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We propose an efficient solution to the state estimation problem inmulti-scan multi-sensor multiple extended target sensing scenarios. We firstmodel the measurement process by a doubly inhomogeneous-generalized shot noiseCox process and then estimate the parameters using a jump Markov chain MonteCarlo sampling technique. The proposed approach scales linearly in the numberof 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 algorithmoffers improvements in high clutter scenarios with closely spaced targets overstate-of-the-art clustering techniques used in existing multiple extendedtarget tracking algorithms.

Multi-Sensor Multi-Scan Radar Sensing of Multiple Extended Targets