A single Synthetic Aperture Radar (SAR) image is a 2-Dimensional projection of a 3-Dimensional scene, with very limited ability to estimate surface topography. However, with multiple SAR images collected from suitably different geometries, they may be compared with multilateration calculations to estimate characteristics of the missing dimension. The ability to employ effective multilateration algorithms is highly dependent on the geometry of the data collections, and can be cast as a least-squares exercise. A measure of Dilution of Precision (DOP) can be used to compare the relative merits of various collection geometries.
Synthetic Aperture Radar (SAR) projects a 3-D scene’s reflectivity into a 2-D image. In doing so, it generally focuses 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. HOF is very radar flightpath dependent. Some flightpaths like straight and level flightpaths will have very large HOF limits. Other flightpaths, especially those that exhibit large out-of-plane motion will have very small HOF limits, perhaps even small fractions of a meter. This paper explores the impact of various flightpaths on HOF, and discusses the conditions for increasing or decreasing HOF. We note also that HOF might be exploited for target height estimation and offer insight to other height estimation techniques.
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.
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.
KEYWORDS: Polarization, Radar, Antennas, Signal attenuation, Signal processing, Signal to noise ratio, Interference (communication), Backscatter, Transmitters, Signal detection
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.
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.
KEYWORDS: Doppler effect, Target detection, Analog electronics, Digital signal processing, Demodulation, Radar, Image processing, Signal processing, Apodization, Phase modulation, Modulation
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.
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.
KEYWORDS: Antennas, Radar, Signal to noise ratio, Beam shaping, Commercial off the shelf technology, Numerical integration, Detection and tracking algorithms, Signal processing, Radar sensor technology, Current controlled current source
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.
The complex coherence function describes information that is necessary to create maps from interferometric synthetic aperture radar (InSAR). This coherence function is complicated by building layover. This paper presents a mathematical model for this complex coherence in the presence of building layover and shows how it can describe intriguing phenomena observed in real interferometric SAR data.
In recent years, a new class of Moving Target Indicator (MTI) radars has emerged, namely those whose mission included detecting moving people, or “dismounts.” This new mode is frequently termed Dismount-MTI, or DMTI. Obviously, detecting people is a harder problem than detecting moving vehicles, necessitating different specifications for performance and hardware quality. Herein we discuss some performance requirements typical of successful DMTI radar modes and systems.
Monitoring seasonal snow accumulation is important for evaluation of snow models, for short- and long-term snow cover monitoring, and for both military and civilian activities in cold climates. Improved spatial analysis of snow depth and volume can help decision makers plan for future events and mitigate risk. Current snow depth measurement methods fall short of operational requirements. This research explored a new approach for determining snow depth using Ku-band multi-pass (monostatic) airborne interferometric synthetic aperture radar (InSAR). A perturbation method that isolated and compared high frequency terrain phase to elevation was used to generate Snow-Off and Snow-On DEMs from the InSAR phase data. Differencing the InSAR DEMs determined elevation change caused by accumulated snow. Comparison of InSAR snow depths to manual snow depth measurements indicated average InSAR snow depth errors of -8cm, 95cm, -49cm, 176cm, 87cm, and 42cm for six SAR pairs. The source of these errors appears to be mostly related to uncorrected slope and tilt in fitted low frequency planes. Results show that this technique has promise but accuracy could be substantially improved by the use of bistatic SAR systems, which would allow for more stable and measurable interferometric baselines.
An occluded or dark region in synthetic aperture radar (SAR) imagery, known as a shadow, is created when incident radar energy is obstructed by a target with height from illuminating resolution cells immediately behind the target in the ground plane. Shadows depend on the physical dimensions and mobility of a target, platform and radar imaging parameters, and scene clutter. Target shadow dimensions and intensity can be important radar observables in SAR imagery for target detection, location, and tracking or even identification. Stationary target shadows can provide insight as to the physical dimensions of a target, while moving target shadows may show more accurately the location and motion of the target over time versus Doppler energy which may be shifted or smeared outside the scene. However, SAR shadows prove difficult to capture as a target or platform moves, since the quality of the no-return area may quickly be washed-out in a scene over many clutter resolution cells during an aperture. Prior work in the literature has been limited to describing partial shadow degradation effects from platform or target motion of vehicles such as static target shadow tip or interior degradation during an aperture, or shadow degradation due to target motion solely in cross-range. In this paper, we provide a more general formulation of SAR shadow dimensions and intensity for non-specific targets with an arbitrary motion.
