A Multiple Radar Approach for Automatic Target Recognition of Aircraft using Inverse Synthetic Aperture Radar

Along with the improvement of radar technologies, Automatic Target Recognition (ATR) using Synthetic Aperture Radar (SAR) and Inverse SAR (ISAR) has come to be an active research area. SAR/ISAR are radar techniques to generate a two-dimensional high-resolution image of a target. Unlike other similar experiments using Convolutional Neural Networks (CNN) to solve this problem, we utilize an unusual approach that leads to better performance and faster training times. Our CNN uses complex values generated by a simulation to train the network; additionally, we utilize a multi-radar approach to increase the accuracy of the training and testing processes, thus resulting in higher accuracies than the other papers working on SAR/ISAR ATR. We generated our dataset with 7 different aircraft models with a radar simulator we developed called RadarPixel; it is a Windows GUI program implemented using Matlab and Java programming, the simulator is capable of accurately replicating a real SAR/ISAR configurations. Our objective is to utilize our multi-radar technique and determine the optimal number of radars needed to detect and classify targets.Comment: 8 pages, 9 figures, International Conference for Data Intelligence and Security (ICDIS

FMCW rail-mounted SAR: Porting spotlight SAR imaging from MATLAB to FPGA

In this work, a low-cost laptop-based radar platform derived from the MIT open courseware has been implemented. It can perform ranging, Doppler measurement and SAR imaging using MATLAB as the processor. In this work, porting the signal processing algorithms onto a FPGA platform will be addressed as well as differences between results obtained using MATLAB and those obtained using the FPGA platform. The target FPGA platforms were a Virtex6 DSP kit and Spartan3A starter kit, the latter was also low-cost to further reduce the cost for students to access radar technology

APPLYING DEEP LEARNING METHODS TO IDENTIFY TARGETS IN SYNTHETIC APERTURE RADAR IMAGES

Synthetic aperture radar (SAR) provides high-resolution imagery and can operate in the day and at night and in every weather condition. SAR has been used for military reconnaissance and surveillance. Examining SAR images manually, however, is challenging even for a specialist, since it is difficult to find high-value targets in a wide area of SAR images. This is especially true when time is critical for operations. Thus, an efficient, reliable method to analyze SAR images automatically is needed. To solve this problem, deep learning (DL) methods are developed for automatic target recognition (ATR). A convolutional neural network (CNN) is a deep-learning algorithm made up of several processing layers for target recognition and classification. One of the challenges in developing and testing a CNN algorithm is to find relevant datasets. The dataset used in this thesis comes from the Moving and Stationary Target Acquisition and Recognition program (MSTAR). In this research, the SAR ATR concept and performance are analyzed using several CNN DL architectures. Specifically, this investigation examines the effects of a few variable parameters within CNN DL architectures to gain insight into optimal strategies for using these architectures. Using CNN structures with different numbers of layers, it was possible to classify SAR targets successfully and automatically with state-of-the-art accuracy. This method proved useful for classification and recognition of military targets.Captain, Turkish Air ForceApproved for public release. distribution is unlimite

On the remote sensing of oceanic and atmospheric convection in the Greenland Sea by synthetic aperture radar

In this paper we discuss characteristic properties of radar signatures of oceanic and atmospheric convection features in the Greenland Sea. If the water surface is clean (no surface films or ice coverage), oceanic and atmospheric features can become visible in radar images via a modulation of the surface roughness, and their radar signatures can be very similar. For an unambiguous interpretation and for the retrieval of quantitative information on current and wind variations from radar imagery with such signatures, theoretical models of current and wind phenomena and their radar imaging mechanisms must be utilized. We demonstrate this approach with the analysis of some synthetic aperture radar (SAR) images acquired by the satellites ERS-2 and RADARSAT-1. In once case, an ERS-2 SAR image an a RADARSAT-1 ScanSAR image exhibit pronounced cell-like signatures with length scales on the order of 10-20 km and modulation depths of about 5-6 dB and 9-10 dB, respectively. Simulations with a numerical SAR imagaing model and various input current and wind fields reveal that the signatures in both images can be expained consistently by wind variations on the order of±2.5 ms, but not by surface current variations on realistic orders of magnitude. Accordingly, the observed features must be atmospheric convection cells. This is confirmed by visible typical cloud patterns in a NOAA AVHRR image of the test scenario. In another case, the presence of an oceanic convective chimney is obvious from in situ data, but no signatures of it are visible in an ERS-2 SAR image. We show by numerical simulations with an oceanic convection model and our SAR imaging model that this is consistent with theoretical predictions, since the current gradients associated with the observed chimney are not sufficiently strong to give rise to significant signatures in an ERS-2 SAR image under the given conditions. Further model results indicate that it should be generally difficult to observe oceanic convection features in the Greenland Sea with ERS-2 or RADARSAT-1 SAR, since their signatures resulting from pure wave-current interaction will be too weak to become visible in the noisy SAR images in most cases. This situation will improve with the availability of future high-resolution SARs such as RADARSAT-2 SAR in fine resolution mode (2004) and TerraSAR-X (2005) which will offer significantly reduced speckle noise fluctuations at comparable spatial resolutions and thus a much better visibility of small image variations on spatial scales on the order of a few hundred meters

