Gregory A. Showman

An approach to knowledge-aided covariance estimation

This paper introduces a parametric covariance estimation scheme for use with space-time adaptive processing (STAP) methods operating in heterogeneous clutter environments. The approach blends both a priori knowledge and data observations within a parameterized model to capture instantaneous characteristics of the cell under test (CUT) and reduce covariance errors leading to detection performance loss. We justify this method using both measured and synthetic data. Performance potential for the specific operating conditions examined herein include: 1) averaged behavior within roughly 2 dB of the optimal filter, 2) 1 dB improvement in exceedance characteristic relative to the optimal filter, highlighting improved instantaneous capability, and 3) impervious ness to corruptive target-like signals in the secondary data (no additional signal-to-interference-plus-noise ratio (SINK) loss, compared with 10 dB or greater loss for the standard STAP implementation), with corresponding detections comparable to the optimal filter case

Coherent MIMO radar: The phased array and orthogonal waveforms

Coherent multiple-input multiple-output (MIMO) radar is a natural extension of the phased array antenna that has been used by radar systems for decades. This tutorial unifies concepts from the literature and provides a framework for the analysis of an arbitrary suite of MIMO radar waveforms. A number of gain patterns are introduced, which quantify the antenna performance of a MIMO radar, and the impact of the waveform characteristics (e.g., range sidelobes) is discussed.

A knowledge-aided GMTI detection architecture [radar signal processing]

Space-time adaptive processing (STAP) plays an important role in ground moving target indication (GMTI). Heterogeneous clutter environments prevent STAP from achieving its theoretical performance bounds. The incorporation of a priori knowledge into the signal processing architecture holds the potential to greatly enhance detection performance by mitigating heterogeneous clutter effects. In this paper we propose one possible knowledge-aided STAP approach comprised of the following elements: a knowledge-aided prediction/estimation filter, a discrete matched filter, and a partially adaptive STAP applied to the clutter residual, assisted by knowledge-aided training. We focus our discussion on justifying the aforementioned elements and independently characterizing their performance potential. Using both measured and simulated data, we find the potential for substantial performance improvement.

Performance evaluation of two polarimetric STAP architectures

Space-time adaptive processing (STAP) is a powerful technique for detecting slowly moving targets in strong clutter. However, STAP performance is limited when used on radars with small apertures. Past research suggests polarizations potential to effectively discriminate between targets and clutter, thereby improving detection performance. We evaluate two methods of incorporating polarization into STAP, modeled after the polarimetric matched filter (PMF) and the polarimetric whitening filter (PWF). Our analysis serves to unify various proposed approaches to combining STAP and polarization, and benchmarks the benefits of polarization to GMTI detection. Using measured clutter and target characteristics from published sources as a baseline, we found that the PMF technique offers up to 6-dB improvement over STAP. This significant advantage is realized in the center of the clutter spectrum, and the impact on minimum detectable velocity (MDV) is modest. However, the PWF offers increased performance over the entire Doppler spectrum, and can be easily implemented as a STAP post-processor.

Correction of artifacts in turntable inverse synthetic aperture radar images

Inverse synthetic aperture radar (ISAR) imaging on a turntable-tower test range permits convenient generation of high resolution 2- and 3-D images of radar targets under controlled conditions, typically for characterization of the radar cross section of targets or for testing SAR image processing and automatic target recognition algorithms. However, turntable ISAR images suffer geometric distortions and zero-Doppler clutter (ZDC) artifacts not found in airborne SAR images. In this paper, ISAR images formed at Georgia Techs Electromagnetic Test Facility are used to demonstrate and compare selected members of one family of 2- D ISAR imaging algorithms, from a simple but distortion- prone 2D discrete Fourier transform to a computationally- intensive matched filter solution. A simple algorithm for correcting range curvature using image domain resampling is described. We then demonstrate two signal processing techniques to suppress zero-Doppler clutter while minimizing effects on the target signature. The first removes ZDC components in the frequency domain, whereas the second performs cancellation in the image domain.

