Dr Jason Williams wins best paper award in IEEE Transactions on Aerospace and Electronic Systems
We are very proud to share that earlier this month Dr Jason Williams, one of our Senior Research Scientists, won the renowned M. Barry Carlton Award which acknowledges what is judged to be the best paper in IEEE Transactions on Aerospace and Electronic Systems (TAES).
The Carlton Award considers papers published four years earlier in TAES. As such, this year’s Carlton Award is the 2015 M. Barry Carlton Award.
Dr Jason Williams won the 2015 award for his paper entitled “Marginal Multi-Bernoulli Filters: RFS Derivation of MHT, JIPDA, and Association-Based MeMBer”, which appeared in the June/July issue of TAES.
In the words of Michael Rice, IEEE Editor-in-Chief:
“Random finite set (RFS) methods are used in statistical inference problems in which the variables of interest or the observations form finite sets. RFS methods apply to multiple-target tracking problems where the number of targets is unknown, the measurements are unordered, and measurement-to-target correspondence (data association) is unknown.
In this paper, Williams derives a form of the full Bayes RFS filter and shows that the RFS filter comprises an alternate derivation to some well-known techniques. Further, he identifies an implicit data association in the derivation and shows that approximations to the data association produce both alternate derivations of existing techniques and possible improvements of those techniques.
The paper was nominated by the AESS Publications team and endorsed by Florian Meyer and Stefano Coraluppi. Dr. Meyer finds this paper to be “an outstanding and highly innovative paper” that is “clearly organized, comprehensive yet concise, and very carefully written. The presentation and style are excellent and the mathematical analysis is groundbreaking.” Dr. Coraluppi writes that this paper, “provides the essential mathematical approximations leading to the highly promising and ongoing development of belief propagation as a paradigm for multiple-target tracking.”
I find this paper to be a very nice read. I appreciate the way this paper creates a framework in which a number of multiple-target tracking techniques can be interpreted and generalized.”
Huge congratulations to Jason!