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检索条件"任意字段=SPIE Conference on Algorithm for Synthetic Aperture Radar Imagery V"

共 386 条记录，以下是1-10 订阅

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Morphology and Refocus of Fast-Moving Exo-clutter Targets in synthetic aperture radar imagery 31

Morphology and Refocus of Fast-Moving Exo-clutter Targets in...

引用

conference on algorithms for synthetic aperture radar imagery XXXI

作者： Carrell, David A. Naval Postgrad Sch Elect & Comp Engn Monterey CA 93943 USA

ISBN: (纸本)9781510673830;9781510673823

The signatures of moving targets with synthetic aperture radar (SAR) imagery are typically smeared primarily in the radar cross-range direction. For such endoclutter targets, the time interval between adjacent radar pulses along the synthetic aperture is sufficiently small that the collection of the target signature does not experience aliasing. If a mobile target is moving sufficiently fast during the SAR collection interval, then the signature exhibits a smearing that lies along a diagonal in the SAR image space of down-range versus cross-range. In the present analysis, the properties of such diagonal signatures are investigated for constant velocity targets. The research further includes the development of new and advanced mathematics and algorithms that yield finely refocused SAR imagery for automatic detection and recognition, even for relatively fast exoclutter targets with non-uniform rotation. This proposal develops and applies two algorithms to detect and refocus fast-moving exoclutter surface targets within SAR imagery: (1) Rapid xoclutter Eocus Transformation (REFT) algorithm to transform input SAR imagery into a form conducive for the detection of fast-moving exoclutter targets, and (2) Arbitrary Rigid Object Motion Autofocus (AROMA) algorithm for automatic focus of moving targets with non-uniform rotation and translation.

关键词： synthetic aperture radar radar imaging moving target imaging

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Adversarial Physics-based Augmentations for Robust Training Using synthetic Data 31

Adversarial Physics-based Augmentations for Robust Training ...

引用

conference on algorithms for synthetic aperture radar imagery XXXI

作者： Clark, Emma Walters, Ellie Zelnio, Edmund Univ Illinois Urbana IL 60680 USA Air Force Res Lab Wright Patterson AFB OH USA

ISBN: (纸本)9781510673830;9781510673823

Given the limited amount of measured synthetic aperture radar data available to train object recognition algorithms. synthetic data is used to train the algorithm while using measured data to test. To account for the variability of measured data and to ensure robustness to various conditions, extensive physics- based augmentations are used during the training process. These augmentations include target, background, and sensor variability. In order to explore the augmentation space most efficiently, the background and sensor variability are explored on-line during the training process using an adversarial learning strategy. Performance trades are reported as a function of the various augmentation strategies.

关键词： synthetic aperture radar Classification

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Operating Condition Sampling Strategies for Evaluating ATR Performance 31

Operating Condition Sampling Strategies for Evaluating ATR P...

引用

conference on algorithms for synthetic aperture radar imagery XXXI

作者： Stephens, Shaun Ross, Jacob Hill, Charles Coleman, William Scherreik, Matthew Etegent Technol Ltd Beavercreek OH 45431 USA Air Force Res Lab Sensors Directorate Wright Patterson AFB OH USA

ISBN: (纸本)9781510673830;9781510673823

synthetic data is commonly used to assess the performance of synthetic aperture radar (SAR) Automatic Target Recognition (ATR) systems modeling the OC space in question. In this work we demonstrate that the use of an informed sampling technique compared to an uninformed sampling approach can efficiently assess the "OC gap" between train and test OC spaces as the gap narrows. To demonstrate the effectiveness of an informed sampling approach, SAR ATR experiments are conducted as a function of how representative the train distribution of OCs are compared to the test OC space given a variety of challenging OC scenarios. algorithm performance is assessed over a series of experiments given discrepancies between azimuth and depression angle of the sensor

关键词： synthetic aperture radar operating conditions automatic target recognition synthetic data

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Boot-Strapping Methods for Improved SAR-GMTI 31

Boot-Strapping Methods for Improved SAR-GMTI

引用

conference on algorithms for synthetic aperture radar imagery XXXI

作者： Rigling, Brian D. Majumder, Uttam Zelnio, Edmund Univ Dayton Dept Elect & Comp Engn 300 Coll Pk Dayton OH 45469 USA

