Fabian D. Lapierre

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We address the problem of detecting slow-moving targets using space-time adaptive processing (STAP) radar. Determining the optimum weights at each range requires data snapshots at neighboring ranges. However, in virtually all configurations, snapshot statistics are range dependent, meaning that snapshots are nonstationary with respect to range. This results(More)
We address the problem of detecting slow-moving targets using a non-sideloking monostatic space-time adaptive processing (STAP) radar. The construction of optimum weights at each range implies the estimation of the clutter covariance matrix. This is typically done by straight averaging of neighboring data snapshots. The range-dependence of these snapshots(More)
Space-time adaptive processing (STAP) is a well-suited technique to detect slow-moving targets in the presence of a clutter-spreading environment. When considering the STAP system deployed with conformal radar array (CFA), the training data is range-dependent, which results in poor detection performance of traditional statistical-based algorithms. Current(More)
Ship-based automatic detection of small floating objects on an agitated sea surface remains a hard problem. Our main concern is the detection of floating mines, which proved a real threat to shipping in confined waterways during the first Gulf War, but applications include salvaging, search-and-rescue operation, perimeter, or harbour defense. Detection in(More)
For locating maritime vessels longer than 45 meters, such vessels are required to set up an Automatic Identification System (AIS) used by vessel traffic services. However, when a boat is shutting down its AIS, there are no means to detect it in open sea. In this paper, we use Electro-Optical (EO) imagers for noncooperative vessel detection when the AIS is(More)
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