Frequency-Diverse Computational Automotive Radar Technique for Debris Detection

: This dissertation presents both analytic and processing techniques for various radar imaging systems. A two dimensional system classification method, which is very general and hence applies to the special case of radar imaging systems as well, is proposed to assist in understanding the structure and describing the limitations of 2-D systems. Once a given system is identified with the simplest possible class, the specific techniques can be directly utilized to process the data or reconstruct the images. Following a review of radar imaging principles, several coherent radar systems are analyzed and experimented upon. They include synthetic aperture radar (SAR) ground mapping, imaging of an aircraft target from turntable data, and imaging of a flying aircraft target. In each case the point spread function (PSF) of the imaging system is derived or estimated. Physical considerations are then incorporated in mathematical PSF's to categorize the imaging systems according to the aforementioned system classification principle proposed.

Recently many researchers have turned their interests to Orthogonal Frequency Division Multiplexing (OFDM) signal since its promising features in radar. This paper concerns a novel Synthetic Aperture Radar (SAR) system which employs this OFDM signal. Though the OFDM will bring lots of advantages, the performance may be changed since the signal of SAR is different from conventional ones. To evaluate the change, this paper will give a theoretical analysis and the corresponding simulation results from the viewpoint of ambiguity function. This ambiguity function is equivalent to the role of Point Spread Function (PSF) which is often used in SAR for evaluating resolutions. Comparing to the conventional SAR with LFM signal, it is proved that the resolution of SAR based on OFDM is equivalent to the conventional one or even better. This ambiguity function also provides a principle for selecting various OFDM signals as the transmit signal of SAR.

Computational imaging techniques that rely on a compressed set of measurements and exploit prior information such as target size, scene sparsity, transceiver radiation pattern, etc are rapidly gaining popularity in areas such as medical and security imaging, remote sensing, and automotive radar as they can significantly reduce SWAP-C (Size, Weight, Power, and Cost) of hardware modules, especially at millimeter-wave frequencies. In this article, we propose using the covariance matrix of a large ensemble of representative targets to form a diagonalizing basis in which the transformed scene voxels are uncorrelated. In this basis, we introduce a method of image reconstruction, Covariance Likelihood based Regularization (CLR), where transformed voxels, with low likelihood according to the ensemble statistics, are penalized. We also discuss another method, Thresholded Eigenbasis (TE), which involves thresholding the eigenvalues of the covariance matrix and reconstructing the transformed scene voxels in a lower dimensional approximate basis. We use these techniques to reconstruct images from simulations of measurements made using a W-band (75 - 110 GHz) imaging system, where the linear imaging matrix is carefully designed based on vector electromagnetics and realistic hardware. Based on these reconstruction results, we discuss the opportunities and challenges for these methods, including scenarios where TE provides improved reconstruction speed and CLR provides improved accuracy.

We present a framework for adapting a large pretrained latent diffusion model to very high-resolution Synthetic Aperture Radar (SAR) image generation. The approach enables controllable synthesis of rare or unseen scenes beyond the training set. Rather than training a task-specific small model from scratch, we adapt an open-source text-to-image foundation model to the SAR modality, using its semantic prior to align prompts with SAR imaging physics (side-looking geometry, slant-range projection, and coherent speckle with heavy-tailed statistics). Using a 100k-image SAR dataset at 40 cm resolution, we compare full fine-tuning and parameter-efficient Low-Rank Adaptation (LoRA) across the UNet diffusion backbone, the Variational Autoencoder (VAE), and the Text Encoders. Evaluation combines (i) statistical distances to real SAR amplitude distributions, (ii) GLCM-based textural similarity, (iii) semantic alignment with a SAR-specialized CLIP model, and (iv) SAR-specific analyses, including layover–shadow geometry on ∼ 150 buildings and Point Spread Function (PSF) fidelity via spectral main-lobe widths. Our results show that a hybrid strategy — full U-Net tuning with LoRA on the text encoders and a learned token embedding — best preserves SAR geometry and texture while maintaining prompt fidelity reducing the KL divergence. The framework supports text-based control and multimodal conditioning (e.g., segmentation maps, TerraSAR-X, or optical guidance), opening new paths for large-scale SAR scene data augmentation and unseen scenario simulation in Earth observation.