ISBN: (纸本)9781510673830;9781510673823

Extensive work has been published on theoretical methods for ground moving target indication (GMTI) in synthetic aperture radar (SAR) images. The primary challenge to this problem is that SAR imaging inherently assumes a stationary scene in order to allow a long coherent processing interval, and thus moving targets that violate that assumption may be difficult to reliably detect. Recent work in this area has benefited from experiments with measured data sets, that are of high-quality but also include the measurement imperfections innate to any measured data. In many instances, sophisticated SAR-GMTI techniques have been brought to bear without necessarily employing well-known bootstrapping methods for data calibration and error correction. This leads to comparisons with algorithm baselines that are not reflective of the state of the art and to performance analysis with new algorithms that may be inaccurate or even pessimistic due to the presence of unresolved measurement errors. In this paper, we show that straightforward methods of SAR data calibration allows high-quality SAR-GMTI images to be achieved from measured data via simplistic clutter cancellation and along-track interferometry. These simple steps may serve as the basis for the testing of more advanced algorithms.

关键词： synthetic aperture radar ground moving target indication

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synthetic Data, Measured Data Integrated Learning Experiments 30

Synthetic Data, Measured Data Integrated Learning Experiment...

引用

conference on algorithms for synthetic aperture radar imagery XXX

作者： Cavallo, Jeremy Zelnio, Edmund Wayne State Univ Detroit MI 48202 USA Air Force Res Lab WP AFB OH USA

ISBN: (纸本)9781510661547;9781510661554

This paper addresses the problem of adequately training deep learning networks to be operational on measured synthetic aperture radar (SAR) data when the quantity of measured data alone is insufficient. In particular, this is a study in transfer learning utilizing synthetically generated SAR data and measured SAR data to train a deep learning algorithm to classify military tactical vehicles. The present study is motivated by sparsity of measured data for Air Force targets of interest. Specifically, this effort builds on an existing Convolution Neural Network (CNN) architecture, i.e. Understanding the synthetic and Measured GAP from the CNN Classifier Perspective(1) and aims to improve achievable performance by increasing the algorithm complexity and performing parameter analysis on MSTAR data.

关键词： Transfer Learning CNN SAR Classification

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Efficient Computation of Superresolution Methods for SAR Imaging 30

Efficient Computation of Superresolution Methods for SAR Ima...

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conference on algorithms for synthetic aperture radar imagery XXX

作者： Batts, Alex Rigling, Brian Univ Dayton 300 Coll Pk Dayton OH USA

ISBN: (纸本)9781510661547;9781510661554

While traditional Fourier methods of SAR imaging are well known in addition to being easy to implement, they have limitations in terms of quality, particularly with respect to speckle, scintillation, and side lobe artifacts. Methods of SAR imaging that have shown promise include superresolution methods like the Minimum variance Method (MvM) and the Multiple Signal Classification (MUSIC) algorithm;however, these algorithms are computationally intense. Both algorithms require the estimation of a correlation matrix, and manipulations thereof, as well as computing the image spectrum through computation of a quadratic form for each image pixel. This paper presents an efficient method for estimating the correlation matrix and shows how the structure of the correlation matrix can be exploited to efficiently compute the aforementioned superresolution methods.

关键词： synthetic aperture radar Minimum variance Method MUSIC algorithm Efficient Computation

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Efficient High Resolution 3D SAR Imaging via Super-resolution Spectral Estimation Methods 31

Efficient High Resolution 3D SAR Imaging via Super-resolutio...

引用

conference on algorithms for synthetic aperture radar imagery XXXI

作者： Batts, Alex Rigling, Brian Majumder, Uttam Zelnio, Edmund Univ Dayton 300 Coll Pk Dayton OH 68412 USA Natl Geospatial Intelligence Agcy 7500 GEOINT Dr Springfield VA USA Air Force Res Lab 1864 4th St Wright Patterson AFB OH USA

ISBN: (纸本)9781510673830;9781510673823

The volumetric scattering of radar waves allows for three-dimensional SAR imaging of a scene. As is the case in two-dimensions, traditional Fourier methods are easier to implement, but have limitations in terms of quality with respect to speckle, scintillation, and side lobe artifacts. Other methods that have shown promise, such as superresolution methods like the Minimum variance Method (MvM) and the Multiple Signal Classification (MUSIC) algorithm, have been shown to produce high quality images. However, these algorithms are computationally intense as they require the estimation of a correlation matrix, and manipulations thereof, as well as computing the image spectrum through computation of a quadratic form for each image pixel. This paper presents an efficient method for computing these superresolution techniques for 3D SAR images.

关键词： 3D image processing Image resolution Spectral resolution Stereoscopy Super resolution synthetic aperture radar

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Out-of-distribution detection for SAR imagery using ATR systems 31

Out-of-distribution detection for SAR imagery using ATR syst...

