  We propose and evaluate an iterative localization mechanism employing
Bayesian inference to estimate the position of a target using received signal
strength measurements. The probability density functions of the target's
coordinates are estimated through a Bayesian network. Herein, we consider an
iterative procedure whereby our predictor (posterior distribution) is updated
in a sequential order whenever new measurements are made available. The
performance of the mechanism is assessed in terms of the respective root mean
square error and kernel density estimation of the target coordinates. Our
numerical results showed the proposed iterative mechanism achieves increasingly
better estimation of the target node position each updating round of the
Bayesian network with new input measurements.
