Effectiveness of knowledge-based STAP in ground targets detection with real dataset

S. Gelli; A. Bacci; E. Giusti; M. Martorella; F. Berizzi

doi:10.1049/cp.2017.0389

Effectiveness of knowledge-based STAP in ground targets detection with real dataset

S. Gelli
, A. Bacci
, E. Giusti
, M. Martorella
, F. Berizzi

Engineering

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

6 Citations (Scopus)

Abstract

A priori knowledge allows for clutter suppression and moving target detection to be improved. Specifically, in the Intelligent Filter and Training Selection (ITFS) approach terrain/clutter databases allow for the segmentation of terrain in Regions of Interest to be performed. This information is then used to optimize two adaptive filtering steps: the filter training strategy and the filter selection. In this paper a comparison between Knowledge-Based STAP and conventional STAP processing will be carried out. A real dataset is used to test and validate the proposed algorithm and to demonstrate the improvement with respect to conventional STAP.

Original language	English
Title of host publication	IET Conference Publications
Publisher	Institution of Engineering and Technology (IET)
Edition	CP728
ISBN (Electronic)	9781785614217, 9781785615030, 9781785616624, 9781785616723, 9781785616990, 9781785617072
ISBN (Print)	9781785615078, 9781785615153
DOIs	https://doi.org/10.1049/cp.2017.0389
Publication status	Published - 2017
Event	2017 International Conference on Radar Systems: International Conference on Radar Systems - Belfast, United Kingdom Duration: 23 Oct 2017 → 26 Oct 2017

Publication series

Name	IET Conference Publications
Number	CP728
Volume	2017

Conference

Conference	2017 International Conference on Radar Systems
Abbreviated title	RADAR 2017
Country/Territory	United Kingdom
City	Belfast
Period	23/10/17 → 26/10/17

Bibliographical note

Publisher Copyright:
© 2017 Institution of Engineering and Technology. All rights reserved.

ASJC Scopus subject areas

Electrical and Electronic Engineering

Access to Document

10.1049/cp.2017.0389

Cite this

@inproceedings{9b23909e6515447c85124c2fcef333fb,

title = "Effectiveness of knowledge-based STAP in ground targets detection with real dataset",

abstract = "A priori knowledge allows for clutter suppression and moving target detection to be improved. Specifically, in the Intelligent Filter and Training Selection (ITFS) approach terrain/clutter databases allow for the segmentation of terrain in Regions of Interest to be performed. This information is then used to optimize two adaptive filtering steps: the filter training strategy and the filter selection. In this paper a comparison between Knowledge-Based STAP and conventional STAP processing will be carried out. A real dataset is used to test and validate the proposed algorithm and to demonstrate the improvement with respect to conventional STAP.",

author = "S. Gelli and A. Bacci and E. Giusti and M. Martorella and F. Berizzi",

note = "Publisher Copyright: {\textcopyright} 2017 Institution of Engineering and Technology. All rights reserved.; 2017 International Conference on Radar Systems : International Conference on Radar Systems, RADAR 2017 ; Conference date: 23-10-2017 Through 26-10-2017",

year = "2017",

doi = "10.1049/cp.2017.0389",

language = "English",

isbn = "9781785615078",

series = "IET Conference Publications",

publisher = "Institution of Engineering and Technology (IET)",

number = "CP728",

booktitle = "IET Conference Publications",

edition = "CP728",

}

Gelli, S, Bacci, A, Giusti, E, Martorella, M & Berizzi, F 2017, Effectiveness of knowledge-based STAP in ground targets detection with real dataset. in IET Conference Publications. CP728 edn, IET Conference Publications, no. CP728, vol. 2017, Institution of Engineering and Technology (IET), 2017 International Conference on Radar Systems, Belfast, United Kingdom, 23/10/17. https://doi.org/10.1049/cp.2017.0389

TY - GEN

T1 - Effectiveness of knowledge-based STAP in ground targets detection with real dataset

AU - Gelli, S.

AU - Bacci, A.

AU - Giusti, E.

AU - Martorella, M.

AU - Berizzi, F.

PY - 2017

Y1 - 2017

N2 - A priori knowledge allows for clutter suppression and moving target detection to be improved. Specifically, in the Intelligent Filter and Training Selection (ITFS) approach terrain/clutter databases allow for the segmentation of terrain in Regions of Interest to be performed. This information is then used to optimize two adaptive filtering steps: the filter training strategy and the filter selection. In this paper a comparison between Knowledge-Based STAP and conventional STAP processing will be carried out. A real dataset is used to test and validate the proposed algorithm and to demonstrate the improvement with respect to conventional STAP.

AB - A priori knowledge allows for clutter suppression and moving target detection to be improved. Specifically, in the Intelligent Filter and Training Selection (ITFS) approach terrain/clutter databases allow for the segmentation of terrain in Regions of Interest to be performed. This information is then used to optimize two adaptive filtering steps: the filter training strategy and the filter selection. In this paper a comparison between Knowledge-Based STAP and conventional STAP processing will be carried out. A real dataset is used to test and validate the proposed algorithm and to demonstrate the improvement with respect to conventional STAP.

UR - https://www.scopus.com/pages/publications/85048671438

U2 - 10.1049/cp.2017.0389

DO - 10.1049/cp.2017.0389

M3 - Conference contribution

AN - SCOPUS:85048671438

SN - 9781785615078

SN - 9781785615153

T3 - IET Conference Publications

BT - IET Conference Publications

PB - Institution of Engineering and Technology (IET)

T2 - 2017 International Conference on Radar Systems

Y2 - 23 October 2017 through 26 October 2017

ER -

Effectiveness of knowledge-based STAP in ground targets detection with real dataset

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus subject areas

Access to Document

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Cite this