Abstract:
This paper presents a fixed lag smoothing
algorithm for target tracking in clutter. The proposed
algorithm is based on Integrated Probabilistic
Data Association (IPDA) approach. The algorithm
runs two filters for each track - one in forward direction
(as standard IPDA) and the other in backward
direction. A standard fusion of both the target
existence probability and state estimates of both the
filters at a fixed time lag yields the smoothed probability
densities of both of them. The paper also
presents the refined target dynamics and target existence
transition model for backward running IPDA
filter. Simulation results are also presented to compare
the performance (in terms of true track detection,
false track discrimination) of the proposed
IPDA smoother with that of Augmented State IPDA
filter and standard IPDA filter.