The interpretation of Synthetic Aperture Radar (SAR) images of dynamic ocean scene is extremely complex, owing to the comprehensive mechanisms of ocean SAR imaging. Along-track Interferometric SAR (AT-InSAR) technique provides us a convenient tool to cope with these problems. But the lack of in-situ data of AT-InSAR is one of the most restricted factors for the further study of this technique. Simulation tools, if appropriately based on sound models, can provide extremely fruitful results to improve the comprehension of the physics and to interpret the SAR images. This paper outlines a spaceborne AT-InSAR raw signal simulator for dynamic ocean scene in time domain. Firstly, we adopt the fractal model to construct the large scale wave of the ocean scene, and then the scene is divided into facets (of approximately half the size of a resolution cell) and each facet is given a dynamic behaviour under certain constraint. Secondly, the RCS corresponding to each frame of the dynamic sea surface is calculated with Kirchhoff Approximation method. At last, the spaceborne AT-InSAR raw signal simulation flow, considering the actual procedure of real satellite moving in the obit, is established. In the dynamic ocean scene raw signal simulation flow, the scene of ocean surface and corresponding RCS was updated at each azimuth slow time. In this procedure, the raw signal can reflect the system error, and it will be quite helpful for the SAR system design. And the simulator is also validated by the accordance of the simulation results and the theoretical results. We adopted the MPI (Message Passing Interface) and parallel computing techniques to implement the fast simulation.

Optical imaging usually suffers from aberrations that are induced by various structures when imaging biological samples. Usually aberrations degrade the imaging system performances by broadening the point spread function (PSF). Unexpectedly we show that in spatially incoherent interferometry like full-filed optical coherence tomography (FFOCT), the system PSF width is almost insensitive to aberrations. Instead of considering the PSF of a classical imaging system such as a microscope, we specifically pay attention to the system PSF of interferometric imaging systems for which an undistorted wavefront from a reference beam interferes with the distorted wavefront of the object beam. By comparing the cases of scanning OCT with spatially coherent illumination, wide-field OCT with spatially coherent illumination and FFOCT with spatially incoherent illumination, we found that in FFOCT with spatially incoherent illumination the system PSF width is almost independent of the aberrations and only its amplitude varies. This is demonstrated by theoretical analysis as well as numerical calculations for different aberrations, and confirmed by experiments with a FFOCT system. It is the first time to the best of our knowledge that such specific merit of incoherent illumination in FFOCT has been demonstrated. Based on this, the signal level is used as metric in our adaptive optics FFOCT system for retinal imaging. Only the main aberrations (defocus and astigmatism) that are dominating in eye are corrected to improve the signal to noise ratio and the high order aberrations are skipped. This would increase the correction speed thus reducing the imaging time.

The 60GHz band is promising for applications such as high-speed short-range wireless personal-area network (WPAN), real-time video streaming at rates of several-Gbps, automotive radar, and mm-Wave imaging, since it provides a large amount of bandwidth that can freely (i.e. without a license) be used worldwide. However, transceivers at 60GHz pose several additional challenges over microwave transceivers. In addition to the circuit design challenges of implementing high performance 60GHz RF circuits in mainstream CMOS technology, the path loss at 60GHz is significantly higher than at microwave frequencies because of the smaller size of isotropic antennas. This can be overcome by using phased array technology. This thesis studies the new concepts and design techniques that can be used for 60GHz phased array systems. It starts with an overview of various applications at mm-wave frequencies, such as multi-Gbps radio at 60GHz, automotive radar and millimeter-wave imaging. System considerations of mm-wave receivers and transmitters are discussed, followed by the selection of a CMOS technology to implement millimeter-wave (60GHz) systems. The link budget of a 60GHz WPAN is analyzed, which leads to the introduction of phased array techniques to improve system performance. Different phased array architectures are studied and compared. The system requirements of phase shifters are discussed. Several types of conventional RF phase shifters are reviewed. A 60GHz 4-bit passive phase shifter is designed and implemented in a 65nm CMOS technology. Measurement results are presented and compared to published prior art. A 60GHz 4-bit active phase shifter is designed and integrated with low noise amplifier and combiner for a phased array receiver. This is implemented in a 65nm CMOS technology, and the measurement results are presented. The design of a 60GHz 4-bit active phase shifter and its integration with power amplifier is also presented for a phased array transmitter. This is implemented in a 65nm CMOS technology. The measurement results are also presented and compared to reported prior art. The integration of a 60GHz CMOS amplifier and an antenna in a printed circuit-board (PCB) package is investigated. Experimental results are presented and discussed.

In the modern tactical imaging environment, new computational imaging (CI) systems and algorithms are being used to improve speed and accuracy for detection tasks. Therefore, a measurement technique is needed to predict the performance of complex non-shift invariant EO/IR imaging systems, including CI systems. Detection performance of traditional imaging systems can be modeled using current system metrics and measurements such as Modulation Transfer Function (MTF), Signal to Noise (SNR), and instantaneous Field of View (iFOV). In this correspondence, we propose a technique to experimentally measure a detection sensitivity metric for non-traditional CI systems. The detection sensitivity metric predicts the upper bound of linear algorithm performance though evaluation of a matched filter. The experimental results are compared with theoretical expected values though the Night Vision Integrated Performance Model (NV-IPM). Additionally, we demonstrate the experimental results for a variety of imaging systems (IR, visible, and color), target sizes and orientations, as well as SNR values. Our results demonstrate how this detection sensitivity metric can be measured to provide additional insight into the final system performance.

