Location Adaptive Least Square Algorithm for Target Localization in Multi-Static Active Sonar
Summary :
In multi-static sonar systems, the least square (LS) and maximum likelihood (ML) are the typical estimation criteria for target location estimation. The LS localizaiton has the advantage of low computational complexity. On the other hand, the performance of LS can be degraded severely when the target lies on or around the straight line between the source and receiver. We examine mathematically the reason for the performance degradation of LS. Then, we propose a location adaptive — least square (LA-LS) localization that removes the weakness of the LS localizaiton. LA-LS decides the receivers that produce abnormally large measurement errors with a proposed probabilistic measure. LA-LS achieves improved performance of the LS localization by ignoring the information from the selected receivers.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E97-B No.1 pp.204-209
- Publication Date
- 2014/01/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.E97.B.204
- Type of Manuscript
- PAPER
- Category
- Sensing
Authors
Eun Jeong JANG
KNU
Dong Seog HAN
KNU
Keyword
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Eun Jeong JANG, Dong Seog HAN, "Location Adaptive Least Square Algorithm for Target Localization in Multi-Static Active Sonar" in IEICE TRANSACTIONS on Communications,
vol. E97-B, no. 1, pp. 204-209, January 2014, doi: 10.1587/transcom.E97.B.204.
Abstract: In multi-static sonar systems, the least square (LS) and maximum likelihood (ML) are the typical estimation criteria for target location estimation. The LS localizaiton has the advantage of low computational complexity. On the other hand, the performance of LS can be degraded severely when the target lies on or around the straight line between the source and receiver. We examine mathematically the reason for the performance degradation of LS. Then, we propose a location adaptive — least square (LA-LS) localization that removes the weakness of the LS localizaiton. LA-LS decides the receivers that produce abnormally large measurement errors with a proposed probabilistic measure. LA-LS achieves improved performance of the LS localization by ignoring the information from the selected receivers.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E97.B.204/_p
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@ARTICLE{e97-b_1_204,
author={Eun Jeong JANG, Dong Seog HAN, },
journal={IEICE TRANSACTIONS on Communications},
title={Location Adaptive Least Square Algorithm for Target Localization in Multi-Static Active Sonar},
year={2014},
volume={E97-B},
number={1},
pages={204-209},
abstract={In multi-static sonar systems, the least square (LS) and maximum likelihood (ML) are the typical estimation criteria for target location estimation. The LS localizaiton has the advantage of low computational complexity. On the other hand, the performance of LS can be degraded severely when the target lies on or around the straight line between the source and receiver. We examine mathematically the reason for the performance degradation of LS. Then, we propose a location adaptive — least square (LA-LS) localization that removes the weakness of the LS localizaiton. LA-LS decides the receivers that produce abnormally large measurement errors with a proposed probabilistic measure. LA-LS achieves improved performance of the LS localization by ignoring the information from the selected receivers.},
keywords={},
doi={10.1587/transcom.E97.B.204},
ISSN={1745-1345},
month={January},}
Copy
TY - JOUR
TI - Location Adaptive Least Square Algorithm for Target Localization in Multi-Static Active Sonar
T2 - IEICE TRANSACTIONS on Communications
SP - 204
EP - 209
AU - Eun Jeong JANG
AU - Dong Seog HAN
PY - 2014
DO - 10.1587/transcom.E97.B.204
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E97-B
IS - 1
JA - IEICE TRANSACTIONS on Communications
Y1 - January 2014
AB - In multi-static sonar systems, the least square (LS) and maximum likelihood (ML) are the typical estimation criteria for target location estimation. The LS localizaiton has the advantage of low computational complexity. On the other hand, the performance of LS can be degraded severely when the target lies on or around the straight line between the source and receiver. We examine mathematically the reason for the performance degradation of LS. Then, we propose a location adaptive — least square (LA-LS) localization that removes the weakness of the LS localizaiton. LA-LS decides the receivers that produce abnormally large measurement errors with a proposed probabilistic measure. LA-LS achieves improved performance of the LS localization by ignoring the information from the selected receivers.
ER -
English
