Decentralized Tracking with Feedback, Adaptive Sample Rate and IMM.

摘要

This report compares filter architectures for target tracking with multiple sensors. Two decentralized architectures are implemented and evaluated in simulations against a central tracking filter. Data from an array radar and an infrared sensor are used. The central filter is an interactive multiple models filter (IMM) with three models. These models are extended Kalman filters. The decentralized architectures have two sensor filters and one central filter that fuses the tracks from the sensor filters. The filter architectures are evaluated with respect to their accuracy and ability to perform adaptive sampling.