Along-track interferometry (ATI) has the ability to generate high-quality synthetic aperture radar (SAR) images and concurrently detect and estimate the positions of ground moving target indicators (GMTI) with moderate processing requirements. This paper focuses on several different ATI system configurations, with an emphasis on low-cost configurations employing no active electronic scanned array (AESA). The objective system has two transmit phase centers and four receive phase centers and supports agile adaptive radar behavior. The advantages of multistatic, multiple input multiple output (MIMO) ATI system configurations are explored. The two transmit phase centers can employ a ping-pong configuration to provide the multistatic behavior. For example, they can toggle between an up and down linear frequency modulated (LFM) waveform every other pulse. The four receive apertures are considered in simple linear spatial configurations. Simulated examples are examined to understand the trade space and verify the expected results. Finally, actual results are collected with the Space Dynamics Laboratorys (SDL) FlexSAR system in diverse configurations. The theory, as well as the simulated and actual SAR results, are presented and discussed.
We present an efficient and computationally simple approach for synthetic aperture radar (SAR) imaging in cases when the radar data have gaps, due to missing pulses and/or notches in the frequency band. Our method is a simple variation of gradient projection, in which the search path in each iteration is obtained by projecting the negative-gradient of the L1 norm onto a hyper-plane defining solutions which are consistent with the data. The computations are not complicated since the L1 gradient is simply equal to the sign() of the pixels in the image. Computational efficiency is obtained by incorporating the polar format algorithm, which accomplishes the projection operation using a fast Fourier transform. Sample results are presented using the AFRL Gotcha 2006 radar data set and the Space Dynamics Laboratory FlexSAR system.
Clutter suppression interferometry (CSI) has received extensive attention due to its multi-modal capability to detect slow-moving targets, and concurrently form high-resolution synthetic aperture radar (SAR) images from the same data. The ability to continuously augment SAR images with geo-located ground moving target indicators (GMTI) provides valuable real-time situational awareness that is important for many applications. CSI can be accomplished with minimal hardware and processing resources. This makes CSI a natural candidate for applications where size, weight and power (SWaP) are constrained, such as unmanned aerial vehicles (UAVs) and small satellites. This paper will discuss the theory for optimal CSI system configuration focusing on sparse time-varying transmit and receive array manifold due to SWaP considerations. The underlying signal model will be presented and discussed as well as the potential benefits that a sparse time-varying transmit receive manifold provides. The high-level processing objectives will be detailed and examined on simulated data. Then actual SAR data collected with the Space Dynamic Laboratory (SDL) FlexSAR radar system will be analyzed. The simulated data contrasted with actual SAR data helps illustrate the challenges and limitations found in practice vs. theory. A new novel approach incorporating sparse signal processing is discussed that has the potential to reduce false- alarm rates and improve detections.
Due to its computational efficiency, the polar format algorithm (PFA) is considered by many to be the workhorse for
airborne synthetic aperture radar (SAR) imaging. PFA is implemented in spatial Fourier space, also known as “K-space”,
which is a convenient domain for understanding SAR performance metrics, sampling requirements, etc. In this
paper the mathematics behind PFA are explained and computed examples are presented, both using simulated data, and
experimental airborne radar data from the Air Force Research Laboratory (AFRL) Gotcha Challenge collect. In
addition, a simple graphical method is described that can be used to model and predict wavefront curvature artifacts in
PFA imagery, which are due to the limited validity of the underlying far-field approximation. The appendix includes
Matlab code for computing SAR images using PFA.
In previous work, we presented GMTI detection and geo-location results from the AFRL Gotcha challenge data set, which was collected using a 3-channel, X-band, circular SAR system. These results were compared against GPS truth for a scripted vehicle target. The algorithm used for this analysis is known as ATI/DPCA, which is a hybrid of along-track interferometry (ATI) and the displaced phase center antenna (DPCA) technique. In the present paper the use of ATI/DPCA is extended in order to detect and geo-locate all observable moving targets in the Gotcha challenge data, including both the scripted movers and targets of opportunity. In addition, a computationally efficient SAR imaging technique is presented, appropriate for short integration times, which is used for computing an image of the scene of interest using the same pulses of data used for the GMTI processing. The GMTI detections are then overlaid on the SAR image to produce a simultaneous SAR/GMTI map.
In a previous SPIE paper we described several variations of along-track interferometry (ATI), which can be used for
moving target detection and geo-location in clutter. ATI produces a phase map in range/Doppler coordinates by
combining radar data from several receive channels separated fore-and-aft (along-track) on the sensor platform. In
principle, the radial velocity of a moving target can be estimated from the ATI phase of the pixels in the target signature
footprint. Once the radial velocity is known, the target azimuth follows directly. Unfortunately, the ATI phase is
wrapped, i.e., it repeats in the interval [-π, π], and therefore the mapping from ATI phase to target azimuth is non-unique.
In fact, depending on the radar system parameters, each detected target can map to several equally-likely azimuth values.
In the present paper we discuss a signal processing method for resolving the phase wrapping ambiguity, in which the
radar bandwidth is split into a high and low sub-band in software, and an ATI phase map is generated for each. By
subtracting these two phase maps we can generate a coarse, but unambiguous, radial velocity estimate. This coarse
estimate is then combined with the fine, but ambiguous estimate to pinpoint the target radial velocity, and therefore its
azimuth. Since the coarse estimate is quite sensitive to noise, a rudimentary tracker is used to help smooth out the phase
errors. The method is demonstrated on Gotcha 2006 Challenge data.
