Scattering Center Extraction and Recognition Based on ESPRIT Algorithm

Inverse Synthetic Aperture Radar (ISAR) generates high quality radar images even in low visibility. And it provides important physical features for space target recognition and location. This thesis focuses on ISAR rapid imaging, scattering center information extraction, and target classification. Based on the principle of Fourier imaging, the backscattering field of radar target is obtained by physical optics (PO) algorithm, and the relation between scattering field and objective function is deduced. According to the resolution formula, the incident parameters of electromagnetic wave are set reasonably. The interpolation method is used to realize three-dimensional (3D) simulation of aircraft target, and the results are compared with direct imaging results. CLEAN algorithm extracts scattering center information effectively. But due to the limitation of resolution parameters, traditional imaging can’t meet the actual demand. Therefore, the super-resolution Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) algorithm is used to obtain spatial target location information. The signal subspace and noise subspace are orthogonal to each other. By combining spatial smoothing method with ESPRIT algorithm, the physical characteristics of geometric target scattering center are obtained accurately. In particular, the proposed method is validated on complex 3D aircraft targets and it proves that this method is applied to most scattering mechanisms. The distribution of scattering centers reflects the geometric information of the target. Therefore, the electromagnetic image to be recognized and ESPRIT image are matched by the domain matching method. And the classification results under different radii are obtained. In addition, because the neural network can extract rich image features, the improved ALEX network is used to classify and recognize target data processed by ESPRIT. It proves that ESPRIT algorithm can be used to expand the existing datasets and prepare for future identification of targets in real environments. Final a visual classification system is constructed to visually display the results

Fractional Focusing and the Chirp Scaling Algorithm With Real Synthetic Aperture Radar Data

abstract: For synthetic aperture radar (SAR) image formation processing, the chirp scaling algorithm (CSA) has gained considerable attention mainly because of its excellent target focusing ability, optimized processing steps, and ease of implementation. In particular, unlike the range Doppler and range migration algorithms, the CSA is easy to implement since it does not require interpolation, and it can be used on both stripmap and spotlight SAR systems. Another transform that can be used to enhance the processing of SAR image formation is the fractional Fourier transform (FRFT). This transform has been recently introduced to the signal processing community, and it has shown many promising applications in the realm of SAR signal processing, specifically because of its close association to the Wigner distribution and ambiguity function. The objective of this work is to improve the application of the FRFT in order to enhance the implementation of the CSA for SAR processing. This will be achieved by processing real phase-history data from the RADARSAT-1 satellite, a multi-mode SAR platform operating in the C-band, providing imagery with resolution between 8 and 100 meters at incidence angles of 10 through 59 degrees. The phase-history data will be processed into imagery using the conventional chirp scaling algorithm. The results will then be compared using a new implementation of the CSA based on the use of the FRFT, combined with traditional SAR focusing techniques, to enhance the algorithm's focusing ability, thereby increasing the peak-to-sidelobe ratio of the focused targets. The FRFT can also be used to provide focusing enhancements at extended ranges.Dissertation/ThesisM.S. Electrical Engineering 201

Utilizing Near-Field Measurements to Characterize Far-Field Radar Signatures

The increased need for stealth aircraft requires an on-site Far-Field (FF) Radar Cross-Section (RCS) measurement process. Conducting these measurements in on-site Near-Field (NF) monostatic facilities results in significant savings for manufacturers and acquisition programs. However, NF measurements are not directly extended to a FF RCS. Therefore, a large target Near-Field to Far-Field Transformation (NFFFT) is needed for RCS measurements. One approach requires an Inverse Synthetic Aperture Radar (ISAR) process to create accurate scattering maps. The focus of this work is the development of accurate NF scattering maps generated by a monostatic ISAR process. As a first look, the process is isolated to a simulated environment to avoid the uncontrollable effects of real measurement environments. The simulation begins with a NF Synthetic Target Generator (STG) which approximates a target using scattering centers illuminated by spherical electromagnetic waves to approximating NF scattering. The resulting NF In-phase and Quadrature (IQ) data is used in a Trapezoidal ISAR process to create spatially distorted images that are accurately corrected within the ISAR process resolution using a newly developed NF correction. The resulting spatially accurate ISAR images do not complete the NFFFT. However, accurate scattering maps are essential for process development

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

Multichannel techniques for 3D ISAR

This thesis deals with the challenge of forming 3D target reconstruction by using spatial multi-channel ISAR configurations. The standard output of an ISAR imaging system is a 2D projection of the true three-dimensional target reflectivity onto an image plane. The orientation of the image plane cannot be predicted a priori as it strongly depends on the radar-target geometry and on the target motion, which is typically unknown. This leads to a difficult interpretation of the ISAR images. In this scenario, this thesis aim to give possible solutions to such problems by proposing three 3D processing based on interferometry, beamforming techniques and MIMO InISAR systems. The CLEAN method for scattering centres extraction is extended to multichannel ISAR systems and a multistatic 3D target reconstruction that is based on a incoherent technique is suggested

Multichannel techniques for 3D ISAR

This thesis deals with the challenge of forming 3D target reconstruction by using spatial multi-channel ISAR configurations. The standard output of an ISAR imaging system is a 2D projection of the true three-dimensional target reflectivity onto an image plane. The orientation of the image plane cannot be predicted a priori as it strongly depends on the radar-target geometry and on the target motion, which is typically unknown. This leads to a difficult interpretation of the ISAR images. In this scenario, this thesis aim to give possible solutions to such problems by proposing three 3D processing based on interferometry, beamforming techniques and MIMO InISAR systems. The CLEAN method for scattering centres extraction is extended to multichannel ISAR systems and a multistatic 3D target reconstruction that is based on a incoherent technique is suggested

Passive Synthetic Aperture Radar Imaging Using Commercial OFDM Communication Networks

Modern communication systems provide myriad opportunities for passive radar applications. OFDM is a popular waveform used widely in wireless communication networks today. Understanding the structure of these networks becomes critical in future passive radar systems design and concept development. This research develops collection and signal processing models to produce passive SAR ground images using OFDM communication networks. The OFDM-based WiMAX network is selected as a relevant example and is evaluated as a viable source for radar ground imaging. The monostatic and bistatic phase history models for OFDM are derived and validated with experimental single dimensional data. An airborne passive collection model is defined and signal processing approaches are proposed providing practical solutions to passive SAR imaging scenarios. Finally, experimental SAR images using general OFDM and WiMAX waveforms are shown to validate the overarching signal processing concept

Contributions in inverse synthetic aperture radar imaging

Ph.DDOCTOR OF PHILOSOPH

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