Long-Range Imaging Radar for Autonomous Navigation

This thesis describes the theoretical and practical implementation of a long-range high-resolution millimetre wave imaging radar system to aid with the navigation and guidance of both airborne and ground-based autonomous vehicles. To achieve true autonomy, a vehicle must be able to sense its environment, comprehensively, over a broad range of scales. Objects in the immediate vicinity of the vehicle must be classified at high resolution to ensure that the vehicle can traverse the terrain. At slightly longer ranges, individual features such as trees and low branches must be resolved to allow for short-range path planning. At long range, general terrain characteristics must be known so that the vehicle can plan around difficult or impassable obstructions. Finally, at the largest scale, the vehicle must be aware of the direction to its objective. In the past, short-range sensors based on radar and laser technology have been capable of producing high-resolution maps in the immediate vicinity of the vehicle extending out to a few hundred metres at most. For path planning, and navigation applications where a vehicle must traverse many kilometres of unstructured terrain, a sensor capable of imaging out to at least 3km is required to permit mid and long-range motion planning. This thesis addresses this need by describing the development a high-resolution interrupted frequency modulated continuous wave (FMICW) radar operating at 94GHz. The contributions of this thesis include a comprehensive analysis of both FMCW and FMICW processes leading to an effective implementation of a radar prototype which is capable of producing high-resolution reflectivity images of the ground at low grazing angles. A number of techniques are described that use these images and some a priori knowledge of the area, for both feature and image based navigation. It is shown that sub-pixel registration accuracies can be achieved to achieve navigation accuracies from a single image that are superior to those available from GPS. For a ground vehicle to traverse unknown terrain effectively, it must select an appropriate path from as long a range as possible. This thesis describes a technique to use the reflectivity maps generated by the radar to plan a path up to 3km long over rough terrain. It makes the assumption that any change in the reflectivity characteristics of the terrain being traversed should be avoided if possible, and so, uses a modified form of the gradient-descent algorithm to plan a path to achieve this. The millimetre wave radar described here will improve the performance of autonomous vehicles by extending the range of their high-resolution sensing capability by an order of magnitude to 3km. This will in turn enable significantly enhanced capability and wider future application for these systems

An analysis of chosen image formation algorithms for synthetic aperture radar with FMCW

The modelling of FMCW SAR systems, due to long signal duration time, commonly used start-stop approximation for pulsed radars causes errors in the image. Continuous motion of the radar platform results in additional range-azimuth couplings and range walk term that should be considered in processing of signal from this type of radar. The paper presents an analysis of the following algorithms: Time Domain Correlation (TDC), Range Doppler Algorithm (RDA), and Range Migration Algorithm (RMA). The comparison of the algorithms is based on theoretical estimation of their computation complexity and the quality of images obtained on the basis of real signals of FMCW SAR systems

Novel Hybrid-Learning Algorithms for Improved Millimeter-Wave Imaging Systems

Increasing attention is being paid to millimeter-wave (mmWave), 30 GHz to 300 GHz, and terahertz (THz), 300 GHz to 10 THz, sensing applications including security sensing, industrial packaging, medical imaging, and non-destructive testing. Traditional methods for perception and imaging are challenged by novel data-driven algorithms that offer improved resolution, localization, and detection rates. Over the past decade, deep learning technology has garnered substantial popularity, particularly in perception and computer vision applications. Whereas conventional signal processing techniques are more easily generalized to various applications, hybrid approaches where signal processing and learning-based algorithms are interleaved pose a promising compromise between performance and generalizability. Furthermore, such hybrid algorithms improve model training by leveraging the known characteristics of radio frequency (RF) waveforms, thus yielding more efficiently trained deep learning algorithms and offering higher performance than conventional methods. This dissertation introduces novel hybrid-learning algorithms for improved mmWave imaging systems applicable to a host of problems in perception and sensing. Various problem spaces are explored, including static and dynamic gesture classification; precise hand localization for human computer interaction; high-resolution near-field mmWave imaging using forward synthetic aperture radar (SAR); SAR under irregular scanning geometries; mmWave image super-resolution using deep neural network (DNN) and Vision Transformer (ViT) architectures; and data-level multiband radar fusion using a novel hybrid-learning architecture. Furthermore, we introduce several novel approaches for deep learning model training and dataset synthesis.Comment: PhD Dissertation Submitted to UTD ECE Departmen

Low-cost CW-LFM radar sensor at 100 GHz

This paper presents a W-band high-resolution radar sensor for short-range applications. Low-cost technologies have been properly selected in order to implement a versatile and easily scalable radar system. A large operational bandwidth of 9 GHz, required for obtaining high-range resolution, is attained by means of a frequency multiplication-based architecture. The system characterization to identify the performance-limiting stages and the subsequent design optimization are presented. The assessment of system performance for several representative applications has been carried out

Frequency-modulated continuous-wave synthetic-aperture radar: improvements in signal processing

