David Krout Principal Engineer Affiliate Assistant Professor, Electrical Engineering dkrout@apl.washington.edu Phone 206-616-2589 |

Publications |
2000-present and while at APL-UW |

Constrained robust submodular sensor selection with applications to multistatic sonar arrays Powers, T., J. Bilmes, D.W. Krout, and L. Atlas, "Constrained robust submodular sensor selection with applications to multistatic sonar arrays," Proc., FUSION — 19th International Conference on Information Fusion, 5-8 July, 2179-2185 (IEEE, 2016). |
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4 Aug 2016 |
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We develop a framework to select a subset of sensors from a field in which the sensors have an ingrained independence structure. Given an arbitrary independence pattern, we construct a graph that denotes pairwise independence between sensors, which means those sensors may operate simultaneously. The set of all fully-connected subgraphs (cliques) of this independence graph forms the independent sets of a matroid over which we maximize the minimum of a set of submodular objective functions. We propose a novel algorithm called MatSat that exploits submodularity and, as a result, returns a near-optimal solution with approximation guarantees that are within a small factor of the average-case scenario. We apply this framework to ping sequence optimization for active multistatic sonar arrays by maximizing sensor coverage and derive lower bounds for minimum probability of detection for a fractional number of targets. In these ping sequence optimization simulations, MatSat exceeds the fractional lower bounds and reaches near-optimal performance. |

Contact clustering and classification using likelihood-based similarities Hanusa, E., M.R. Gupta, and D.W. Krout, "Contact clustering and classification using likelihood-based similarities," Proc., MTS/IEEE Oceans 2012, 14-19 October, Hampton Road, VA, doi:10.1109/OCEANS.2012.6404928 (MTS/IEEE, 2012). |
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14 Oct 2012 |
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This paper presents the results of using a likelihood-based clustering step before tracking on a multistatic sonar step. The likelihood-based clustering appropriately models the measurement noise and allows for the incorporation of features. The clustering step also allows for the rejection of clutter and fusion of the contact measurements within a cluster. After clustering, fusion and classification, the tracking results are improved over previous preprocessing methods. Results are shown for the three scenarios in the PACSim dataset. |

Tracking drifting surface objects with aerial infrared and electro-optical sensors Krout, D.W., G. Okopal, A. Jessup, and E. Hanusa, "Tracking drifting surface objects with aerial infrared and electro-optical sensors," Proc., MTS/IEEE Oceans 2012, 14-19 October, Hampton Roads, VA, doi:10.1109/OCEANS.2012.6404804 (MTS/IEEE, 2012). |
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14 Oct 2012 |
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Recently, researchers at the Applied Physics Laboratory at the University of Washington collected a unique dataset by suspending two cameras, one infrared and one electro-optical, from a balloon. This apparatus was then used to image objects drifting on the surface of Lake Washington. The authors took that data and built a processing stream to track the movements of those drifting surface objects. |