A Statistical Information Method for Sensor-Target Geometry

Sung-Ho Kim and Joon Ha Park


Time difference of arrival, likelihood function, Fisher information, CramerRao lower bound, linear approximation, Gaussian noise


In this paper, the target localization problem using multiple sensors based on the time difference of arrival (TDOA) data is investigated under the assumption that the target is far off from the sensors. We examine the geometric features of the problem and compute the Fisher information matrix (FIM) and the Cramer-Rao lower bounds by using the power series expansion to analyze the variability of the angle and the range estimates. We found a relationship between sensor formation and tracking performance through the formula of the FIM.

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