Publications
Volume 10, Number 2
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The Development of Superresolution at Lincoln Laboratory This special issue focuses on a variety of superresolution-oriented adaptive-array processing efforts at Lincoln Laboratory. For more than two decades, the Laboratory has been responsible for applications in which the achievement of the performance benefits of superresolution was essential for truly effective system operation. During this period, adaptive-array processing has made the transition from a technological curiosity with limited apparent potential for practical importance to a significant technology that has brought about paradigm shifts in sensor design rules. This issue of the Lincoln Laboratory Journal includes examples of such efforts in the context of various application areas. Airborne Signal Intercept for Wide-Area Battlefield Surveillance This article discusses the wide-area monitoring of enemy battlefield communications by a standoff aircraft. The purpose of this activity is to detect enemy emitters, determine their directions, and, when possible, copy their signals. Difficulties arise, however, because in typical battlefield scenarios many simultaneous communication emitters use frequency channels in the low VHF band (30 to 88 MHz). At this frequency band, the conventional antenna aperture available to the monitoring aircraft platform is only a few wavelengths long, leading to a broad receiving beamwidth and heavy cochannel interference. We discuss superresolution techniques that overcome the cochannel interference to improve the direction finding and copying of signals of interest. We also discuss improvements that can be obtained by knowing about the classes of signals being transmitted or by enhancing the antenna-array calibration of the airborne antenna. These techniques can be used to upgrade current signal intercept systems. Utilizing Waveform Features for Adaptive Beamforming and Direction Finding with Narrowband Signals Extensive research has been done on the use of antenna arrays for direction finding and beamforming; this research focuses on the detailed behavior of specific techniques rather than on actual signal processing applications. In most applications, there is a fundamental signal feature that provides essential leverage for an effective processing approach. This article, which is structured around such features, presents a comprehensive framework for selecting an appropriate adaptive approach for processing cochannel narrowband signals. We address the roles of antenna calibration and prior waveform knowledge, and give examples of effective, practical direction-finding and beamforming procedures that cover a wide range of potential applications. Superresolution Source Location with Planar Arrays The challenge of precision source location with a radio-frequency antenna array has existed from the beginnings of radiometry and has continued in modern applications with planar antenna arrays. Early work in this field was limited to estimating single source directions in one dimension with systems like crossed-loop radiometers. Currently, more advanced systems attempt to estimate azimuth and elevation by using two-dimensional arrays. Monopulse techniques have been extended to two-dimensional arrays to provide a computationally efficient method for estimating the azimuth and elevation of a single source from a planar array, but all monopulse techniques fail if there is appreciable interference close to the source. In this situation, adaptive array (superresolution) processing techniques are needed for direction estimation. This article discusses the results of a study on the proper way to design an adaptive planar array with a constrained antenna aperture. We consider the segmentation of the antenna aperture, the polarization of the antenna segments, and the algorithms used to process the signals received from the antenna. In particular, we concentrate on interference that is within one Rayleigh beamwidth of the source. The interference can be highly localized in space, as in a single direct-path interferer, or diffuse in space (possibly due to multipath). We present results of tests conducted with a segmented antenna array, along with simulations and analytical bounds, that guide us in designing a source-location system. High-Definition Vector Imaging High-definition vector imaging (HDVI) is a data-adaptive approach to synthetic-aperture radar (SAR) image reconstruction based on superresolution techniques originally developed for passive sensor arrays. The goals are to produce more informative, higher-resolution imagery for improving target recognition with UHF and millimeter-wave SAR and to aid the image analyst in identifying targets in radar imagery. Algorithms presented here include two-dimensional minimum-variance techniques based on the maximum-likelihood method (Capon) algorithm and a two-dimensional version of the MUSIC algorithm. We use simulations to compare processing techniques, and we present results of wideband Rail SAR measurements of reflectors in foliage, demonstrating resolution improvement and clutter rejection. Results with airborne millimeter-wave SAR data demonstrate improved resolution and speckle reduction. We also discuss the vector aspect of HDVI, i.e., the incorporation of non-pointlike scattering models to enable feature detection. An example of a vector image is presented for data from an airborne UHF radar, using the broadside flash model to reveal greater information in the data. Enhanced Imagery Using Spectral-Estimation-Based Techniques Since the early 1970s, Lincoln Laboratory has been working on algorithms to enhance the resolution of imagery from wideband radars. This article describes a class of algorithms that are based on the technique of bandwidth extrapolation, which uses a model-based spectral-estimation technique for generating synthetic radar data. The extrapolated radar data are combined with the measured radar data in a Fourier transform to produce images with high spectral resolution. The article describes the application of these algorithms to measured radar data from a small commercial aircraft in flight. The Automatic Target-Recognition System in SAIP Lincoln Laboratory has developed a new automatic target recognition (ATR) system that provides significantly improved target-recognition performance compared with ATR systems that use conventional synthetic-aperture radar (SAR) image-processing techniques. We achieve significant improvement in target-recognition performance by using a new superresolution image-processing technique that enhances SAR image resolution and image quality prior to performing target recognition. A computationally efficient two-stage template-based classifier is used to perform the target-recognition function. This article quantifies the improvement in target-recognition performance achieved by using superresolution image processing in the new ATR system. Ultra-Wideband Coherent Processing Lincoln Laboratory has developed an approach for estimating the ultra-wideband radar signature of a target by using sparse-subband measurements. First, we determine the parameters of an appropriate signal model that best fits the measured data. Next, the fitted signal model is used to interpolate between and extrapolate outside the measurement subbands. Standard pulse-compression methods are then applied to provide superresolved range profiles of the target. A superresolution algorithm automatically compensates for lack of mutual coherence between the radar subbands, providing the potential for ultra-wideband processing of real-world radar data collected by separate wideband radars. Because the processing preserves the phase distribution across the measured and estimated subbands, extended coherent processing can be applied to the ultra-wideband compressed radar pulses to generate superresolved radar images of the target. Applications of this approach to static test range and field data show promising results.
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