Improving Tracking by Integrating Reliability of Multiple Sources

Tracking algorithms are often designed around optimistic assumptions on uncertainty model. Handling with conflicting data, however, requires specific strategies, that consider quality of information sources. To improve performance of tracking systems, the use of reliability, as evaluation of quality of data sources, has been proved to be a promising technique. In this paper we show how to use reliability of information sources to increase performance of tracking methods, using two different strategies: discount and pruning. We apply those two strategies in two different scenarios: landmark based mobile robot localization using Extended Kalman Filter and multi-agent object-tracking using Particle Filter. Experimental results show effectiveness of proposed methodology.