Synthetic Aperture Radar (SAR) creates imagery of the earth?s surface from airborne or spaceborne radar platforms. However, the nature of any radar is to geolocate its echo data, i.e., SAR images, relative to its own measured radar location. Acceptable accuracy and precision of such geolocation can be quite di fficult to achieve, and is limite d by any number of parameters. However, databases of geolocated earth imagery do exist, often using other imaging modalities, with Google Earth being one such example. Thes e can often be much more accurate than what might be achievable by the radar itself. Cons equently, SAR images may be aligned to some higher accuracy database, there by improving the geolocation of features in the SAR image. Examples offer anecdotal evidence of the viability of such an approach. - 4 - Acknowledgements This report is the result of an unf unded Research and Development effort. A special thank you to Tommy Burks for his da ta collections in the Albuquerque area.
This document contains the final report for the midyear LDRD titled "Extension of Interferometric Synthetic Aperture Radar to Multiple Phase-Centers." This report presents an overview of several methods for approaching the two-target in layover problem that exists in interferometric synthetic aperture radar systems. Simulation results for one of the methods are presented. In addition, a new direct approach is introduced.
Radar is by its basic nature a ranging instrument. If radar range and range-rate measurements from multiple directions can be made and assembled, then multilateration allows locating a feature common to the set of Synthetic Aperture Radar (SAR) images to an accurate 3-D coordinate. The ability to employ effective multilateration algorithms is highly dependent on the geometry of the data collections, and the accuracy with which relative range measurements can be made. The problem can be cast as a least-squares exercise, and the concept of Dilution of Precision can describe the accuracy and precision with which a 3-D location can be made.
Radar is by its basic nature a ranging instrument. If radar range measurements from multiple directions can be made and assembled, then multilateration allows locating a feature common to the set of Synthetic Aperture Radar (SAR) images to an accurate 3-D coordinate. The ability to employ effective multilateration algorithms is highly dependent on the geometry of the data collections, and the accuracy with which relative range measurements can be made. The problem can be cast as a least-squares exercise, and the concept of Dilution of Precision can describe the accuracy and precision with which a 3-D location can be made.
Often a crucial exploitation of a Synthetic Aperture Radar (SAR) image requires accurate and precise knowledge of its geolocation, or at least the geolocation of a feature of interest in the image. However, SAR, like all radar modes of operation, makes its measurements relative to its own location or position. Consequently, it is crucial to understand how the radar's own position and motion impacts the ability to geolocate a feature in the SAR image. Furthermore, accuracy and precision of navigation aids like GPS directly impact the goodness of the geolocation solution.
Synthetic Aperture Radar (SAR) projects a 3-D scene’s reflectivity into a 2-D image. In doing so, it generally focusses the image to a surface, usually a ground plane. Consequently, scatterers above or below the focal/ground plane typically exhibit some degree of distortion manifesting as a geometric distortion and misfocusing or smearing. Limits to acceptable misfocusing define a Height of Focus (HOF), analogous to Depth of Field in optical systems. This may be exacerbated by the radar’s flightpath during the synthetic aperture data collection. It might also be exploited for target height estimation and offer insight to other height estimation techniques.
A stripmap Synthetic Aperture Radar (SAR) image is a long SAR image along some centerline, and formed from multiple synthetic apertures. At issue is that the centerline in the image actually corresponds to an arc on a round earth, and multiple strategies exist for fitting the image centerline to the round earth. Some of those strategies involve Rhumb lines, great circle paths, and great ellipse paths. Some are better than others in polar regions. Notions of parallel flight paths for the radar during data collection also require careful consideration of the geometry of a round earth.
High-performance spotlight Synthetic Aperture Radar (SAR) requires measurement of the radars motion during the synthetic aperture. A convenient coordinate frame for motion measurement is often not the convenient coordinate frame for motion compensation during the SAR data generation and image formation processing. A convenient frame for radar motion measurement is the Earth-Centered Earth-Fixed (ECEF) coordinate frame, whereas spotlight SAR processing typically require s polar coordinates from a selected Scene Reference Point (SRP). This report presents the conversion from ECEF coordinates to appropriate parameters for SAR processing.
Traditional dual-channel phase-monopulse and amplitude-monopulse antenna systems might electrically steer their difference-channel nulls by suitably adjusting characteristics of their constituent beams or lobes. A phase-monopulse systems' null might be steered by applying suitable relative phase shifts. An amplitude-monopulse systems' null might be steered by applying a suitable relative beam amplitude scaling. The steering of the null might be employed by a continuously mechanically-scanning antenna to stabilize the null direction over a series of radar pulses.
An important part of a navigation system for a moving platform is the estimation of the rate of travel. This document presents a method for estimating the platform velocity in 3-dimensions using multiple antenna subarrays which could be used to augment navigation in a GPS-degraded environment. An advantage of this technique is that it does not require any knowledge of a positions of any landmarks. Results from radar data collected by the Sandia National Laboratories demonstration radar system are presented to illustrate the promise of this technique.