Stap performance in site-specific clutter environments

Space-time adaptive processing (STAP) assumes the availability of an ample supply of independent and identically distributed secondary data from a homogeneous clutter environment to estimate the covariance matrix, but typical airborne sensor systems operate under heterogeneous, site-specific clutter circumstances. Sitespecific clutter exhibits several types of heterogeneity, including varying amplitude and spectral characteristics as a function of azimuth and range, which lead to degradation of the STAP filter. Utilizing site-specific clutter prediction based on cultural databases, the effects of different types of clutter heterogeneity can be isolated to help determine the most important parameters for STAP design. Characterization of the effect of heterogeneous clutter on the STAP, and fusion of a priori operating environment knowledge into a knowledge-aided and expert reasoning filter design philosophy, offer potentially significant improvements in target detection and data screening capabilities. Several scenarios are presented to demonstrate the effects of site-specific clutter heterogeneity on the performance of STAP algorithms. Examples of intelligent screening of secondary data, showing 10 to 20 dB improvement in signal-to-interference plus noise ratio (SINR) loss in measured data, and synthetic aperture radar based pre-filtering of discretes are also discussed. This research was supported under US Air Force Contract Number F30602-02-C-00 1 1. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the U.S. Government. This paper presented at the 2003 IEEE Aerospace Conference, Big Sky, MT, USA. March 8-15.2003. 0-7803-7651-X/03/

Application of the Cramer-Rao lower bound for bearing estimation to STAP performance studies

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Filters for polarimetric calibration of UWB SAR imagery

The performance of space-time adaptive processing (STAP) radar is a strong function of array geometry and the particular algorithm implementation. Traditionally, detection performance has been of paramount importance, but recently interest has grown in the accuracy of target direction of arrival (DOA) estimates. This paper describes an evaluation of the Cramer-Rao lower bound (CRLB) for DOA accuracy. The CRLB is useful for bounding the bearing estimation performance of candidate array architectures and STAP algorithms, but often generates counter-intuitive results. Anomalous characteristics of the CRLB are investigated, explained, and placed in the context of maximum likelihood estimation (MLE) properties. The end product is a tool that can be applied to comparative analyses with confidence. The paper concludes by demonstrating the utility of the CRLB in both array and algorithm studies.

Clutter-based array calibration for enhanced geolocation accuracy

Polarimetric synthetic aperture radar (SAR) imagery is susceptible to degradation due to non-ideal responses in antennas and other hardware. Image corruption is especially serious in SAR operating over ultra-wide bandwidths (UWB) and imaging over wide angles, as the system response becomes a complicated function of angle and frequency. The usual approach to removing the system response from measured UWB SAR images involves operations in the two-dimensional spatial frequency domain of the imagery. We show that correction can also be performed by convolving the polarimetric images with two-dimensional polarimetric calibration filters (PCF). When compared to the frequency domain procedure, filtering offers improved calibration on extended-area targets, lower sidelobe noise, and computational savings. PCF can be synthesized from knowledge of the system response, or by an adaptive equalization technique that uses clutter returns to generate filters to correct for leakage, or crosstalk, between channels.

Radar detection and angle estimation of over-resolved ground vehicles

Accurate target geolocation is critical to ground moving target indication (GMTI) performance. Large geolocation errors can make interpreting GMTI detection results difficult, particularly in dense target environments. Geolocation errors can result from a combination of antenna channel errors and platform inertial navigation system (INS) biases. In this paper we develop clutter-based array calibration techniques that remove unknown, channel-to-channel, phase and amplitude errors. These clutter-based techniques utilize distributed clutter for calibration and require no a priori knowledge of the scene or calibration targets. We also apply these techniques to estimate the platform orientation, which significantly improves cross-range geolocation accuracy as compared to platform INS orientation estimates. We present results on measured X-band data.