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

• 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
• DOI: 10.1631/FITEE.1700735

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.