GaPP: Multi-Target Tracking with Gaussian Processes

Abstract

Multi-target tracking of agile targets can be limited by choice of dynamical models. This is typically overcome by using sophisticated non-Gaussian and/or nonlinear motion models, and complex data association schemes. Here we aim to tackle scenarios when tracking (semi-)autonomous systems, such as drones, which often follow smooth optimised trajectories and undertake rapid manoeuvres when needed. This paper introduces a novel, flexible, multi-target tracking approach based upon a Gaussian process as a dynamical model, coupled with a non-homogeneous Poisson process for the observation model. It applies a particle filtering inference method for state estimation (including data association) and online parameter learning. The promising performance of the proposed technique is demonstrated on both synthetic data and real drone surveillance radar measurements, compared with a selection of more standard approaches.