Diagnostics are crucial to the operation of all particle accelerator facilities. They provide a means of optimising beam parameters and monitoring beam quality for users. However, for the next generation of accelerators, operating in extreme parameter spaces, most current beam diagnostics will fail; either through material failure or lack of resolution. This thesis presents three new diagnostic concepts to address this imminent diagnostic need. These novel diagnostics are supported by a simulation-based algorithm, successfully benchmarked throughout this work. All work has been conducted by the Author unless otherwise stated and referenced. The first technique measures sub-micron transverse beam profiles using optical transition radiation (OTR). Simulations were used to design a new imaging system, which was then installed at KEK (Japan). OTR images were then collected for a range of beam sizes. Comparisons were then made with simulations to successfully achieve sub-micron beam size measurements. Validation of these results was found in the agreement across different imaging systems for fixed beam parameters, providing a self-consistency check for the algorithm in lieu of alternate corroboration. The next diagnostic is a bunch length monitor for ∼<100 fs particle bunches; based on imaging the spatial distribution of coherent transition radiation (CTR) in the THz regime. Simulations demonstrated a relationship between image width and intensity, with bunch length. A THz imaging system was then installed at MAX IV (Sweden). CTR images were taken for a range of compressions. Maximum compression was successfully identified by monitoring relative changes in the properties of the images. Bunch lengths were then acquired from these images through comparison with simulations. Finally, the optical performance of a Digital Micro-mirror Device (DMD) was assessed. Image resolution produced by a DMD was quantified by measuring the Point Spread Function (PSF) of an imaging system, which was capable of remotely replacing a DMD with a planar mirror. The PSFs were measured using the sub-micron focus of a laser. Results show no measurable alteration to a PSF with the introduction of a DMD. This provides a foundation for the development of future diagnostics centred around this device. This thesis provides several validated proof-of-principle experimental results and supporting simulations. Future work has been identified for each topic, only possible following the results presented here, which will provide an array of novel next generation diagnostics.

A new compressed sensing (CS) image reconstruction method is proposed for high‐resolution wideband three‐dimensional synthetic aperture radar (SAR) imaging systems. In contrast to existing CS SAR methods that employ only a forward SAR transform in pre‐ or post‐processing, the proposed method employs both forward SAR and reverse SAR (R‐SAR) transforms in each CS iteration to improve the quality of reconstructed images. This study proposes a simple and elegant truncation repair method to combat the truncation error and utilises non‐uniform fast Fourier transform to reduce the SAR and R‐SAR transform errors, thereby ensuring the convergence of the CS algorithm and improving the quality of the reconstructed images. The proposed CS SAR method is applied to microwave and millimeter wave imaging systems for non‐destructive evaluation of materials embedded in stratified media. Three different specimens under test are measured by conventional uniform sampling and by random under‐sampling with 20% or 30% spatial points of the uniform sampling. The reconstructed images show that, albeit having much less measurement points, the proposed CS method achieves better image quality and lower background artefacts than the images reconstructed from the fully‐sampled uniform measurements.

Ice shelves are the fringes of ice sheets, found in both Polar regions (Antarctica and Greenland). Thickness changing patterns of these ice shelves have direct consequence to sea level rising. This paper introduces an ice-penetrating phase-sensitive FMCW radar system that is capable of measuring the depth of ice shelves to mm precision within 2 km. The signal processing technique for achieving this high precision range is described along with trial results verifying the accuracy. The radar had also been operated in Larsen North ice shelf in Antarctica in 2011-12 Austral summer for cross-sectional imaging. The processed results of the data obtained from that expedition are illustrated in this paper. The data processing for cross-sectional imaging of Larsen North ice shelf has been done by using both phased array and synthetic aperture radar (SAR) processing methods.

