  This paper addresses the problem of fixed motion and measurement models for
multi-target filtering using an adaptive learning framework. This is performed
by defining target tuples with random finite set terminology and utilisation of
recurrent neural networks with a long short-term memory architecture. A novel
data association algorithm compatible with the predicted tracklet tuples is
proposed, enabling the update of occluded targets, in addition to assigning
birth, survival and death of targets. The algorithm is evaluated over a
commonly used filtering simulation scenario, with highly promising results.