This paper describes a method for accurately geo-locating moving targets using three-channel SAR-based GMTI
interferometry. The main goals in GMTI processing are moving target detection and geo-location. In a 2011 SPIE
paper we showed that reliable target detection is possible using two-channel interferometry, even in the presence of
main-beam clutter. Unfortunately, accurate geo-location is problematic when using two-channel interferometry,
since azimuth estimation is corrupted by interfering clutter. However, we show here that by performing three-channel
processing in an appropriate sequence, clutter effects can be diminished and significant improvement
can be obtained in geo-location accuracy. The method described here is similar to an existing technique known
as Clutter Suppression Interferometry (CSI), although there are new aspects of our implementation. The main
contribution of this paper is the mathematical discussion, which explains in a straightforward manner why
three-channel CSI outperforms standard two-channel interferometry when target signatures are embedded in
main-beam clutter. Also, to our knowledge this paper presents the first results of CSI applied to the Gotcha
Challange data set, collected using an X-band circular SAR system in an urban environment.
The use of multiple cooperative sensors for the detection of person borne IEDs is investigated. The purpose of the effort
is to evaluate the performance benefits of adding multiple sensor data streams into an aided threat detection algorithm,
and a quantitative analysis of which sensor data combinations improve overall detection performance. Testing includes
both mannequins and human subjects with simulated suicide bomb devices of various configurations, materials, sizes
and metal content. Aided threat recognition algorithms are being developed to test detection performance of individual
sensors against combined fused sensors inputs. Sensors investigated include active and passive millimeter wave imaging
systems, passive infrared, 3-D profiling sensors and acoustic imaging. The paper describes the experimental set-up and
outlines the methodology behind a decision fusion algorithm-based on the concept of a "body model".
This paper describes several alternative techniques for detecting and localizing slowly-moving targets in cultural
clutter using synthetic aperture radar (SAR) data. Here, single-pass data is jointly processed from two or more
receive channels which are spatially offset in the along-track direction. We concentrate on two clutter cancelation
methods known as the displaced phase center antenna (DPCA) technique and along-track SAR interferometry
(AT-InSAR). Unlike the commonly-used space-time adaptive processing (STAP) techniques, both DPCA and
AT-InSAR tend to perform well in the presence of non-homogeneous urban or mountainous clutter. We show,
mathematically, the striking similarities between DPCA and AT-InSAR. Furthermore, we demonstrate using
experimental SAR data that these two techniques yield complementary information, which can be combined into
a "hybrid" technique that incorporates the advantages of each for significantly better performance. Results are
generated using the Gotcha challenge data, acquired using a three-channel X-band spotlight SAR system.
The Fourier Diffraction Theorem relates the data measured during electromagnetic, optical, or acoustic scattering
experiments to the spatial Fourier transform of the object under test. The theorem is well-known, but since it is
based on integral equations and complicated mathematical expansions, the typical derivation may be difficult for
the non-specialist. In this paper, the theorem is derived and presented using simple geometry, plus undergraduatelevel
physics and mathematics. For practitioners of synthetic aperture radar (SAR) imaging, the theorem is
important to understand because it leads to a simple geometric and graphical understanding of image resolution
and sampling requirements, and how they are affected by radar system parameters and experimental geometry.
Also, the theorem can be used as a starting point for imaging algorithms and motion compensation methods.
Several examples are given in this paper for realistic scenarios.
We describe a new approach for performing pseudo-imaging of point energy sources from spectral-temporal sensor data collected using a rotating-prism spectrometer. Pseudo-imaging, which involves the automatic localization, spectrum estimation, and identification of energetic sources, can be difficult for dim sources and/or noisy images, or in data containing multiple sources which are closely spaced such that their signatures overlap, or where sources move during data collection. The new approach is specifically designed for these difficult cases. It is developed within an iterative, maximum-entropy, framework which incorporates an efficient optimization over the space of all model parameters and mappings between image pixels and sources, or clutter. The optimized set of parameters is then used for detection, localization, tracking, and identification of the multiple sources in the data. The paper includes results computed from experimental data.
Ground-penetrating imaging radar ("GPiR") combines standard GPR with accurate positioning and advanced signal processing to create three-dimensional (3D) images of the shallow subsurface. These images can reveal soil conditions and buried infrastructure typically down to depths of about 2-3m with high resolution. A commercial GPiR called the CART Imaging System, which was designed for mapping urban infrastructure, has been developed. The CART system uses a radar array consisting of 17 antennas (9 transmitters and 8 receivers) that cover a 2m swath on the ground and can collect data while moving at speeds up to about 1 km/h. A laser theodolite tracks the position of the array during operation. The system collects enough data in a single pass to form a 3D image beneath its track; side-by-side passes are stitched together to create a seamless image of the subsurface. GPiR was first tested on a large scale in a project that mapped an area of approximately 12,000m2 in the south Bronx in four nights. Positions of surface features were also surveyed with the theodolite to provide a local reference grid. Final images were visualized with large-scale maps and electronic movies that scroll through the 3D data volume and show the enormous complexity of the subsurface in large cities.
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