A Reliability of Measurement Based Algorithm for Adaptive Estimation in Sensor Networks
For sensor network applications requiring distributed estimation, this work offers an incremental improvement by adapting step-size to sensor reliability.
The paper addresses the issue of varying measurement reliability in sensor networks for adaptive estimation. The proposed algorithm, which adjusts step-size based on estimated observation noise variance, improves the performance of the incremental distributed least mean-square (IDLMS) algorithm, as shown in simulations.
In this paper we consider the issue of reliability of measurements in distributed adaptive estimation problem. To this aim, we assume a sensor network with different observation noise variance among the sensors and propose new estimation method based on incremental distributed least mean-square (IDLMS) algorithm. The proposed method contains two phases: I) Estimation of each sensors observation noise variance, and II) Estimation of the desired parameter using the estimated observation variances. To deal with the reliability of measurements, in the second phase of the proposed algorithm, the step-size parameter is adjusted for each sensor according to its observation noise variance. As our simulation results show, the proposed algorithm considerably improves the performance of the IDLMS algorithm in the same condition.