2016/03/13 by W. Faber, Faber, W., Suman Chakravorty +3
Computer Science · #Applications (stat.AP) #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Statistics Theory (math.ST) #Target Tracking and Data Fusion in Sensor Networks
paper · pdf · doi:10.48550/arxiv.1603.04096
openalex publication_date 2016/03/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we present a randomized version of the finite set statistics (FISST) Bayesian recursions for multi-object tracking problems. We propose a hypothesis level derivation of the FISST equations that shows that the multi-object tracking problem may be considered as a finite state space Bayesian filtering problem, albeit with a growing state space. We further show that the FISST and Multi-Hypothesis Tracking (MHT) methods for multi-target tracking are essentially the same. We propose a randomized scheme, termed randomized FISST (R-FISST), where we sample the highly likely hypotheses using Markov Chain Monte Carlo (MCMC) methods which allows us to keep the problem computationally tractable. We apply the R-FISST technique to a fifty-object birth and death Space Situational Awareness (SSA) tracking and detection problem. We also compare the R-FISST technique to the Hypothesis Oriented Multiple Hypothesis Tracking (HOMHT) method using an SSA example.