With the advance of solid state devices, frequency-modulated continuous-wave (FMCW) designs have recently been used in synthetic-aperture radar (SAR) to decrease cost, size, weight and power consumption, making it deployable on smaller mobile plat-forms, including small (< 25 kg) unmanned aerial vehicle(s) (UAV). To foster its mobile uses, several SAR capabilities were studied: moving target indication (MTI) for increased situational awareness, bistatic operation, e.g. in UAV formation flights, for increased range, and signal processing algorithms for faster real-time performance. Most off-the-shelf SAR systems for small mobile platforms are commercial proprie-tary and/or military (ITAR, International Trades in Arms Regulations) restricted. As such, it necessitated the design and build of a prototype FMCW SAR system at the early stage to serve as a research tool. This enabled unrestricted hardware and software modifica-tions and experimentation. A model to analyze the triangularly modulated (TM) linear frequency modulated (LFM) waveform as one signal was established and used to develop a MTI algorithm which is effective for slow moving targets detection. Experimental field data collected by the prototyped FMCW SAR was then used to validate and demonstrate the effectiveness of the proposed MTI method. A bistatic FMCW SAR model was next introduced: Bistatic configuration is a poten-tial technique to overcome the power leakage problem in monostatic FMCW SAR. By mounting the transmitter and receiver on spatially separate mobile (UAV) platforms in formation deployment, the operation range of a bistatic FMCW SAR can be significantly improved. The proposed approximation algorithm established a signal model for bistatic FMCW SAR by using the Fresnel approximation. This model allows the existing signal processing algorithms to be used in bistatic FMCW SAR image generation without sig-nificant modification simplifying bistatic FMCW SAR signal processing. The proposed range migration algorithm is a versatile and efficient FMCW SAR sig-nal processing algorithm which requires less memory and computational load than the traditional RMA. This imaging algorithm can be employed for real-time image genera-tion by the FMCW SAR system on mobile platforms. Simulation results verified the pro-posed spectral model and experimental data demonstrated the effectiveness of the modi-fied RMA

Generalized continuous wave synthetic aperture radar for high resolution and wide swath remote sensing

© 2018 IEEE. A generalized continuous wave synthetic aperture radar (GCW-SAR) concept is proposed in this paper. By using full-duplex radio frontend and continuous wave signaling, the GCW-SAR system can overcome a number of limitations inherent within the existing SAR systems and achieve high-resolution and wide-swath remote sensing with low-power signal transmission. Unlike the conventional pulsed SAR and the frequency-modulated continuous-wave SAR, the GCW-SAR reconstructs a radar image by directly correlating the received 1-D raw data after self-interference cancellation with predetermined location-dependent reference signals. A fast imaging algorithm, called the piecewise constant Doppler (PCD) algorithm, is also proposed, which produces the radar image recursively in the azimuth direction without any intermediate step, such as range compression and migration compensation, as required by conventional algorithms. By removing the stop-and-go assumption or slow-time sampling in azimuth, the PCD algorithm not only achieves better imaging quality but also allows for more flexible waveform and system designs. Analyses and simulations show that the GCW-SAR tolerates significant self-interference and works well with a large selection of various system parameters. The work presented in this paper establishes a solid theoretical foundation for next-generation imaging radars

A portable 3D Imaging FMCW MIMO Radar Demonstrator with a 24x24 Antenna Array for Medium Range Applications

© 2018 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] Multiple-input multiple-output (MIMO) radars have been shown to improve target detection for surveillance applications thanks to their proven high-performance properties. In this paper, the design, implementation, and results of a complete 3-D imaging frequency-modulated continuous-wave MIMO radar demonstrator are presented. The radar sensor working frequency range spans between 16 and 17 GHz, and the proposed solution is based on a 24-transmitter and 24-receiver MIMO radar architecture, implemented by timedivision multiplexing of the transmit signals. A modular approach based on conventional low-cost printed circuit boards is used for the transmit and receive systems. Using digital beamforming algorithms and radar processing techniques on the received signals, a high-resolution 3-D sensing of the range, azimuth, and elevation can be calculated. With the current antenna configuration, an angular resolution of 2.9° can be reached. Furthermore, by taking advantage of the 1-GHz bandwidth of the system, a range resolution of 0.5 m is achieved. The radio-frequency front-end, digital system and radar signal processing units are here presented. The medium-range surveillance potential and the high-resolution capabilities of the MIMO radar are proved with results in the form of radar images captured from the field measurements.Ganis, A.; Miralles-Navarro, E.; Schoenlinner, B.; Prechtel, U.; Meusling, A.; Heller, C.; Spreng, T.... (2018). A portable 3D Imaging FMCW MIMO Radar Demonstrator with a 24x24 Antenna Array for Medium Range Applications. IEEE Transactions on Geoscience and Remote Sensing. 56(1):298-312. https://doi.org/10.1109/TGRS.2017.2746739S29831256

Image Reconstruction for Multistatic Stepped Frequency-Modulated Continuous Wave (FMCW) Ultrasound Imaging Systems With Reconfigurable Arrays

The standard architecture of a medical ultrasound transducer is a linear phased array of piezoelectric elements in a compact, hand-held form. Acoustic energy not directly reflected back towards the transducer elements during a transmit-receive cycle amounts to lost information for image reconstruction. To mitigate this loss, a large, flexible transducer array which conforms to contours of the subject's body would result in a greater effective aperture and an increase in received image data. However, in this reconfigurable array design, element distributions are irregular and an organized arrangement can no longer be assumed. Phased array architecture also has limited scalability potential for large 2D arrays. This research work investigates a multistatic, stepped-FMCW modality as an alternative to array phasing in order to accommodate the flexible and reconfigurable nature of an array. A space-time reconstruction algorithm was developed for the imaging system. We include ultrasound imaging experiments and describe a simulation method for quickly predicting imaging performance for any given target and array configuration. Lastly, we demonstrate two reconstruction techniques for improving image resolution. The first takes advantage of the statistical significance of pixel contributions prior to the final summation, and the second corrects data errors originating from the stepped-FMCW quadrature receiver