Timely information on lake ice cover conditions is critical in support of commercial shipping, winter-road transportation, and winter leisure activities such as snowmobiling and ice fishing. Monitoring of lake ice extent and ice phenology (i.e. dates associated with freeze-up/break-up and ice duration) is also valuable for improving numerical weather prediction (NWP) and for climate monitoring. The availability of free synthetic aperture radar (SAR) data from the European Space Agency’s Sentinel-1 A/B constellation provides an unprecedented opportunity to develop operational algorithms for lake ice cover mapping at a temporal frequency not available until now from SAR missions (ca. every 1–5 days depending on latitude). For NWP, rapid and accurate mapping of ice cover and open water areas in lake-rich regions is required. However, the classification of SAR imagery using traditional machine learning (ML) approaches is challenging due to the large data volume generated by imaging systems such as Sentinel-1 SAR as well as the complexity of radar signatures as a function of sensor and target characteristics. For lakes specifically, radar backscatter can vary greatly with incidence angle, polarization and surface properties (e.g. calm and wind-roughed open water, new thin ice, surface roughness due to the presence of pressure ridges, ice type, melting of ice and on-ice snow). To address the challenge of large-scale lake ice mapping, we investigate the GPU-boosted deep neural network approach that is efficient at handling big complex data. In this paper, we design a novel maximum a posterior (MAP) approach combining a convolutional neural network (CNN) and conditional random field (CRF) for better addressing the challenges of operational lake ice mapping. The proposed approach is tested on 17 Sentinel-1 dual-polarization (VV and VH) SAR images, where eight are used for training and validation and nine used for out-of-sample testing. To identify the optimal network architecture, an independent validation set is used to evaluate the performance of a series of six CNNs with increasing model complexity. The best model overcomes the noise effect in Sentinel-1 SAR imagery and the lake ice signature ambiguity issue; it achieves average classification accuracies of 97.10% and 97.14% for open water and ice, respectively, on the validation set. Moreover, the best model outperforms the last-ranked model by about 2% in terms of mean overall accuracy (OA), demonstrating the improvement and importance of model selection. The use of CRF can consistently improve the CNN by reducing the artefacts and noise effect in ice maps, outperforming the CNN model when used alone by about 3% in terms of mean OA on the validation set. The proposed CNN-CRF approach also achieves high accuracy on the nine test scenes, achieving a mean OA of 93.10%, demonstrating strong generalization capability that is important for SAR lake ice mapping.

The point spread function (PSF) of imaging system with coded mask is generally acquired by practical measure- ment with calibration light source. As the thermal radiation of coded masks are relatively severe than it is in visible imaging systems, which buries the modulation effects of the mask pattern, it is difficult to estimate and evaluate the performance of mask pattern from measured results. To tackle this problem, a model for infrared imaging systems with masks is presented in this paper. The model is composed with two functional components, the coded mask imaging with ideal focused lenses and the imperfection imaging with practical lenses. Ignoring the thermal radiation, the systems PSF can then be represented by a convolution of the diffraction pattern of mask with the PSF of practical lenses. To evaluate performances of different mask patterns, a set of criterion are designed according to different imaging and recovery methods. Furthermore, imaging results with inclined plane waves are analyzed to achieve the variation of PSF within the view field. The influence of mask cell size is also analyzed to control the diffraction pattern. Numerical results show that mask pattern for direct imaging systems should have more random structures, while more periodic structures are needed in system with image reconstruction. By adjusting the combination of random and periodic arrangement, desired diffraction pattern can be achieved.

Additional monitoring equipment is commonly used in astronomical imaging. This electro-optical system usually complements the main telescope during acquisition of astronomical phenomena or supports its operation e.g. evaluating the weather conditions. Typically it is a wide-field imaging system, which consists of a digital camera equipped with fish-eye lens. The wide-field imaging system cannot be considered as a space-invariant because of space-variant nature of its input lens. In our previous research efforts we have focused on measurement and analysis of images obtained from the subsidiary all-sky monitor WILLIAM (WIde-field aLL-sky Images Analyzing Monitoring system). Space-variant part of this imaging system consists of input lens with 180 fi angle of view in horizontal and 154 fi in vertical direction. For a precise astronomical measurement over the entire field of view, it is very important to know how the optical aberrations affect characteristics of the imaging system, especially its PSF (Point Spread Function). Two methods were used for characterization of the space-variant PSF, i.e. measurement in the optical laboratory and estimation using acquired images and Zernike polynomials. Analysis of results obtained using these two methods is presented in the paper. Accuracy of astronomical measurements is also discussed while considering the space-variant PSF of the system.

This paper investigates the use of fractional Fourier transform (FrFT)-based Orthogonal Frequency Division Multiplexing for Synthetic Aperture Radar (SAR) imaging for single point target and extended target. The geometry of synthetic aperture radar using FrFT-based OFDM is presented with the developing algorithm to process the raw data to reconstruct the image of the target in the range and in the Azimuth direction. In the simulation results, single point target is assumed to be at the center of swath width to show the performance of this approach as well as the point spread function in the range and Azimuth direction. Extended target with the shape of tank is used to show the capability of this method in resolving extended target images.