Bayesian filtering for location estimation

The use of bayesian-filter technology as a statistical tool in order to help manage measurement uncertainity and to perform multisensor fusion and identity estimation, is discussed. The fusion of sensor data from ultrasound and infrared tags and combining of high-resolution location information from asynchronous laser range finders with low-resolution location sensors that provide information is also discussed. Bayes filters probabilistically estimate a dynamic system's state from noisy observations. The identity-estimation problem is solved by using a combination of particle filters and Kalman filters.