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Distributed localization in sensor networks with noisy distance measurements

This paper investigates the distributed localization problem for sensor networks with noisy distance measurements. A distributed iterative algorithm called ECHO-MN is presented based on the signed barycentric coordinate representation, which can be calculated by relative distance measurements. The measurement noise model is presented followed by an unbiased distance estimator which utilizes the past measurement information. Taking advantage of the estimator each sensor node to be located updates its estimates of internode distances and calculates the barycentric coordinate before localization iteration. To attenuate the effect of measurement noise, a gain parameter which decay to zero is used in the algorithm. It is proved that ECHO-MN converges to the exact location of each sensor almost surely under some necessary conditions. Numerical studies illustrate the proposed localization algorithm.