CLC number: TN911.7
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2018-07-12
Cited: 0
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Xin Chen , Ding Wang , Rui-Rui Liu , Jie-Xin Yin , Ying Wu . Structural total least squares algorithm for locating multiple disjoint sources based on AOA/TOA/FOA in the presence of system error[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(7): 919-936.
@article{title="Structural total least squares algorithm for locating multiple disjoint sources based on AOA/TOA/FOA in the presence of system error",
author="Xin Chen , Ding Wang , Rui-Rui Liu , Jie-Xin Yin , Ying Wu ",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="19",
number="7",
pages="919-936",
year="2018",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1700735"
}
%0 Journal Article
%T Structural total least squares algorithm for locating multiple disjoint sources based on AOA/TOA/FOA in the presence of system error
%A Xin Chen
%A Ding Wang
%A Rui-Rui Liu
%A Jie-Xin Yin
%A Ying Wu
%J Frontiers of Information Technology & Electronic Engineering
%V 19
%N 7
%P 919-936
%@ 2095-9184
%D 2018
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1700735
TY - JOUR
T1 - Structural total least squares algorithm for locating multiple disjoint sources based on AOA/TOA/FOA in the presence of system error
A1 - Xin Chen
A1 - Ding Wang
A1 - Rui-Rui Liu
A1 - Jie-Xin Yin
A1 - Ying Wu
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 19
IS - 7
SP - 919
EP - 936
%@ 2095-9184
Y1 - 2018
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1700735
Abstract: single-station passive localization technology avoids the complex time synchronization and information exchange between multiple observatories, and is increasingly important in electronic warfare. Based on a single moving station localization system, a new method with high localization precision and numerical stability is proposed when the measurements from multiple disjoint sources are subject to the same station position and velocity displacement. According to the available measurements including the angle-of-arrival (AOA), time-of-arrival (TOA), and frequency-of-arrival (FOA), the corresponding pseudo linear equations are deduced. Based on this, a structural total least squares (STLS) optimization model is developed and the inverse iteration algorithm is used to obtain the stationary target location. The localization performance of the STLS localization algorithm is derived, and it is strictly proved that the theoretical performance of the STLS method is consistent with that of the constrained total least squares method under first-order error analysis, both of which can achieve the Cramér-Rao lower bound accuracy. Simulation results show the validity of the theoretical derivation and superiority of the new algorithm.
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