Coherent change detection and interferometric ISAR measurements in the folded compact range

A folded compact range configuration has been developed ant the Sandia National Laboratories` compact range antenna and radar-cross- section measurement facility as a means of performing indoor, environmentally-controlled, far-field simulations of synthetic aperture radar (SAR) measurements of distributed target samples (i.e. gravel, sand, etc.). The folded compact range configuration has previously been used to perform coherent-change-detection (CCD) measurements, which allow disturbances to distributed targets on the order of fractions of a wavelength to be detected. This report describes follow-on CCD measurements of other distributed target samples, and also investigates the sensitivity of the CCD measurement process to changes in the relative spatial location of the SAR sensor between observations of the target. Additionally, this report describes the theoretical and practical aspects of performing interferometric inverse-synthetic-aperture-radar (IFISAR) measurements in the folded compact range environment. IFISAR measurements provide resolution of the relative heights of targets with accuracies on the order of a wavelength. Several examples are given of digital height maps that have been generated from measurements performed at the folded compact range facility

PRECONDITIONING AND THE APPLICATION OF CONVOLUTIONAL NEURAL NETWORKS TO CLASSIFY MOVING TARGETS IN SAR IMAGERY

Synthetic Aperture Radar (SAR) is a principle that uses transmitted pulses that store and combine scene echoes to build an image that represents the scene reflectivity. SAR systems can be found on a wide variety of platforms to include satellites, aircraft, and more recently, unmanned platforms like the Global Hawk unmanned aerial vehicle. The next step is to process, analyze and classify the SAR data. The use of a convolutional neural network (CNN) to analyze SAR imagery is a viable method to achieve Automatic Target Recognition (ATR) in military applications. The CNN is an artificial neural network that uses convolutional layers to detect certain features in an image. These features correspond to a target of interest and train the CNN to recognize and classify future images. Moving targets present a major challenge to current SAR ATR methods due to the “smearing” effect in the image. Past research has shown that the combination of autofocus techniques and proper training with moving targets improves the accuracy of the CNN at target recognition. The current research includes improvement of the CNN algorithm and preconditioning techniques, as well as a deeper analysis of moving targets with complex motion such as changes to roll, pitch or yaw. The CNN algorithm was developed and verified using computer simulation.Lieutenant, United States NavyApproved for public release. Distribution is unlimited

Effects of Motion Measurement Errors on Radar Target Detection

This thesis investigates the relationships present between signal-to-clutter ratios, motion measurement errors, image quality metrics, and the task of target detection, in order to discover what factor merit greater focus in order to attain the highest probability of target detection success. This investigation is accomplished by running a high number of Monte Carlo trials through a coherent target detector and analyzing the results. The aforementioned relationships are demonstrated via sample synthetic aperture radar imagery, histograms, receiver operating characteristics curves, and error bar plots