The use of multiple scans of data to improve ones ability to improve target tracking performance is widespread
in the tracking literature. In this paper, we introduce a novel application of a recent innovation in the SMC
literature that uses multiple scans of data to improve the stochastic approximation (and so the data association
ability) of a multiple target Sequential Monte Carlo based tracking system. Such an improvement is achieved
by resimulating sampled variates over a fixed-lag time window by artificially extending the space of the target
distribution. In doing so, the stochastic approximation is improved and so the data association ambiguity is
more readily resolved.