clutter model as spherically invariant random vector. An optimum scatterers integrator (OSI) is proposed for the problem that it is hard to accurately estimate the number of scatters of the spare range-spread target. The OSI is obtained by integrating the energy of strong scatterers estimated based on the method of maximum detection probability. Moreover, the covariance matrix structure is estimated based on the clutter-cluster estimation and the technology of recursive estimation. An adaptive OSI (AOSI) is obtained by replacing the covariance matrix structure with the estimated one. The AOSI has the Constant false alarm ratio (CFAR) property with respect to the covariance matrix structure and the texture component of the clutter. Finally, the simulation results show the validity of proposed methods.},
 year = {2016}
}

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TY  - JOUR
T1  - Optimum Scatterer Estimation and Adaptive Detection of Sparse Range-spread Target
AU  - Gu Xinfeng
AU  - Hao Xiaolin
AU  - Xu Rong
AU  - Huang Kun
Y1  - 2016/04/16
PY  - 2016
N1  - https://doi.org/10.11648/j.sd.20160401.16
DO  - 10.11648/j.sd.20160401.16
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 31
EP  - 38
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20160401.16
AB  - This paper addresses sparse range-spread target detection in non-Gaussian clutter model as spherically invariant random vector. An optimum scatterers integrator (OSI) is proposed for the problem that it is hard to accurately estimate the number of scatters of the spare range-spread target. The OSI is obtained by integrating the energy of strong scatterers estimated based on the method of maximum detection probability. Moreover, the covariance matrix structure is estimated based on the clutter-cluster estimation and the technology of recursive estimation. An adaptive OSI (AOSI) is obtained by replacing the covariance matrix structure with the estimated one. The AOSI has the Constant false alarm ratio (CFAR) property with respect to the covariance matrix structure and the texture component of the clutter. Finally, the simulation results show the validity of proposed methods.
VL  - 4
IS  - 1
ER  -

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Author Information

Gu Xinfeng

China Satellite Maritime Tracking & Control Department, Jiangyin,China
Hao Xiaolin

Yantai Electricityn and Economy Technical Institute, Yantai, China
Xu Rong

China Satellite Maritime Tracking & Control Department, Jiangyin,China
Huang Kun

China Satellite Maritime Tracking & Control Department, Jiangyin,China

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APA Style

Gu Xinfeng, Hao Xiaolin, Xu Rong, Huang Kun. (2016). Optimum Scatterer Estimation and Adaptive Detection of Sparse Range-spread Target. Science Discovery, 4(1), 31-38. https://doi.org/10.11648/j.sd.20160401.16

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ACS Style

Gu Xinfeng; Hao Xiaolin; Xu Rong; Huang Kun. Optimum Scatterer Estimation and Adaptive Detection of Sparse Range-spread Target. Sci. Discov. 2016, 4(1), 31-38. doi: 10.11648/j.sd.20160401.16

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AMA Style

Gu Xinfeng, Hao Xiaolin, Xu Rong, Huang Kun. Optimum Scatterer Estimation and Adaptive Detection of Sparse Range-spread Target. Sci Discov. 2016;4(1):31-38. doi: 10.11648/j.sd.20160401.16

Copy | Download

@article{10.11648/j.sd.20160401.16,
  author = {Gu Xinfeng and Hao Xiaolin and Xu Rong and Huang Kun},
  title = {Optimum Scatterer Estimation and Adaptive Detection of Sparse Range-spread Target},
  journal = {Science Discovery},
  volume = {4},
  number = {1},
  pages = {31-38},
  doi = {10.11648/j.sd.20160401.16},
  url = {https://doi.org/10.11648/j.sd.20160401.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20160401.16},
  abstract = {This paper addresses sparse range-spread target detection in non-Gaussian clutter model as spherically invariant random vector. An optimum scatterers integrator (OSI) is proposed for the problem that it is hard to accurately estimate the number of scatters of the spare range-spread target. The OSI is obtained by integrating the energy of strong scatterers estimated based on the method of maximum detection probability. Moreover, the covariance matrix structure is estimated based on the clutter-cluster estimation and the technology of recursive estimation. An adaptive OSI (AOSI) is obtained by replacing the covariance matrix structure with the estimated one. The AOSI has the Constant false alarm ratio (CFAR) property with respect to the covariance matrix structure and the texture component of the clutter. Finally, the simulation results show the validity of proposed methods.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - Optimum Scatterer Estimation and Adaptive Detection of Sparse Range-spread Target
AU  - Gu Xinfeng
AU  - Hao Xiaolin
AU  - Xu Rong
AU  - Huang Kun
Y1  - 2016/04/16
PY  - 2016
N1  - https://doi.org/10.11648/j.sd.20160401.16
DO  - 10.11648/j.sd.20160401.16
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 31
EP  - 38
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20160401.16
AB  - This paper addresses sparse range-spread target detection in non-Gaussian clutter model as spherically invariant random vector. An optimum scatterers integrator (OSI) is proposed for the problem that it is hard to accurately estimate the number of scatters of the spare range-spread target. The OSI is obtained by integrating the energy of strong scatterers estimated based on the method of maximum detection probability. Moreover, the covariance matrix structure is estimated based on the clutter-cluster estimation and the technology of recursive estimation. An adaptive OSI (AOSI) is obtained by replacing the covariance matrix structure with the estimated one. The AOSI has the Constant false alarm ratio (CFAR) property with respect to the covariance matrix structure and the texture component of the clutter. Finally, the simulation results show the validity of proposed methods.
VL  - 4
IS  - 1
ER  -

Copy | Download