Accuracy bounds of non-Gaussian Bayesian tracking in a NLOS environment

With a growth of the popularity of wireless sensor networks it has became obvious that Bayesian filter is most commonly used method for the sensor localization. The multiple sensors in one device allow to build different variations of the filter through the definition of the components of Bayesian rule. This paper presents a localization algorithm that is based on Bayesian filter and an attempt to evaluate the accuracy of the algorithm for nanoLOC technology. Empirical probability density functions for two Non-Line of Sight (NLOS) cases and different mobile object movement models have been used in the algorithm. The usage of extreme variants of the movement model allows to estimate the lower and upper bounds of the localization algorithm accuracy. Any movement sensor data incorporated into the algorithm produces the algorithm variant where accuracy is within the bounds. Also, the paper presents a technique for recovering the probability density function of distance overestimates from biased measurements and proposes relative accuracy estimate based on the least squares method. The estimate can be used instead of Cramer-Rao lower bound when there is no analytical probability density function.