A Study in GPS-Denied Navigation Using Synthetic Aperture Radar

In modern navigation systems, GPS is vital to accurately piloting a vehicle. This is especially true in autonomous vehicles, such as UAVs, which have no pilot. Unfortunately, GPS signals can be easily jammed or spoofed. For example, canyons and urban cities create an environment where the sky is obstructed and make GPS signals unreliable. Additionally, hostile individuals can transmit personal signals intended to block or spoof GPS signals. In these situations, it is important to find a means of navigation that doesn’t rely on GPS. Navigating without GPS means that other types of sensors or instruments must be used to replace the information lost from GPS. Some examples of additional sensors include cameras, altimeters, magnetometers, and radar. The work presented in this thesis shows how radar can be used to navigate without GPS. Specifically, synthetic aperture radar (SAR) is used, which is a method of processing radar data to form images of a landscape similar to images captured using a camera. SAR presents its own unique set of benefits and challenges. One major benefit of SAR is that it can produce images of an area even at night or through cloud cover. Additionally, SAR can image a wide swath of land at an angle that would be difficult for a camera to achieve. However, SAR is more computationally complex than other imaging sensors. Image quality is also highly dependent on the quality of navigation information available. In general, SAR requires that good navigation data be had in order to form SAR images. The research here explores the reverse problem where SAR images are formed without good navigation data and then good navigation data is inferred from the images. This thesis performs feasibility studies and real data implementations that show how SAR can be used in navigation without the presence of GPS. Derivations and background materials are provided. Validation methods and additional discussions are provided on the results of each portion of research

Elevation Extraction from Spaceborne SAR Tomography Using Multi-Baseline COSMO-SkyMed SAR Data

SAR tomography (TomoSAR) extends SAR interferometry (InSAR) to image a complex 3D scene with multiple scatterers within the same SAR cell. The phase calibration method and the super-resolution reconstruction method play a crucial role in 3D TomoSAR imaging from multi-baseline SAR stacks, and they both influence the accuracy of the 3D SAR tomographic imaging results. This paper presents a systematic processing method for 3D SAR tomography imaging. Moreover, with the newly released TanDEM-X 12 m DEM, this study proposes a new phase calibration method based on SAR InSAR and DEM error estimation with the super-resolution reconstruction compressive sensing (CS) method for 3D TomoSAR imaging using COSMO-SkyMed Spaceborne SAR data. The test, fieldwork, and results validation were executed at Zipingpu Dam, Dujiangyan, Sichuan, China. After processing, the 1 m resolution TomoSAR elevation extraction results were obtained. Against the terrestrial Lidar ‘truth’ data, the elevation results were shown to have an accuracy of 0.25 ± 1.04 m and a RMSE of 1.07 m in the dam area. The results and their subsequent validation demonstrate that the X band data using the CS method are not suitable for forest structure reconstruction, but are fit for purpose for the elevation extraction of manufactured facilities including buildings in the urban area

