  Target tracking faces the challenge in coping with large volumes of data
which requires efficient methods for real time applications. The complexity
considered in this paper is when there is a large number of measurements which
are required to be processed at each time step. Sequential Markov chain Monte
Carlo (MCMC) has been shown to be a promising approach to target tracking in
complex environments, especially when dealing with clutter. However, a large
number of measurements usually results in large processing requirements. This
paper goes beyond the current state-of-the-art and presents a novel Sequential
MCMC approach that can overcome this challenge through adaptively subsampling
the set of measurements. Instead of using the whole large volume of available
data, the proposed algorithm performs a trade off between the number of
measurements to be used and the desired accuracy of the estimates to be
obtained in the presence of clutter. We show results with large improvements in
processing time, more than 40% with a negligible loss in tracking performance,
compared with the solution without subsampling.
