This paper presents methods for the estimation of the time-varying directions of arrival
(DOAs) of signals emitted by moving sources. Following the sparse Bayesian learning (SBL) …

In this work, we propose a sparsity-exploiting dynamic rainfall monitoring methodology using
rain-induced attenuation measurements from microwave links. To estimate rainfall field …

In today's society, we are flooded with massive volumes of data in the order of a billion
gigabytes on a daily basis from pervasive sensors. It is becoming increasingly challenging to …

Sensor selection is an important design task in sensor networks. We consider the problem of
adaptive sensor selection for applications in which the observations follow a non-linear …

A coordinated dynamic sensor network of autonomous underwater gliders to estimate three-
dimensional time-varying environmental fields is proposed and tested. Integration with a …

Compressive sensing (CS) techniques for estimating the direction-of-arrival (DoA) stand
apart from traditional approaches due to their ability to derive DoA information from just a …

Tracking multiple moving targets is known to be challenged by the nonlinearity present in
the measurement equation, and by the computationally burdensome data association task …

We consider tracking of multiple objects using a wireless sensor network where distributed
nodes transmit to a fusion center using random access. During an initialization phase …

In this paper, we will investigate an adaptive compression scheme for tracking time-varying
sparse signals with possibly varying sparsity patterns and/or order. In particular, we will …

In this paper, we consider the 2-D direction-of-arrival (DOA) tracking problem. The signals
are captured by a uniform spherical array and therefore can be analyzed in the spherical …