Search for: time-difference-of-arrival--tdoa
Article IEEE Communications Letters ; 2018 ; 10897798 (ISSN) ; Behnia, F ; Noroozi, A ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc 2018
In this letter, the problem of source localization using time difference of arrival (TDOA) is investigated. Then, a closedform two-stage solution is proposed based on estimation of the range nuisance parameter in the first stage and refinement of initial solution in the next stage. The proposed solution is shown analytically and verified by simulations to be an efficient estimate, which can attain the CRLB performance under mild Gaussian noise assumption. This method is able to locate the source with the minimum number of sensors required for N-dimensional localization. Numerical simulations demonstrate significant performance improvement of the proposed method compared with the...
Constrained optimization of sensors trajectories for moving source localization using TDOA and FDOA measurements, Article International Conference on Robotics and Mechatronics, ICROM 2015, 7 October 2015 through 9 October 2015 ; 2015 , Pages 200-204 ; 9781467372343 (ISBN) ; Hamdollahzadeh, M ; Behnia, F ; Sharif University of Technology
This paper examines the problem of determining optimal sensors trajectories for localization of a moving radio source based on Time Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA) measurements in situations in which sensors are constrained both in their movements and regions of operation. By considering the movement of the source and constrained movement of the sensors, a constraint problem is formed which is solved to determine optimal trajectories of the sensors for source tracking. The validity of the proposed algorithm is assessed by two different simulation scenarios and the results verify its proper operation with estimation error decreasing in consecutive steps...
M.Sc. Thesis Sharif University of Technology ; Behnia, Feridoon
Localization of radio, acoustic and vibration wave’s sources by passive systems has many applications in positioning systems, navigation systems, wireless sensor networks, defense, security, and geophysics. RSS, AOA, TDOA, and FDOA are some of the techniques available for passive localization. The performance of TDOA/FDOA techniques does not degrade with distance and multipath, but it may suffer from poor performance for narrowband signals. On the other hand, FDOA technique requires narrowband signals. Thus, the combination of TDOA and FDOA can be suitable for a much wider range of sources. In addition, the TDOA/FDOA method can provide more accurate interference source localization compared...
Ph.D. Dissertation Sharif University of Technology ; Haj Sadeghi, Khosrow ; Pezeshk, Amir Mansour
In this thesis, the problem of locating a source based on the time difference of arrival (TDOA) has been studied and three different methods have been presented to improve the precision of the positioning. The first method is a solution to the least square error (LSE) by utilizing a convex problem. Generally, the mentioned problem is a non-convex problem; however, it is possible to convert it to a convex one by some relaxation in the constraints. By some justifiable and insightful relaxation, not only we obtain a convex problem, but also the exact solution of the main problem is derived. The second method is using high perturbed TDOA measurements which severely affected the localization....
Article 24th Iranian Conference on Electrical Engineering, 10 May 2016 through 12 May 2016 ; 2016 , Pages 525-528 ; 9781467387897 (ISBN) ; Amiri, R ; Behnia, F ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
In this paper, a sparsity-aware target localization method in multiple-input-multiple-output (MIMO) radars by utilizing time difference of arrival (TDOA) measurements is proposed. This method provides a maximum likelihood (ML) estimator for target position by employing compressive sensing techniques. Also, for fast convergence and mitigating the mismatch problem due to grid discretization, we address a block-based search coupled with an adaptive dictionary learning technique. The Cramer-Rao lower bound for this model is derived as a benchmark. Simulations results are included to verify the localization performance