Interferometric Synthetic Aperture Radar (IFSAR or InSAR) uses multiple antenna phase centers to ultimately measure
target scene elevation. Its ability to do so depends on the antenna configuration, and how the multiple phase centers are
employed. We examine several different dual-phase-center antenna configurations and modalities, including a
conventional arrangement where a dedicated antenna is used to transmit and receive with another to receive only, a
configuration where transmit and receive operations are ping-ponged between phase centers, a monopulse configuration,
and an orthogonal waveform configuration. Our figure of merit is the RMS height noise in the elevation estimation.
We show that a monopulse configuration is equivalent to the ping-pong scheme, and both offer an advantage over the
conventional arrangement. The orthogonal waveform offers the best potential performance, if sufficient isolation can be
achieved.
Knowing the statistical characteristics of the radar cross-section (RCS) of man-made, or cultural clutter, is crucial to the
success of clutter mitigation, radar target detection algorithms, and radar system requirements in urban environments.
Open literature studies regarding the statistical nature of cultural clutter focus primarily on radar probability models or
limited experimental data analysis of specific locations and frequencies. This paper seeks to expand the existing body of
work on cultural clutter RCS statistics at Ku-band for ground moving target indication (GMTI) and synthetic aperture
radar (SAR) applications. We examine the normalized RCS probability distributions of cultural clutter in several urban
scenes, across aspect and elevation angle, for vertical transmit/receive (VV) polarizations, and at diverse resolutions,
using experimental data collected at Ku-band. We further describe frequency and RCS strength statistics of clutter
discretes per unit area to understand system demands on radars operating in urban environments in this band.
The clutter locus is an important concept in space-time adaptive processing (STAP) for ground moving target
indicator (GMTI) radar systems. The clutter locus defines the expected ground clutter location in the angle-Doppler
domain. Typically in literature, the clutter locus is presented as a line, or even a set of ellipsoids, under certain
assumptions about the geometry of the array. Most often, the array is assumed to be in the horizontal plane
containing the velocity vector. This paper will give a more general 3-dimensional interpretation of the clutter locus
for a general linear array orientation.
Knowing the statistical characteristics of a target's radar cross-section (RCS) is crucial to the success of radar target
detection algorithms. Open literature studies regarding the statistical nature of the RCS of ground vehicles focus
primarily on simulations, scale model chamber measurements, or limited experimental data analysis of specific vehicles
at certain frequencies. This paper seeks to expand the existing body of work on ground vehicle RCS statistics at Ku-band
for ground moving target indication (GMTI) applications. We examine the RCS probability distributions of civilian and
military vehicles, across aspect and elevation angle, for HH and VV polarizations, and at diverse resolutions, using
experimental data collected at Ku-band. We further fit Swerling target models to the distributions and suggest
appropriate detection thresholds for ground vehicles in this band.
Knowing the statistical characteristics of a target's radar cross-section (RCS) is crucial to the success of radar target
detection algorithms. A wide range of applications currently exist for dismount (i.e. human body) detection and
monitoring using ground-moving target indication (GMTI) radar systems. Dismounts are particularly challenging to
detect. Their RCS is orders of magnitude lower than traditional GMTI targets, such as vehicles. Their velocity of about 0
to 1.5 m/s is also much slower than vehicular targets. Studies regarding the statistical nature of the RCS of dismounts
focus primarily on simulations or very limited empirical data at specific frequencies. This paper seeks to enhance the
existing body of work on dismount RCS statistics at Ku-band, which is currently lacking, and has become an important
band for such remote sensing applications. We examine the RCS probability distributions of different sized humans in
various stances, across aspect and elevation angle, for horizontal (HH) and vertical (VV) transmit/receive polarizations,
and at diverse resolutions, using experimental data collected at Ku-band. We further fit Swerling target models to the
RCS distributions and suggest appropriate detection thresholds for dismounts in this band.
