  This paper considers the data association problem for multi-target tracking.
Multiple hypothesis tracking is a popular algorithm for solving this problem
but it is NP-hard and is is quite complicated for a large number of targets or
for tracking maneuvering targets. To improve tracking performance and enhance
robustness, we propose a randomized multiple model multiple hypothesis tracking
method, which has three distinctive advantages. First, it yields a randomized
data association solution which maximizes the expectation of the logarithm of
the posterior probability and can be solved efficiently by linear programming.
Next, the state estimation performance is improved by the random coefficient
matrices Kalman filter, which mitigates the difficulty introduced by randomized
data association, i.e., where the coefficient matrices of the dynamic system
are random. Third, the probability that the target follows a specific dynamic
model is derived by jointly optimizing the multiple possible models and data
association hypotheses, and it does not require prior mode transition
probabilities. Thus, it is more robust for tracking multiple maneuvering
targets. Simulations demonstrate the efficiency and superior results of the
proposed algorithm over interacting multiple model multiple hypothesis
tracking.
