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