simultaneous localization, calibration, and tracking in an ad hoc sensor network
We introduce Simultaneous Localization and Tracking, called SLAT, the problem of tracking a target in a sensor network while simultaneously localizing and calibrating the nodes of the network. Our proposed solution, LaSLAT, is a Bayesian filter that provides on-line probabilistic estimates of sensor locations and target tracks. It does not require globally accessible beacon signals or accurate ranging between the nodes. Real hardware experiments are presented for 2D and 3D, indoor and outdoor, and ultrasound and audible ranging-hardware-based deployments. Results demonstrate rapid convergence and high positioning accuracy.
localization calibration tracking sensor sensor networks network filtering locatability access signaling hardware experience 3d deployment demonstrate convergence posit accuracy wireless sensor networks position estimation statistical machine learning ad-hoc