Performance Evaluation of a Decentralized Multitarget-Tracking Algorithm Using a LIDAR Sensor Network With Stationary Beams

A LIght Detection And Ranging (LIDAR) sensor network to track walking persons inside a surveillance area is investigated. A small number of sensor nodes with spatially stationary and partially overlapping narrow LIDAR beams are chosen in order to keep the costs to a minimum. As a consequence of this network topology, the area of surveillance is not fully covered with LIDAR beams, and thus, accurate tracking of persons walking inside the area of surveillance is challenging, particularly in a multitarget situation. To tackle this problem, multitarget tracking based on a sophisticated decentralized track-to-track fusion architecture is developed and evaluated in this paper: Dynamic multihypothesis tracking (MHT) by independent local trackers is carried out in all sensor nodes; then, local track favorites are sent to a fusion center, where global track candidates are derived and fed back to the local trackers in order to improve local tracking. With this architecture, a track association success rate of (98.8 ± 0.3)% and a mean square position error of Δp = 6.7 cm were derived from 1000 random pairs of intersecting trajectories of two persons walking (mean velocity 1.5 m/s) across a rectangular surveillance area of size 20 m × 10 m. The tracking performances as functions of target velocity
v
and target radius
r
were quantified. Furthermore, the tracking performances as functions of the distance measurement error Δ
L
and beamwidth 2β as the most important parameters were obtained. The performance of the proposed algorithm was also experimentally evaluated.