We study the problem of estimating a random process from the observations collected by a
network of sensors that operate under resource constraints. When the dynamics of the …

Active perception strategies enable an agent to selectively gather information in a way to
improve its performance. In applications in which the agent does not have prior knowledge …

Given a linear dynamical system, we provide a probabilistic treatment to the classic problem
of placing sensors in a set of candidate locations such that the observability Gramian of the …

We study the problem of selecting most informative subset of a large observation set to
enable accurate estimation of unknown parameters. This problem arises in a variety of …

Many problems in signal processing and machine learning can be formalized as weak
submodular optimization tasks. For such problems, a simple greedy algorithm (Greedy) is …

We consider the time-varying actuator placement in continuous time, where the goal is to
maximize the trace of the controllability Grammian. A natural relaxation of the problem is to …

Given a nonlinear dynamical system, this letter considers the problem of selecting a subset
of the total set of sensors that has provable guarantees on standard metrics related to the …

Recently, the world has witnessed a technology revolution in many sectors and fields with
the aim to enhance the quality of life and the human security. Particularly, the integration of …

As both the number and size of satellite constellations continue to increase, there likewise
exists a growing need for incorporating methods for autonomous sensor selection into these …

The problem of organizing data that evolves over time into clusters is encountered in a
number of practical settings. We introduce evolutionary subspace clustering, a method …