The Rapid Terrain Visualization interferometric synthetic aperture radar was designed and built at Sandia National
Laboratories as part of an Advanced Concept Technology Demonstration (ACTD) to "demonstrate the technologies and
infrastructure to meet the Army requirement for rapid generation of digital topographic data to support emerging crisis or
contingencies." This sensor was built by Sandia National Laboratories for the Joint Programs Sustainment and
Development (JPSD) Project Office to provide highly accurate digital elevation models (DEMs) for military and civilian
customers, both inside and outside of the United States. The sensor achieved better than HRTe Level IV position
accuracy in near real-time. The system was flown on a deHavilland DHC-7 Army aircraft.
This paper presents a collection of images and data products from the Rapid Terrain Visualization interferometric
synthetic aperture radar. The imagery includes orthorectified images and DEMs from the RTV interferometric SAR
radar.
In January, 2006, the New York Air National Guard requested that Sandia National Laboratories develop an X-band synthetic aperture radar to use for an experiment to detect crevasses in Antarctica. Sandia provided a MiniSAR radar that was modified to operate at X-band. Data was collected with this system in the Antarctic summer of 2006. The results from this data collection are presented in this paper.
The Rapid Terrain Visualization interferometric synthetic aperture radar was designed and built at Sandia National Laboratories as part of an Advanced Concept Technology Demonstration (ACTD) to “demonstrate the technologies and infrastructure to meet the Army requirement for rapid generation of digital topographic data to support emerging crisis or contingencies.” This sensor is currently being operated by Sandia National Laboratories for the Joint Precision Strike Demonstration (JPSD) Project Office to provide highly accurate digital elevation models (DEMs) for military and civilian customers, both inside and outside of the United States. The sensor achieves better than DTED Level IV position accuracy in near real-time. The system is being flown on a deHavilland DHC-7 Army aircraft. This paper outlines some of the technologies used in the design of the system, discusses the performance, and will discuss operational issues. In addition, we will show results from recent flight tests, including high accuracy maps taken of the San Diego area.
Data collection for interferometric synthetic aperture radar (IFSAR) mapping systems currently utilize two operation modes. A single-antenna, dual-pass IFSAR operation mode is the first mode in which a platform carrying a single antenna traverses a flight path by the scene of interest twice collecting data. A dual-antenna, single-pass IFSAR operation mode is the second mode where a platform possessing two antennas flies past the scene of interest collecting data. There are advantages and disadvantages associated with both of these data collection modes. The single-antenna, dual-pass IFSAR operation mode possesses an imprecise knowledge of the antenna baseline length but allows for large antenna baseline lengths. This imprecise antenna baseline length knowledge lends itself to inaccurate target height scaling. The dual-antenna, one-pass IFSAR operation mode allows for a precise knowledge of the limited antenna baseline length but this limited baseline length leads to increased target height noise. This paper presents a new, innovative dual-antenna, dual-pass IFSAR operation mode which overcomes the disadvantages of the two current IFSAR operation modes. Improved target height information is now obtained with this new mode by accurately estimating the antenna baseline length between the dual flight passes using the data itself. Consequently, this new IFSAR operation mode possesses the target height scaling accuracies of the dual-antenna, one-pass operation mode and the height-noise performance of the one-antenna, dual-pass operation mode.
Interferometric SAR (IFSAR) can be shown to be a special case of 3-D SAR image formation. In fact, traditional IFSAR processing results in the equivalent of merely a super- resolved, under-sampled, 3-D SAR image. However, when approached as a 3-D SAR problem, a number of IFSAR properties and anomalies are easily explained. For example, IFSAR decorrelation with height is merely ordinary migration in 3-D SAR. Consequently, treating IFSAR as a 3-D SAR problem allows insight and development of proper motion compensation techniques and image formation operations to facilitate optimal height estimation. Furthermore, multiple antenna phase centers and baselines are easily incorporated into this formulation, providing essentially a sparse array in the elevation dimension. This paper shows the Polar Format image formation algorithm extended to 3 dimensions, and then proceeds to apply it to the IFSAR collection geometry. This suggests a more optimal reordering of the traditional IFSAR processing steps.
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