Constrained Total Least-Squares Location Algorithm Using Time-Difference-of-Arrival Measurements

• Published in: IEEE transactions on vehicular technology Vol. 59; no. 3; pp. 1558 - 1562
• Main Authors: Yang, Kai, An, Jianping, Bu, Xiangyuan, Sun, Gangcan
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.03.2010; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Applied sciences; Computational efficiency; Computational modeling; Computer simulation; Constrained total least-squares (CTLS); Constraints; Covariance; Detection, estimation, filtering, equalization, prediction; Exact sciences and technology; Information, signal and communications theory; Iterative algorithms; Iterative methods; Least squares method; Newton method; Noise measurement; Noise robustness; Perturbation methods; Position (location); Position measurement; Signal and communications theory; Signal, noise; source location; Telecommunications and information theory; Time difference of arrival; time difference of arrival (TDOA); Measurement sensor; Iterative method; Numerical method; Algorithm; Computational complexity; Arrival time; Perturbation method; Covariance; Algorithm performance; Source localization; Least squares method; time difference of arrival (TDOA); System simulation; source location; Newton method; Target detection; Comparative study; Gain; Constrained total least-squares (CTLS)
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2009.2037509

Determining the location of a source from its emissions has gained considerable interest over the past few years. In this paper, by utilizing the time difference of arrival (TDOA) of a signal received at spatially separated sensors, a novel algorithm for source location is proposed. The algorithm is based on the constrained total least-squares (CTLS) technique, and an iterative technique based on Newton's method is utilized to give a numerical solution. By using a perturbation analysis, the bias and covariance of the proposed CTLS algorithm are also derived. Simulation results show that the proposed CTLS algorithm gives sufficient accuracy with lower computational cost, and more importantly, it is more robust to large measurement noise than the compared algorithms.