Indoor positioning systems (IPS) use sensors and communication technologies to locate
objects in indoor environments. IPS are attracting scientific and enterprise interest because …

Statistical signal processing applications usually require the estimation of some parameters
of interest given a set of observed data. These estimates are typically obtained either by …

In machine learning, if the training data is an unbiased sample of an underlying distribution,
then the learned classification function will make accurate predictions for new samples …

Recently, tracking-by-detection has become a popular paradigm in Multiple-object tracking
(MOT) for its concise pipeline. Many current works first associate the detections to form track …

The particle filter was popularized in the early 1990s and has been used for solving
estimation problems ever since. The standard algorithm can be understood and …

The development of dual-functional radar-communication (DFRC) systems, where vehicle
localization and tracking can be combined with vehicular communication, will lead to more …

Two decades ago, with the publication, we witnessed the rebirth of particle filtering (PF) as a
methodology for sequential signal processing. Since then, PF has become very popular …

Location awareness is vital for emerging Internet-of-Things applications and opens a new
era for Localization-of-Things. This paper first reviews the classical localization techniques …

The particle filter (PF) was introduced in 1993 as a numerical approximation to the nonlinear
Bayesian filtering problem, and there is today a rather mature theory as well as a number of …

Simulation-based inference (SBI) solves statistical inverse problems by repeatedly running a
stochastic simulator and inferring posterior distributions from model-simulations. To improve …