Fabian D. Lapierre

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We first generalize the concept of clutter power spectrum locus so that it can be applied to arbitrary antenna arrays. This locus is a curve in the 4D space of the Doppler frequency and the 3 spatial frequencies. This generalization is valid for both monostatic and bistatic radar configurations. We show that the customary clutter power spectrum locus(More)
The increasing interest for arbitrary antenna arrays in radar space-time adaptive processing (STAP) creates a need for a thorough understanding of the role of, and dependencies between, spatial and Doppler frequencies and related quantities, especially in the characterization of clutter. We successively introduce " geometrical " and statistical concepts,(More)
We address the problem of detecting slow-moving targets using a 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 generally results in poor(More)
The problem of detecting slow-moving targets using a space-time adaptive processing (STAP) radar is addressed. The determination of the optimum interference-rejection weights at each range is based on snapshots at neighbouring ranges. However, in virtually all bistatic configurations or/and when using conformal antenna arrays (CAA), snapshot statistics are(More)
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 space-time adaptive processing (STAP). The construction of the optimum weights at each range implies the estimation of the interference-plus-noise covariance matrix. This is typically done by straight averaging of snapshots at neighboring ranges. However, in most bistatic configurations,(More)
—We address the problem of detecting slow-moving targets using a 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. However, in bistatic configurations, these snapshots are(More)
Radar space-time adaptive processing (STAP) is a well-suited technique to detect slow-moving targets in the presence of a strong interference background. We consider STAP for a radar operating in a bistatic radar configuration and collecting returns with a conformal antenna array (CAA). The statistics of the secondary data snapshots used to estimate the(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)
— We address the problem of detecting slow moving targets from a moving radar system using Space-Time Adaptive Processing (STAP) techniques. Optimum interference rejection is known to require the estimation and the subsequent inversion of an interference-plus-noise covariance matrix. To reduce the number of training samples involved in the estimation and(More)