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I am not sure if this is the right place to ask and I'm new here.

I'm a Ph.D. student (electronics engineering) studying mainly on (multi) target tracking. The common approach is to use a Bayesian approach, i.e. (extended, unscented) Kalman filters. But these filters are actually for single target tracking, and there are some sub-problems, like data association, track initiation etc.

However, more recent studies (relatively recent) focus on models based on Random Finite Sets (RFS), or Point Processes. These approaches bypass some of the sub-problems above. So, I want to focus on these, in a bit more detail (from an engineering perspective, if possible).

Unfortunately, there is no lecture/seminar or similar activities around, so my main are scientific papers. But they are generally brief and others. For now, my main sources are a Ph.D. dissertation linked: http://randomsets.eps.hw.ac.uk/btvthesis_FINAL_FINAL.pdf and a summer school lecture note linked: https://arxiv.org/pdf/1308.2586.pdf.

Can you point me to some resources on Point Processes/RFS/Finite Set Statistics from an engineering point of view (if possible)?

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