2022/11/25 by Mengwei Sun, Mike E. Davies, Sun, Mengwei +5 · 1 citation
Computer Science · Engineering · Environmental Science · #Advanced Chemical Sensor Technologies #Air Quality Monitoring and Forecasting #FOS: Computer and information sciences #FOS: Electrical engineering #Gaussian Processes and Bayesian Inference #Robotics (cs.RO) #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2211.14162
openalex publication_date 2022/11/25 · openalex created_date 2022/11/30 · openalex updated_date 2026/07/28
Maneuvering target tracking is a challenging problem for sensor systems because of the unpredictability of the targets' motions. This paper proposes a novel data-driven method for learning the dynamical motion model of a target. Non-parametric Gaussian process regression (GPR) is used to learn a target's naturally shift invariant motion (NSIM) behavior, which is translationally invariant and does not need to be constantly updated as the target moves. The learned Gaussian processes (GPs) can be applied to track targets within different surveillance regions from the surveillance region of the training data by being incorporated into the particle filter (PF) implementation. The performance of our proposed approach is evaluated over different maneuvering scenarios by being compared with commonly used interacting multiple model (IMM)-PF methods and provides around 90% performance improvement for a multi-target tracking (MTT) highly maneuvering scenario.