Two major missions of Surveillance systems are imaging and ground moving target indication (GMTI). Recent advances in
coded aperture electro optical systems have enabled persistent surveillance systems with extremely large fields of regard.
The areas of interest for these surveillance systems are typically urban, with spatial topologies having a very definite
structure. We incorporate aspects of a priori information on this structure in our aperture code designs to enable optimized
dealiasing operations for undersampled focal plane arrays. Our framework enables us to design aperture codes to minimize
mean square error for image reconstruction or to maximize signal to clutter ratio for GMTI detection. In this paper we
present a technical overview of our code design methodology and show the results of our designed codes on simulated
DIRSIG mega-scene data.
We present an investigation of the performance of coded aperture optical systems where the elements of a set of binary
coded aperture masks are applied over a sequence of acquired images. In particular, we are interested in investigating
code sequences and image reconstruction algorithms that reduce the optical fidelity and hardware requirements for the
system. Performance is jointly tied to the mask design, the image estimation algorithm, and the inherent optical response
of the system. As such, we adopt a simplified reconstruction model and consider generalized optical system aberrations
in designing masks used for multi-frame reconstruction of the imagery. We also consider the case of non-Nyquist
sampled (aliased) imagery. These investigations have focused on using a regularized least-squares reconstruction model
and mean squared error as a performance metric. Masks are found by attempting to minimize a closed form objective
that predicts the mean squared error for the reconstruction algorithm. We find that even with suboptimal solutions that
binary masks can be used to improve imagery over the case of an uncoded aperture with the same aberration.
The IR antenna-pair coupled micro-bolometers has demonstrated its unique power response features compared to the single antenna coupled micro-bolometers. The response pattern is determined by that of the single antenna and an interference oscillation term of the antenna-pair with respective to the angle of incidence of the radiation field, and can be steered by shifting the location of the bolometer. This paper explores the potential application of antenna-pair coupled detector in beam synthesis. It describes an array configuration based upon these micro-bolometers, and discusses the corresponding coherent data processing method for the purpose of obtaining response pattern narrowing effects from such an array. This directional gain enhancement, together with the beam steering control, could potentially lead to an array capable of providing a novel IR lensless imaging technique.
Diffractive optical systems in the Infrared (IR) wavelength regime are being re-examined for remote sensing
applications. A pupil-plane adaptive coded aperture can enable a fine resolution, wide field of view sensor system
without mechanical scanning. Due to the relatively long wavelengths, coded aperture systems in the IR have unique
issues in regards to e.g. X-ray coded apertures. These include diffraction effects, wavelength dependence of optical
elements, off axis aberrations due to thick screens, etc. In this paper, we provide a general system model framework
based on partial coherence theory that enables us to explore many of the technical challenges in IR diffractive
imaging. This paper develops the general theory and shows examples of issues that impact the optical transfer
function (OTF) and impulse response of these types of architectures.
KEYWORDS: Synthetic aperture radar, 3D acquisition, 3D image processing, Detection and tracking algorithms, Signal to noise ratio, Clouds, 3D modeling, Computer simulations, Device simulation, Data modeling
Methods of generating more literal, easily interpretable imagery from 3-D SAR data are being studied to provide all weather, near-visual target identification and/or scene interpretation. One method of approaching this problem is to automatically generate shape-based geometric renderings from the SAR data. In this paper we describe the application of the Marching Tetrahedrons surface finding algorithm to 3-D SAR data. The Marching Tetrahedrons algorithm finds a surface through the 3-D data cube, which provides a recognizable representation of the target surface. This algorithm was applied to the public-release X-patch simulations of a backhoe, which provided densely sampled 3-D SAR data sets. The performance of the algorithm to noise and spatial resolution were explored. Surface renderings were readily recognizable over a range of spatial resolution, and maintained their fidelity even under relatively low Signal-to-Noise Ratio (SNR) conditions.
In this paper we describe a hybrid method of using collected and computer generated signatures for Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) algorithms. Currently, there is significant activity in developing both model-based (e.g. MSTAR) and collected template-based (e.g. STARLOS, SAIP) approaches. Each approach has significant strengths and weaknesses. The strength of the model-based approach is that it can finely sample the many competing hypotheses that describe the data under test. Collected template-based approaches have a strong sense of realism due to the fact that the templates were collected using actual deployed targets and SAR systems. A hybrid approach attempts to meld the strengths of both. We describe methodologies for determining which segments of a reference signature should be provided by collected data or models and how they should be combined. We also show that these new hybrid templates outperform either a model-only or collected template-only approach for target classification. ATR performance results are provided in the form of ROC curves. Also, some topics for future research are discussed.
In this paper we describe a novel method of automatic target detection applied directly to the synthetic aperture radar (SAR) phase history. Our algorithm is based on a sequential likelihood ratio test (Wald test). The time dynamic behavior of the SAR phase history is modeled as a 2D autoregressive process. The sequential test attempts to dynamically ascertain the presence/absence of a target while the SAR phase history data is being collected. A target/no target decision can then be made during the collection aperture. System resources such as collection aperture and image formation processing can be dynamically reallocated depending on scene content. In contrast, image based detection methods wait until the entire aperture is collected, an image formed, then an algorithm is applied. We will show that significant savings in collection aperture can be obtained using this detection structure which may increase system search rates.
