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In multi-object tracking one may encounter situations were at any time stepthe number of possible hypotheses is too large to generate exhaustively. Thesesituations generally occur when there are multiple ambiguous measurementreturns that can be associated to many objects. This paper contains a newlydeveloped approach that keeps the aforementioned situations computationallytractable. Utilizing a hypothesis level derivation of the Finite Set Statistics(FISST) Bayesian recursions for multi-object tracking we are able to propose arandomized method called randomized FISST (R-FISST). Like our previous methods,this approach utilizes Markov Chain Monte Carlo (MCMC) methods to sample highlyprobable hypotheses, however, the newly developed (R-FISST) can account forhypotheses containing multiple births and death within the MCMC sampling. Thisalleviates the burden of having to exhaustively enumerate all birth and deathhypotheses and makes the method more equipped to handle spawn scenarios. Wetest our method on Space Situational Awareness (SSA) scenarios with spawnevents.

Multi-Object Tracking with Multiple Birth, Death, and Spawn Scenarios Using A Randomized Hypothesis Generation Technique (R-FISST)