Philippe Ries

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.

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.

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.

RANSAC-based Flight Parameter Estimation for Registration-based Range-dependence Compensation in Airborne Bistatic STAP Radar with Conformal Antenna Arrays

We consider space-time adaptive processing (STAP) in a bistatic radar configuration and when the radar returns are recorded by 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 of the secondary data requires the knowledge of the clutter power spectrum locus. This paper proposes a new RANSAC-based method for estimating, in the case of CAAs operating in arbitrary bistatic configurations, the clutter power spectrum locus or, equivalently, the flight configuration parameters. Based on the knowledge of this locus, we can perform a range-dependence 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 the method yields promising performance

Knowledge-aided array calibration for registration-based range-dependence compensation in airborne STAP radar with Conformal Antenna Arrays

We consider space-time adaptive processing (STAP) when the radar returns are recorded by 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 customary STAP processor from achieving its optimum performance. The compensation of the range-dependence of the secondary data requires the precise knowledge of the space-time steering vector. We propose a new knowledge-aided method based on the eigenstructure of the space-time covariance matrix for calibrating the gain and phase of each sensor in the CAA. Based on the calibrated space-time steering vectors, we can perform an accurate range-dependence compensation to obtain a valid estimate of the covariance matrix. End-to-end performance analysis in terms of signal to inference-plus-noise ratio loss shows that the method yields promising performance.

Range-dependence compensation in airborne bistatic STAP radar for partially-calibrated conformal antenna arrays

We consider space-time adaptive processing (STAP) when the radar returns are recorded by 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 customary STAP processor from achieving its optimum performance. The compensation of the range dependence of the secondary data requires precise knowledge of the array response for any direction of arrival (DOA), and, thus, the spatial steering vector (SV). We propose a novel registration-based range-dependence compensation algorithm that gives an accurate estimate of the interference-plus-noise covariance matrix under the hypotheses that calibrated spatial SVs are available only for a small set of DOAs, and that the errors in the model available for the array response are DOA dependent. The performance in terms of signal-to-inference-plus-noise ratio loss is promizing.

Knowledge-aided Heterogeneity-compensation Algorithm for STAP Applicable to Bistatic Configurations and Conformal Antenna Arrays

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 the application of STAP in a bistatic radar configuration when the radar returns are recorded by a conformal antenna array (CAA). The secondary data snapshots used to estimate the optimum weight vector are typically heterogeneous, i.e., not identically distributed with respect to range, thus preventing the STAP processor from achieving its optimum performance. We present a novel knowledge- aided (KA), registration-based pre-processor that mitigates the heterogeneity of the secondary data. When applied to simulated data for a bowl-shaped antenna, this pre-processor is shown to provide enhanced performance when used in conjunction either with the standard sample matrix inversion (SMI) algorithm or with the more computationally- and data-efficient joint domain localized (JDL) algorithm.

High-resolution clutter-power estimation for range-dependence compensation in conformal-array STAP

We consider space-time adaptive processing (STAP) when the radar returns are recorded by 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 requires the precise knowledge of the clutter power contained in the secondary data. We propose anew MVDR-based clutter power estimator applied to temporally-smoothed secondary data to obtain an accurate estimate of the expected clutter power This estimate allows one to perform an accurate range-dependence compensation leading to a valid estimate of the clutter covariance matrix. End-to-end performance analysis in terms of signal to inference plus-noise ratio loss shows promizing performance.