| Paper: | SAM-P2.8 | ||
| Session: | Detection and Estimation | ||
| Time: | Tuesday, May 18, 15:30 - 17:30 | ||
| Presentation: | Poster | ||
| Topic: | Sensor Array and Multichannel Signal Processing: Source localization, separation, classification, and tracking | ||
| Title: | NOVEL CLOSED-FORM ML POSITION ESTIMATOR FOR HYPERBOLIC LOCATION | ||
| Authors: | Andreu Urruela; Universitat Politècnica de Catalunya (UPC) | ||
| Jaume Riba; Universitat Politècnica de Catalunya (UPC) | |||
| Abstract: | Geolocation of mobile terminals has become in the last decades an important issue in mobile networks. In the literature, there have been presented several closed-form position estimators based on Time-difference-of-arrival (TDOA) measurements. Only Fang's estimator can be considered optimum in the Maximum Likelihood (ML) sense. Unfortunately, it can only be applied to the particular case of two TDOA measurements for the two dimensional (2D) location case. This paper presents an extension of this closed-form estimator to be applied to an arbitrary number of TDOA measurements by means of a transformation in the Maximum Likelihood function. This allows to split the ML function minimization in several partial ML minimizations which only consider a subset of the available measurements where the original Fang's estimator can be applied. Numerical simulation show that the proposed algorithm attains the theoretical limits for all range of reasonable SNR values and has a low implementation complexity. | ||
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