Tracking Multiple Vehicles Constrained to a Road Network from a UAV with Sparse Visual Measurements
Craig C. Bidstrup, Jared J. Moore, Cameron K. Peterson, Randal W. Beard · 2019
Many multiple target tracking algorithms operate in the local frame of the sensor and have difficulty with track reallocation when targets move in and out of the sensor field of view. This poses a problem when an unmanned aerial vehicle (UAV) is tracking multiple ground targets on a road network larger than its field of view. We propose a Rao-Blackwellized Particle Filter (RBPF) to maintain individual target tracks and to perform probabilistic data association when the targets are constrained to a road network. This is particularly useful when a target leaves then re-enters the UAV's field of view. The RBPF is structured as a particle filter of particle filters. The top level filter handles data association and each of its particles maintains a bank of particle filters to handle target tracking. The tracking particle filters incorporate both positive and negative information when a measurement is received. We then implement a receding horizon controller to improve the filter certainty of multiple target locations. The controller prioritizes searching for targets based on the entropy of each target's estimate.