Structural total least squares algorithm for locating multiple disjoint sources based on AOA/TOA/FOA in the presence of system error

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...

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Bibliographic Details
Published in:Frontiers of information technology & electronic engineering Vol. 19; no. 7; pp. 917 - 936
Main Authors: Chen, Xin, Wang, Ding, Liu, Rui-rui, Yin, Jie-xin, Wu, Ying
Format: Journal Article
Language:English
Published: Hangzhou Zhejiang University Press 01.07.2018
Springer Nature B.V
Subjects:
Algorithms
Communications Engineering
Computer Hardware
Computer Science
Computer Systems Organization and Communication Networks
Electrical Engineering
Electronic warfare
Electronics and Microelectronics
Error analysis
Instrumentation
Iterative algorithms
Iterative methods
Least squares method
Linear equations
Localization
Lower bounds
Networks
Numerical stability
Observatories
Optimization models
Position measurement
Time synchronization
Structural total least squares
Disjoint sources
Single-station
Time-of-arrival (TOA)
TN911.7
Inverse iteration
Angle-of-arrival (AOA)
Frequency-of-arrival (FOA)
ISSN:2095-9184, 2095-9230
Online Access:Get full text
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Summary: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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ISSN:2095-9184
2095-9230
DOI:10.1631/FITEE.1700735