Elevation and Deformation Extraction from TomoSAR

3D SAR tomography (TomoSAR) and 4D SAR differential tomography (Diff-TomoSAR) exploit multi-baseline SAR data stacks to provide an essential innovation of SAR Interferometry for many applications, sensing complex scenes with multiple scatterers mapped into the same SAR pixel cell. However, these are still influenced by DEM uncertainty, temporal decorrelation, orbital, tropospheric and ionospheric phase distortion and height blurring. In this thesis, these techniques are explored. As part of this exploration, the systematic procedures for DEM generation, DEM quality assessment, DEM quality improvement and DEM applications are first studied. Besides, this thesis focuses on the whole cycle of systematic methods for 3D & 4D TomoSAR imaging for height and deformation retrieval, from the problem formation phase, through the development of methods to testing on real SAR data. After DEM generation introduction from spaceborne bistatic InSAR (TanDEM-X) and airborne photogrammetry (Bluesky), a new DEM co-registration method with line feature validation (river network line, ridgeline, valley line, crater boundary feature and so on) is developed and demonstrated to assist the study of a wide area DEM data quality. This DEM co-registration method aligns two DEMs irrespective of the linear distortion model, which improves the quality of DEM vertical comparison accuracy significantly and is suitable and helpful for DEM quality assessment. A systematic TomoSAR algorithm and method have been established, tested, analysed and demonstrated for various applications (urban buildings, bridges, dams) to achieve better 3D & 4D tomographic SAR imaging results. These include applying Cosmo-Skymed X band single-polarisation data over the Zipingpu dam, Dujiangyan, Sichuan, China, to map topography; and using ALOS L band data in the San Francisco Bay region to map urban building and bridge. A new ionospheric correction method based on the tile method employing IGS TEC data, a split-spectrum and an ionospheric model via least squares are developed to correct ionospheric distortion to improve the accuracy of 3D & 4D tomographic SAR imaging. Meanwhile, a pixel by pixel orbit baseline estimation method is developed to address the research gaps of baseline estimation for 3D & 4D spaceborne SAR tomography imaging. Moreover, a SAR tomography imaging algorithm and a differential tomography four-dimensional SAR imaging algorithm based on compressive sensing, SAR interferometry phase (InSAR) calibration reference to DEM with DEM error correction, a new phase error calibration and compensation algorithm, based on PS, SVD, PGA, weighted least squares and minimum entropy, are developed to obtain accurate 3D & 4D tomographic SAR imaging results. The new baseline estimation method and consequent TomoSAR processing results showed that an accurate baseline estimation is essential to build up the TomoSAR model. After baseline estimation, phase calibration experiments (via FFT and Capon method) indicate that a phase calibration step is indispensable for TomoSAR imaging, which eventually influences the inversion results. A super-resolution reconstruction CS based study demonstrates X band data with the CS method does not fit for forest reconstruction but works for reconstruction of large civil engineering structures such as dams and urban buildings. Meanwhile, the L band data with FFT, Capon and the CS method are shown to work for the reconstruction of large manmade structures (such as bridges) and urban buildings

Wide-Angle Multistatic Synthetic Aperture Radar: Focused Image Formation and Aliasing Artifact Mitigation

Traditional monostatic Synthetic Aperture Radar (SAR) platforms force the user to choose between two image types: larger, low resolution images or smaller, high resolution images. Switching to a Wide-Angle Multistatic Synthetic Aperture Radar (WAM-SAR) approach allows formation of large high-resolution images. Unfortunately, WAM-SAR suffers from two significant implementation problems. First, wavefront curvature effects, non-linear flight paths, and warped ground planes lead to image defocusing with traditional SAR processing methods. A new 3-D monostatic/bistatic image formation routine solves the defocusing problem, correcting for all relevant wide-angle effects. Inverse SAR (ISAR) imagery from a Radar Cross Section (RCS) chamber validates this approach. The second implementation problem stems from the large Doppler spread in the wide-angle scene, leading to severe aliasing problems. This research effort develops a new anti-aliasing technique using randomized Stepped-Frequency (SF) waveforms to form Doppler filter nulls coinciding with aliasing artifact locations. Both simulation and laboratory results demonstrate effective performance, eliminating more than 99% of the aliased energy

Synthetic Aperture Radar (SAR) data processing

The available and optimal methods for generating SAR imagery for NASA applications were identified. The SAR image quality and data processing requirements associated with these applications were studied. Mathematical operations and algorithms required to process sensor data into SAR imagery were defined. The architecture of SAR image formation processors was discussed, and technology necessary to implement the SAR data processors used in both general purpose and dedicated imaging systems was addressed