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conference on algorithms for synthetic aperture radar imagery XXXI

作者： Hill, Charles Etegent Technol 2601 Mission Point Blvd STE 220 Beavercreek OH 45431 USA

ISBN: (纸本)9781510673830;9781510673823

A typical assumption for deploying machine learning models is that the model training and inference data were drawn from the same distribution. However, this assumption rarely holds true for systems deployed in the open world. Inference data can drift over time for numerous reasons, such as changes in operating conditions, adversarial modifications to targets, or sensor degradation. Despite these changes, deep learning models are especially vulnerable to issuing over-confident predictions on out-of-distribution data. This work seeks to address this issue by proposing a framework for describing out-of-distribution detection pipelines, proposing an out-of-distribution detection algorithm using Gaussian Mixture Models which is well suited for SAR ATR, and by evaluating multiple pipelines which exploit the intermediate states of ATR model deep neural networks. This work studies candidate pipelines with varied amounts of dimensionality reduction and detection algorithms on the SAMPLE+ dataset challenge problems for clutter and confuser rejection. Despite the exclusion of out-of-distribution samples from pipeline training, the presented results demonstrate that these samples can nonetheless be reliably detected, exceeding baseline performance by more than 10 percentage points.

关键词： out-of-distribution detection anomaly detection synthetic aperture radar model trust open set recognition artificial intelligence machine learning

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3D SAR image reconstruction of ground vehicles using sparse multiple flight path data 30

3D SAR image reconstruction of ground vehicles using sparse ...

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conference on algorithms for synthetic aperture radar imagery XXX

作者： Sotirelis, Paul Gilmore, Sean Air Force Res Lab 2241 Avion Circle Wright Patterson AFB OH 45433 USA Analyt Designs Inc 245 East Gay St Columbus OH 43215 USA

ISBN: (纸本)9781510661547;9781510661554

We present an exploration of collection geometries for producing three-dimensionally (3D) focused synthetic aperture radar (SAR) derived point clouds. We consider collection geometries that can be produced by a series of continuous curves such as multiple flight paths of a fixed wing aircraft or multiple passes of a satellite orbiting the earth. As part of our analysis, we use sparse methods to reconstruct undersampled radar data. We use back-projection to focus the radar data into the spatial domain, onto a uniform volumetric grid. Additionally, we use a 3D resonance finding algorithm to extract scattering centers from volumetric radar data to produce 3D point clouds. Our analysis is based upon synthetic radar data produced using the parameters derived from our laboratory's in-door turntable inverse synthetic radar aperture (ISAR) system.(1) A key point of our analysis is to determine how many repeat passes are required to achieve a given fidelity of an object's 3D representation. Analysis will include a comparison with interferometric methods, particularly with regard to the fidelity and the point cloud density. We use a digital model of a civilian pickup truck that has been validated for use in synthetic prediction, both as a full-size model in outdoor collects as well as a reduced scale model measured indoors in our lab. Future research directions are also discussed.

关键词： synthetic aperture radar radar Interferometric synthetic aperture radar 3D modeling Point clouds

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Decision Level Fusion Experiments on MWIR, vNIR, and SAR imagery 30

Decision Level Fusion Experiments on MWIR, VNIR, and SAR Ima...

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conference on algorithms for synthetic aperture radar imagery XXX

作者： Ross, Jacob Stephens, Shaun Bischof, Patrick Nolan, Adam Scherreik, Matthew Etegent Technol Beavercreek OH 45431 USA Air Force Res Lab Wright Patterson AFB OH USA

ISBN: (纸本)9781510661547;9781510661554

Decision level fusion algorithms combine separate classification scores of a test sample to make a unified class declaration. The aim of decision level fusion algorithms is to achieve better classification performance by combining decisions rather than picking the single best performing algorithm. Multi-modal fusion can be achieved by fusing scores of deep learning models trained on different sensing modalities and tested on a target imaged from co-located sensors. Given differences in phenomenology, fusing EO sensors with SAR may boost performance when extended operating conditions are detrimental to the performance of one modality over the other. The EO modalities discussed in this work (vNIR and MWIR) are susceptible to the time of day while SAR is robust to time of day. Conversely, SAR returns of a target can vary greatly when aspect angle changes while EO modalities are relatively robust. This work analyzes the effectiveness of decision level fusion algorithms on MWIR, vNIR, and SAR modalities given disparate times of day and collection aspects.

关键词： synthetic aperture radar Performance modeling Sensors Automatic target recognition Data fusion

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