ML-based sensor network localization and tracking: Batch and time-recursive approaches

Simultaneous localization and tracking (SLAT) in sensor networks aims to determine the positions of sensor nodes and a moving target in a network, given incomplete and inaccurate range measurements. One of the established methods for achieving this goal is to maximize a likelihood function (ML), which requires initialization with an approximate solution to avoid convergence towards local extrema. In this paper a Euclidean Distance Matrix (EDM) completion problem is solved to obtain initial sensor/target positions. The likelihood function is then iteratively optimized through either a Majorization-Minimization (MM) or Newton method. To reduce the computational load, an incremental scheme is proposed whereby each new target position is estimated from range measurements, providing additional initialization for ML without the need for solving an expanded EDM completion problem. The performance of these methods is assessed through simulation.