KEYWORDS: Image processing, Signal processing, Radar, Optoelectronics, 3D image processing, Fourier transforms, 3D acquisition, Scattering, Optical signal processing, Data processing
A 3D formulation of inverse synthetic aperture (ISAR) is presented showing how the various geometry and motion parameters determine the image generation properties. We then show how the arbitrary formatting capability of an opto-electronic processor can be used to format the data in such a way as to focus the image. The focusing parameters are found from tracking prominent points in the radar data itself and using rigid body constraints imposed on the data. The opto-electronic processor is particularly suited for generated image data such as found with ISAR. The processor is a time-integrating architecture that uses acousto-optic scanners for arbitrary formatting and a modified Kosters interferometer for stable Fourier transformation. This research is being funded by the Office of Naval Research.
We describe some performance trades for a hybrid optical processor for real-time synthetic aperture radar (SAR) image formation. A 2D Fourier transforming time-integrating interferometrically based optical processor is a key element of the system. The optical processor uses a modulated laser diode for radar signal insertion, crossed 1D acousto-optic scanners for 2D scanning, a modified Koster interferometer for fringe generation, and fast detector arrays (cameras) for light detection and integration. The image dynamic range is enhanced by processing many camera frames. Digital pre- and post-processing play essential roles in the system enhancement. We present the characteristics of this type of processor and consider some of its performance trades. The optical processor design approach lends itself to the important attributes of high (real-time) data rates, multiple SAR mode processing capabilities, compact and rugged packaging, and power efficiency.
We describe a band selection process based on wavelet analysis of hyperspectral data which naturally decomposes the data into sub-bands. Wavelet analysis allows the control of the position, resolution, and envelope of the specific spectral sub-bands which will be selected. The sub-band sets are selected to maximize the Kullback-Liebler distance between specific classes of materials for a specific dimensionality contraint or discrimination performance goal. A sequential construction of the sub-band sets is used as an approximation to the global maximization operation over all possible sub-band sets. A max/min strategy is also introduced to provide a robust framework for sub-band selection when faced with multiple materials. We show band selection and material classification results of this technique applied to Fourier transform spectrometer data.
We present a detection concept for initial target screening based on features that are derived from a multiresolution decomposition of synthetic aperture radar (SAR) data. The physical motivation of the multiresolution feature-based approach is the exploitation of signature oscillations produced by the interference between prominant scatterers in cultural objects when resolution is varied. We develop a generalized likelihood ratio test detector which differentiates between first order autoregressive multiresolution processes attributed to different spatial areas. We then derive two special cases of this detector motivated by arguments regarding the clutter statistics. We show that these schemes significantly outperform a standard energy detector operating on the finest available SAR resolution only.
We describe the current status of a hybrid optical processor being developed for real-time synthetic aperture radar (SAR) image formation. The processor is being developed for insertion into the ERIM spotlight mode SAR airborne data collection system under the ARPA TOPS program monitored by the Army Research Lab. A 2D Fourier transforming time-integrating interferometrically based optical processor is a key element of the system. The optical processor uses a modulated laser diode for radar signal insertion, crossed 1D acousto-optic scanners for 2D scanning, a modified Koster interferometer for fringe generation, and fast detector arrays for light detection and integration. The image space-bandwidth-product and dynamic range are enhanced by processing time-multiplexed interlaced image subpatches at real-time rates. Digital pre- and post-processing play essential roles in the system enhancement. The final image is a mosaic of the subpatch images. The optical processor design approach lends itself to the important attributes of high (real-time) data rates, multiple SAR mode processing capabilities, compact and rugged packaging, and power efficiency.
The development and airborne demonstration of a compact realtime optical processor for synthetic aperture radar (SAR) image formation under the DARPA TOPS program is described. The ERIM spotlight mode SAR system and its processing requirements are presented. It is shown that a 2-D Fourier transforming time-integrating interferometrically based optical processor is an attractive solution to the processing requirements. The optical processor uses a modulated laser diode for radar signal insertion, crossed acousto-opto scanners for 2-D scanning, a modified Köster interferometer for fringe generation, fast detector arrays for light detection and integration, and accumulating frame grabbers to build up the dynamic range of the image. Analysis, simulation, and laboratory experimental results are presented.
The spatially-scanning, 2D, time-integrating hybrid interferometric processors presented employ directly-modulated CW laser diodes as input sources and are applicable to complex SAR data processing in real time. Crossed acoustooptic cells scan the input signal over a virtual 2D input space, and an optical interference pattern is detected with a solid-state detector array camera. Continuous transfer of the frames of integrated interference from the camera to a digital image processor, overall dynamic range is increased over that of the camera alone. Envelope detection in the display circuitry generates a continuous real-time representation of image magnitude.
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