Experimental evaluation of simultaneous 3D localization of sensor nodes and tracking moving targets

In this work we carry out an experimental performance characterization of a simultaneous localization and tracking (SLAT) algorithm for sensor networks, whose aim is to determine the positions of sensor nodes and a moving target in a network, given incomplete and inaccurate range measurements between the target and each of the sensors. To achieve this, we propose to iteratively maximize a likelihood function (ML) of positions given the observed ranges, which requires initialization with an approximate solution to avoid convergence towards local extrema. A modified Euclidean Distance Matrix (EDM) completion problem is solved for a block of target range measurements to approximately set up initial sensor/target positions, and the likelihood function is then iteratively refined through Majorization-Minimization (MM). To reduce the computational load, an incremental scheme is used whereby each new target or sensor position is estimated from range measurements, providing additional initialization for ML without the need for solving an expanded EDM completion problem. The proposed algorithms are experimentally evaluated with a series of 3D indoor tests for a range of operation of up to ten meters using a Crossbow Cricket location system and a robotic or human target. Centimetric accuracy is obtained under realistic conditions.