This new book, cleverly entitledEeyondthe Kalman Filler, is by far the best book on filtering published in many years: It is clearly written, well organized, authoritative, and contains a wealth of useful information. It explains a state-of-the-art class of practical nonlinear filtering algorithms, called ''pam.de filrers," using examples and theory accessible to normal engineers. The book contains many examples of practical nonlinear filters worked out in detail. One of the best features of the book is the large variety of plots of filter estimation accuracy for different types of filters, including the extended Kalman filter (EKF), the unscented Kalman filter (UKF),
The generalized labeled multi-Bernoulli (GLMB) is a family of tractable models that alleviates the limitations of the Poisson family in dynamic Bayesian inference of point processes. In this paper, we derive closed form expressions for the void probability functional and the Cauchy-Schwarz divergence for GLMBs. The proposed analytic void probability functional is a necessary and sufficient statistic that uniquely characterizes a GLMB, while the proposed analytic Cauchy-Schwarz divergence provides a tractable measure of similarity between GLMBs. We demonstrate the use of both results on a partially observed Markov decision process for GLMBs, with Cauchy-Schwarz divergence based reward, and void probability constraint.Index Terms-Random finite sets, Poisson point process, generalized labelled multi-Bernoulli, information divergence M. Beard is with the Defence Science and Technology Organisation, Rockingham, WA, Australia, and also with the
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