Clutter-sensitive data association for simultaneous localization and mapping in robotic wireless sensor networks

Abstract

Joint Probabilistic Data Association (JPDA) technique can be applied for locating and tracking the radiated sources in dynamic and ad hoc wireless sensor networks. Vice versa, the located sensor nodes in the network can help mapping the indoor environment connected by their RF communication links. However, the sensed information may be corrupted by the clutter (ambient noise and RF interference) that cause error in data association, and results in catastrophic effect on simultaneous localization and mapping (SLAM). We propose a spatial-temporal algorithm using three-scan JPDA to set up the correlation between the observation and its originating radiated source, along with a statistic motion detector that detects the existence of moving clutter in validation gates. 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.