5,396 research outputs found
Quickest Change Detection of a Markov Process Across a Sensor Array
Recent attention in quickest change detection in the multi-sensor setting has
been on the case where the densities of the observations change at the same
instant at all the sensors due to the disruption. In this work, a more general
scenario is considered where the change propagates across the sensors, and its
propagation can be modeled as a Markov process. A centralized, Bayesian version
of this problem, with a fusion center that has perfect information about the
observations and a priori knowledge of the statistics of the change process, is
considered. The problem of minimizing the average detection delay subject to
false alarm constraints is formulated as a partially observable Markov decision
process (POMDP). Insights into the structure of the optimal stopping rule are
presented. In the limiting case of rare disruptions, we show that the structure
of the optimal test reduces to thresholding the a posteriori probability of the
hypothesis that no change has happened. We establish the asymptotic optimality
(in the vanishing false alarm probability regime) of this threshold test under
a certain condition on the Kullback-Leibler (K-L) divergence between the post-
and the pre-change densities. In the special case of near-instantaneous change
propagation across the sensors, this condition reduces to the mild condition
that the K-L divergence be positive. Numerical studies show that this low
complexity threshold test results in a substantial improvement in performance
over naive tests such as a single-sensor test or a test that wrongly assumes
that the change propagates instantaneously.Comment: 40 pages, 5 figures, Submitted to IEEE Trans. Inform. Theor
Limited Visibility and Uncertainty Aware Motion Planning for Automated Driving
Adverse weather conditions and occlusions in urban environments result in
impaired perception. The uncertainties are handled in different modules of an
automated vehicle, ranging from sensor level over situation prediction until
motion planning. This paper focuses on motion planning given an uncertain
environment model with occlusions. We present a method to remain collision free
for the worst-case evolution of the given scene. We define criteria that
measure the available margins to a collision while considering visibility and
interactions, and consequently integrate conditions that apply these criteria
into an optimization-based motion planner. We show the generality of our method
by validating it in several distinct urban scenarios
Balancing antagonistic time and resource utilization constraints in over-subscribed scheduling problems
In this paper, we report work aimed at applying concepts of constraint-based problem structuring and multi-perspective scheduling to an over-subscribed scheduling problem. Previous research has demonstrated the utility of these concepts as a means for effectively balancing conflicting objectives in constraint-relaxable scheduling problems, and our goal here is to provide evidence of their similar potential in the context of HST observation scheduling. To this end, we define and experimentally assess the performance of two time-bounded heuristic scheduling strategies in balancing the tradeoff between resource setup time minimization and satisfaction of absolute time constraints. The first strategy considered is motivated by dispatch-based manufacturing scheduling research, and employs a problem decomposition that concentrates local search on minimizing resource idle time due to setup activities. The second is motivated by research in opportunistic scheduling and advocates a problem decomposition that focuses attention on the goal activities that have the tightest temporal constraints. Analysis of experimental results gives evidence of differential superiority on the part of each strategy in different problem solving circumstances. A composite strategy based on recognition of characteristics of the current problem solving state is then defined and tested to illustrate the potential benefits of constraint-based problem structuring and multi-perspective scheduling in over-subscribe scheduling problems
Trans-dimensional inversion of modal dispersion data on the New England Mud Patch
© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Bonnel, J., Dosso, S. E., Eleftherakis, D., & Chapman, N. R. Trans-dimensional inversion of modal dispersion data on the New England Mud Patch. IEEE Journal of Oceanic Engineering, 45(1), (2020): 116-130, doi:10.1109/JOE.2019.2896389.This paper presents single receiver geoacoustic inversion of two independent data sets recorded during the 2017 seabed characterization experiment on the New England Mud Patch. In the experimental area, the water depth is around 70 m, and the seabed is characterized by an upper layer of fine grained sediments with clay (i.e., mud). The first data set considered in this paper is a combustive sound source signal, and the second is a chirp emitted by a J15 source. These two data sets provide differing information on the geoacoustic properties of the seabed, as a result of their differing frequency content, and the dispersion properties of the environment. For both data sets, source/receiver range is about 7 km, and modal time-frequency dispersion curves are estimated using warping. Estimated dispersion curves are then used as input data for a Bayesian trans-dimensional inversion algorithm. Subbottom layering and geoacoustic parameters (sound speed and density) are thus inferred from the data. This paper highlights important properties of the mud, consistent with independent in situ measurements. It also demonstrates how information content differs for two data sets collected on reciprocal tracks, but with different acoustic sources and modal content.10.13039/100000006-Office of Naval Research
10.13039/100007297-Office of Naval Research Globa
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