Cost-Based Monte Carlo Sampling Approaches for Sensor Self-Localization Under Beacon Position Uncertainty

Sensor localization methods based on Monte Carlo sampling approximate the sensor position distributions by a weighted set of samples. These approaches traditionally require complete knowledge of the probabilistic distributions of the uncertainties in the sensor system. In this paper, we propose alternative sampling-based methods which do not require complete knowledge of the probabilistic distributions. The sensor position distributions are represented by a set of samples and costs which are described by spatial parametric regions. Few parameters are needed to characterize these regions, and therefore the amount of information to be transmitted to the rest of the sensors to self-localize is simplified. Computer simulations show that the proposed methods are more robust and less computationally intensive than standard sampling approaches.