Fabian D. Lapierre

New solutions to the problem of range dependence in bistatic STAP radars

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 range-dependent. As a result, straight averaging results in poor performance. After reviewing existing methods exploiting the precise shape of the bistatic direction-Doppler curves.

Framework and Taxonomy for Radar Space-Time Adaptive Processing (STAP) Methods

The goal of radar space-time adaptive processing (STAP) is to detect slow moving targets from a moving platform, typically airborne or spaceborne. STAP generally requires the estimation and the inversion of an interference-plus-noise (I+N) covariance matrix. To reduce both the number of samples involved in the estimation and the computational cost inherent to the matrix inversion, many suboptimum STAP methods have been proposed. We propose a new canonical framework that encompasses all suboptimum STAP methods we are aware of. The framework allows for both covariance-matrix (CM) estimation and range-dependence compensation (RDC); it also applies to monostatic and bistatic configurations. Finally, we discuss a taxonomy for classifying the methods described by the framework.

Computationally-efficient range-dependence compensation method for bistatic radar STAP

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, snapshot statistics are range dependent. Straight averaging results thus in poor performance. In an earlier paper, we proposed a range-dependence compensation method that provides optimum performance. However, this implies a large computational cost. In this paper, we adapt the method to provide optimum performance at low computational cost.

Geometry-Induced Range-Dependence Compensation for Bistatic STAP with Conformal Arrays

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 optimum weight vector are not identically distributed with respect to range, thus preventing the STAP processor from achieving its optimum performance. The compensation of the range-dependence (RD) requires the knowledge of the locus of the clutter signature. We use a new RANSAC-based method for estimating this locus or, equivalently, the flight configuration parameters. Based on this knowledge, we perform an RD compensation of the snapshots to obtain an accurate estimate of the clutter covariance matrix. End-to-end performance analysis in terms of signal-to-inference-plus-noise ratio loss shows that our method yields promising performance.

An evaluation of pixel-based methods for the detection of floating objects on the sea surface

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 infrared (IR) is challenging because a rough sea is seen as a dynamic background of moving objects with size order, shape, and temperature similar to those of the floating mine. In this paper we have applied a selection of background subtraction algorithms to the problem, and we show that the recent algorithms such as ViBe and behaviour subtraction, which take into account spatial and temporal correlations within the dynamic scene, significantly outperform the more conventional parametric techniques, with only little prior assumptions about the physical properties of the scene.

New methods for handling the range dependence of the clutter spectrum in non-sidelooking monostatic STAP radars

We address the problem of detecting slow-moving targets using a non-sidelooking 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 generally results in poor performance. We present two new methods that handle the range-dependence by exploiting the geometry of the direction-Doppler curves.

Registration-based compensation using sparse representation in conformal-array STAP

Space-time adaptive processing (STAP) is a well-known technique in detecting slow-moving targets in the clutter-spreading environment. When considering the STAP system with conformal radar array (CFA), the training data are range-dependent, which results in poor detection performance of traditional statistical-based algorithms. Current registration-based compensation (RBC) is implemented based on a sub-snapshot spectrum using temporal smoothing. In this case, the estimation accuracy of the configuration parameters and the clutter power distribution is limited. In this paper, the technique of sparse representation is introduced into the spectral estimation, and a new compensation method is proposed, namely RBC with sparse representation (SR-RBC). This method first establishes the relationship between the clutter covariance matrix (CCM) and the clutter spectral distribution. Based on this, it avoids the problem of lacking stationary training data and converts the CCM estimation into the solving of the underdetermined equation only with the test cell. Then sparse representation method, like iterative reweighted least square (IRLS) is used to guide the solution of the underdetermined equation towards the actual clutter distribution. Finally, the transform matrix is designed using the CCM estimation so that the processed training data behaves nearly stationary with the test cell. Because the configuration parameters and the clutter spectral response are obtained with full-snapshot using sparse representation, SR-RBC provides more accurate clutter spectral estimation, and the transformed training data are more stationary so that better signal-clutter-ratio (SCR) improvement is achieved.

Registration-based range-dependence compensation for bistatic STAP radars

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 in poor performance. In this paper, we propose a new compensation method based on registration of clutter ridges and designed to work on a single realization of the stochastic snapshot at each range. The method has been successfully tested on simulated, stochastic snapshots. An evaluation of performance is presented.

Fundamentals of spatial and Doppler frequencies in radar STAP

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 ldquogeometricalrdquo and statistical concepts, where we respectively emphasize the 4D direction-Doppler (DD) curve and the 4D power spectral density (PSD) that characterize the (clutter) space-time field. These descriptors, which are flight-configuration dependent, but antenna independent, are fundamental since they can be used to derive the key spectral properties of any antenna, essentially by rotations and projections. These descriptors are related in various ways, mostly because the DD curve is the support of the ridge of the clutter PSD. We also emphasize the surprising benefits of systematically considering the three spatial frequencies that are always present behind the scene, even for the customary linear antenna. A solid, simple, and elegant basis for thinking about STAP for arbitrary measurement configurations and antenna arrays is provided.

Flight Simulator with IR and MMW Radar Image Generation Capabilities

In the future, modern airliners will use enhanced-synthesic vision systems (ESVS) to improve aeronautical operations in bad weather conditions. Before ESVS are effectively found aboard airliners, one must develop a multisensor flight simulator capable of synthetizing, in real time, images corresponding to a variety of imaging modalities. We present a real-time simulator called ARIS (Airborne Radar and Infrared Simulator) which is capable of generating two such imaging modalities: a forward-looking infrared (FLIR) and a millimeter-wave radar (MMWR) imaging system. The proposed simulator is modular sothat additional imaging modalities can be added. Example of images generated by the simulator are shown.