Random finite set Markov Chain Monte Carlo predetection fusion

Predetection fusion is an efficient (and, depending on what underlies it, indispensable) way to process high volume data from large networks of low quality sensors and thus, an aid to multisensor multitarget tracking. In previous work we derived both the GLRT (presumably “optimal”) technique and a more practicable contact-sifting variant. Unfortunately, the gaps between the two in terms of computation time and performance are not inconsiderable. Hence in this paper we propose a new approach based on random finite sets (RFS) and implemented by Monte Carlo (MCMC) simulation. We trust that it is found interesting; but even if not, we show that it offers improved results, in the sense of RMSE and number of declared targets.