  This paper proposes an efficient and robust algorithm to estimate target
trajectories with unknown target detection profiles and clutter rates using
measurements from multiple sensors. In particular, we propose to combine the
multi-sensor Generalized Labeled Multi-Bernoulli (MS-GLMB) filter to estimate
target trajectories and robust Cardinalized Probability Hypothesis Density
(CPHD) filters to estimate the clutter rates. The target detection probability
is augmented to the filtering state space for joint estimation. Experimental
results show that the proposed robust filter exhibits near-optimal performance
in the sense that it is comparable to the optimal MS-GLMB operating with true
clutter rate and detection probability. More importantly, it outperforms other
studied filters when the detection profile and clutter rate are unknown and
time-variant. This is attributed to the ability of the robust filter to learn
the background parameters on-the-fly.
