Polarimetric STAP via clutter spectrum reconstruction

Kang Zhao; Yulin Huang; Zhiwen Liu; Yougen Xu; Shuli Shi

doi:10.1145/3364908.3364911

Polarimetric STAP via clutter spectrum reconstruction

Kang Zhao, Yulin Huang, Zhiwen Liu, Yougen Xu, Shuli Shi

School of Integrated Circuits and Electronics

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

A new polarimetric space-time adaptive processing (pSTAP) approach is proposed to clutter suppression for weak target detection under short data samples. In the method, the target vector (also known as the target polarization-space-time steering vector) is determined based on the maximum likelihood scheme, while the clutter polarization-space-time spectrum (profile) is reconstructed by using a newly developed polarimetric sparse recovery technique. Numerical results are given to illustrate the performance of the proposed method.

Original language	English
Title of host publication	SSPS 2019 - 2019 International Symposium on Signal Processing Systems
Publisher	Association for Computing Machinery
Pages	99-102
Number of pages	4
ISBN (Electronic)	9781450362412
DOIs	https://doi.org/10.1145/3364908.3364911
Publication status	Published - 20 Sept 2019
Event	2019 International Symposium on Signal Processing Systems, SSPS 2019 - Beijing, China Duration: 20 Sept 2019 → 22 Sept 2019

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	2019 International Symposium on Signal Processing Systems, SSPS 2019
Country/Territory	China
City	Beijing
Period	20/09/19 → 22/09/19

Keywords

Polarization-space-time adaptive processing
Sparse dictionary matrix
Spectrum estimation

Access to Document

10.1145/3364908.3364911

Cite this

Zhao, K., Huang, Y., Liu, Z., Xu, Y., & Shi, S. (2019). Polarimetric STAP via clutter spectrum reconstruction. In SSPS 2019 - 2019 International Symposium on Signal Processing Systems (pp. 99-102). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3364908.3364911

@inproceedings{82ebffa83fa94c728c91da4ffa81e527,

title = "Polarimetric STAP via clutter spectrum reconstruction",

abstract = "A new polarimetric space-time adaptive processing (pSTAP) approach is proposed to clutter suppression for weak target detection under short data samples. In the method, the target vector (also known as the target polarization-space-time steering vector) is determined based on the maximum likelihood scheme, while the clutter polarization-space-time spectrum (profile) is reconstructed by using a newly developed polarimetric sparse recovery technique. Numerical results are given to illustrate the performance of the proposed method.",

keywords = "Polarization-space-time adaptive processing, Sparse dictionary matrix, Spectrum estimation",

author = "Kang Zhao and Yulin Huang and Zhiwen Liu and Yougen Xu and Shuli Shi",

note = "Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery.; 2019 International Symposium on Signal Processing Systems, SSPS 2019 ; Conference date: 20-09-2019 Through 22-09-2019",

year = "2019",

month = sep,

day = "20",

doi = "10.1145/3364908.3364911",

language = "English",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

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booktitle = "SSPS 2019 - 2019 International Symposium on Signal Processing Systems",

}

Zhao, K, Huang, Y, Liu, Z, Xu, Y & Shi, S 2019, Polarimetric STAP via clutter spectrum reconstruction. in SSPS 2019 - 2019 International Symposium on Signal Processing Systems. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 99-102, 2019 International Symposium on Signal Processing Systems, SSPS 2019, Beijing, China, 20/09/19. https://doi.org/10.1145/3364908.3364911

Polarimetric STAP via clutter spectrum reconstruction. / Zhao, Kang; Huang, Yulin; Liu, Zhiwen et al.
SSPS 2019 - 2019 International Symposium on Signal Processing Systems. Association for Computing Machinery, 2019. p. 99-102 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Polarimetric STAP via clutter spectrum reconstruction

AU - Zhao, Kang

AU - Huang, Yulin

AU - Liu, Zhiwen

AU - Xu, Yougen

AU - Shi, Shuli

PY - 2019/9/20

Y1 - 2019/9/20

N2 - A new polarimetric space-time adaptive processing (pSTAP) approach is proposed to clutter suppression for weak target detection under short data samples. In the method, the target vector (also known as the target polarization-space-time steering vector) is determined based on the maximum likelihood scheme, while the clutter polarization-space-time spectrum (profile) is reconstructed by using a newly developed polarimetric sparse recovery technique. Numerical results are given to illustrate the performance of the proposed method.

AB - A new polarimetric space-time adaptive processing (pSTAP) approach is proposed to clutter suppression for weak target detection under short data samples. In the method, the target vector (also known as the target polarization-space-time steering vector) is determined based on the maximum likelihood scheme, while the clutter polarization-space-time spectrum (profile) is reconstructed by using a newly developed polarimetric sparse recovery technique. Numerical results are given to illustrate the performance of the proposed method.

KW - Polarization-space-time adaptive processing

KW - Sparse dictionary matrix

KW - Spectrum estimation

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U2 - 10.1145/3364908.3364911

DO - 10.1145/3364908.3364911

M3 - Conference contribution

AN - SCOPUS:85077216301

T3 - ACM International Conference Proceeding Series

SP - 99

EP - 102

BT - SSPS 2019 - 2019 International Symposium on Signal Processing Systems

PB - Association for Computing Machinery

T2 - 2019 International Symposium on Signal Processing Systems, SSPS 2019

Y2 - 20 September 2019 through 22 September 2019

ER -

Polarimetric STAP via clutter spectrum reconstruction

Abstract

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Keywords

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