  We propose an efficient solution to the state estimation problem in
multi-scan multi-sensor multiple extended target sensing scenarios. We first
model the measurement process by a doubly inhomogeneous-generalized shot noise
Cox process and then estimate the parameters using a jump Markov chain Monte
Carlo sampling technique. The proposed approach scales linearly in the number
of measurements and can take spatial properties of the sensors into account,
herein, sensor noise covariance, detection probability, and resolution.
Numerical experiments using radar measurement data suggest that the algorithm
offers improvements in high clutter scenarios with closely spaced targets over
state-of-the-art clustering techniques used in existing multiple extended
target tracking algorithms.
