Data association for simultaneous localization and mapping in robotic wireless sensor networks

Joint Probabilistic Data Association (JPDA) technique can be applied for locating and tracking the radiated sources in dynamic and ad hoc wireless sensor networks (WSN). Vice versa, the located sensor nodes in the WSN network can help mapping the environment which is covered by their RF communication links. However, the sensed information may be corrupted by the ambient clutter or RF interference that causes error in data association, and results in catastrophic error for simultaneous localization and mapping (SLAM). We propose a semi-temporal algorithm using three-scan JPDA to accurately correlate the observation with its corresponding radiated source as the landmark, and identify the new source as the new landmark. The existence of moving clutter in validation gates is alerted by a statistic motion detector that enhances data association in a dynamic environment. This method can be applied for real-time SLAM applications with less complexity comparing with other high-cost optimal Bayesian filter. Simulation is performed to verify the effectiveness of method.