In comparing system performance for ground moving target indicator (GMTI) radar systems, various metrics are used. It is highly desirable that the metric be simple and powerful. Ideally it is a single number, or a plot. It is often the case that a single number is not sufficient to describe the radar performance under all operational conditions. In spite of this, it is still common to attempt to use a simple metric, such as the minimum detectable velocity (MDV). This paper discusses the concept of minimum detectable velocity with the goal of showing what this metric attempts to communicate, and what may not be properly communicated by this metric without careful attention. Basic parameters that affect the minimum detectable velocity are presented.
Various parameters can affect the ground moving target indicator (GMTI) radar performance. One such parameter that may often be overlooked is that of the unintended polarization of the antenna, e.g., the cross-polarization response. This paper discusses the issue of cross-polarization on clutter attenuation performance for GMTI radars.
When radar receivers employ multiple channels, the general intent is for the receive channels to be as alike as possible, if not as ideal as possible. This is usually done via prudent hardware design, supplemented by system calibration. Towards this end, we require a quality metric for ascertaining the goodness of a radar channel, and the degree of match to sibling channels. We propose a relevant and usable metric to do just that. Acknowledgements: This report was the result of an unfunded research and development activity.
Radar receivers with multiple receive channels generally strive to make the receive channels as ideal as possible, and as alike as possible. This is done via prudent hardware design, and system calibration. Towards that end, we require a quality metric for ascertaining the goodness of a radar channel, and its match to sibling channels. We propose a relevant and useable metric to do just that.
Many types of dark regions occur naturally or artificially in Synthetic Aperture Radar (SAR) and Coherent Change Detection (CCD) products. Occluded regions in SAR imagery, known as shadows, are created when incident radar energy is obstructed by a target with height from illuminating resolution cells immediately behind the target in the ground plane. No return areas are also created from objects or terrain that produce little scattering in the direction of the receiver, such as still water or flat plates for monostatic systems. Depending on the size of the dark region, additive and multiplicative noise levels are commonly measured for SAR performance testing. However, techniques for radar performance testing of CCD using dark regions are not common in the literature. While dark regions in SAR imagery also produce dark regions in CCD products, additional dark regions in CCD may further arise from decorrelation of bright regions in SAR imagery due to clutter or terrain that has poor wide-sense stationarity (such as foliage in wind), man-made disturbances of the scene, or unintended artifacts introduced by the radar and image processing. By comparing dark regions in CCD imagery over multiple passes, one can identify unintended decorrelation introduced by poor radar performance rather than phenomenology. This paper addresses select dark region automated measurement techniques for the evaluation of radar performance during SAR and CCD field testing.
We desire a metric with which to evaluate the "goodness" of various image compression schemes in recreating an original Synthetic Aperture Radar image. Herein we propose a "coherence" measure that results in a single quality number for such an evaluation.
Spurious energy in received radar data is unanticipated and undesired signal relevant to radar target signatures, usually a consequence of nonideal component and circuit behavior, perhaps due to I/Q imbalance, nonlinear component behavior, additive interference (e.g. cross-talk, etc.), or other sources. The manifestation of the spurious energy in a range-Doppler map or image can often be influenced by appropriate pulse-to-pulse phase modulation. Comparing multiple images having been processed with the same data but different signal paths and modulations allows identifying undesired spurs and then cropping or apodizing them.
Antenna apertures are often parsed into subapertures for Direction of Arrival (DOA) measurements. However, when the overall aperture is tapered for sidelobe control, the locations of phase centers for the individual subapertures are shifted due to the local taper of individual subapertures. Furthermore, individual subaperture gains are also affected. These non-uniform perturbations complicate DOA calculations. Techniques are presented to calculate subaperture phase center locations, and algorithms are given for equalizing subapertures' gains.
This document discusses some interesting features of the new coherence estimator in [1] . The estimator is d erived from a slightly different viewpoint. We discuss a few properties of the estimator, including presenting the probability density function of the denominator of the new estimator , which is a new feature of this estimator . Finally, we present an appr oximate equation for analysis of the sensitivity of the estimator to the knowledge of the noise value. ACKNOWLEDGEMENTS The preparation of this report is the result of an unfunded research and development activity. Sandia National Laboratories is a multi - program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE - AC04 - 94AL85000.
Antenna apertures that are tapered for sidelobe control can also be parsed into subapertures for Direction of Arrival (DOA) measurements. However, the aperture tapering complicates phase center location for the subapertures, knowledge of which is critical for proper DOA calculation. In addition, tapering affects subaperture gains, making gain dependent on subaperture position. Techniques are presented to calculate subaperture phase center locations, and algorithms are given for equalizing subapertures’ gains. Sidelobe characteristics and mitigation are also discussed.
It is commonly observed that resolution plays a role in coherent change detection. Although this is the case, the relationship of the resolution in coherent change detection is not yet defined . In this document, we present an analytical method of evaluating this relationship using detection theory. Specifically we examine the effect of resolution on receiver operating characteristic curves for coherent change detection.