Penetrating 3-D Imaging at 4- and 25-m Range Using a Submillimeter-Wave Radar

We show experimentally that a high-resolution imaging radar operating at 576–605 GHz is capable of detecting weapons concealed by clothing at standoff ranges of 4–25 m. We also demonstrate the critical advantage of 3-D image reconstruction for visualizing hidden objects using active-illumination coherent terahertz imaging. The present system can image a torso with <1 cm resolution at 4 m standoff in about five minutes. Greater standoff distances and much higher frame rates should be achievable by capitalizing on the bandwidth, output power, and compactness of solid state Schottky-diode based terahertz mixers and multiplied sources

Investigation of Non-coherent Discrete Target Range Estimation Techniques for High-precision Location

Ranging is an essential and crucial task for radar systems. How to solve the range-detection problem effectively and precisely is massively important. Meanwhile, unambiguity and high resolution are the points of interest as well. Coherent and non-coherent techniques can be applied to achieve range estimation, and both of them have advantages and disadvantages. Coherent estimates offer higher precision but are more vulnerable to noise and clutter and phase wrap errors, particularly in a complex or harsh environment, while the non-coherent approaches are simpler but provide lower precision. With the purpose of mitigating inaccuracy and perturbation in range estimation, miscellaneous techniques are employed to achieve optimally precise detection. Numerous elegant processing solutions stemming from non-coherent estimate are now introduced into the coherent realm, and vice versa. This thesis describes two non-coherent ranging estimate techniques with novel algorithms to mitigate the instinct deficit of non-coherent ranging approaches. One technique is based on peak detection and realised by Kth-order Polynomial Interpolation, while another is based on Z-transform and realised by Most-likelihood Chirp Z-transform. A two-stage approach for the fine ranging estimate is applied to the Discrete Fourier transform domain of both algorithms. An N-point Discrete Fourier transform is implemented to attain a coarse estimation; an accurate process around the point of interest determined in the first stage is conducted. For KPI technique, it interpolates around the peak of Discrete Fourier transform profiles of the chirp signal to achieve accurate interpolation and optimum precision. For Most-likelihood Chirp Z-transform technique, the Chirp Z-transform accurately implements the periodogram where only a narrow band spectrum is processed. Furthermore, the concept of most-likelihood estimator is introduced to combine with Chirp Z-transform to acquire better ranging performance. Cramer-Rao lower bound is presented to evaluate the performance of these two techniques from the perspective of statistical signal processing. Mathematical derivation, simulation modelling, theoretical analysis and experimental validation are conducted to assess technique performance. Further research will be pushed forward to algorithm optimisation and system development of a location system using non-coherent techniques and make a comparison to a coherent approach

Frequency Modulated Continuous Waveform Radar for Collision Prevention in Large Vehicles

The drivers of large vehicles can have very limited visibility, which contributes to poor situation awareness and an increased risk of collision with other agents. This thesis is focused on the development of reliable sensing for this close proximity problem in large vehicles operating in harsh environmental conditions. It emphasises the use of in-depth knowledge of a sensor’s physics and performance characteristics to develop effective mathematical models for use in different mapping algorithms. An analysis of the close proximity problem and the demands it poses on sensing technologies is presented. This guides the design and modelling process for a frequency modulated continuous waveform (FMCW) radar sensor for use in solving the close proximity problem. Radar offers better all-weather performance than other sensing modalities, but its measurement structure is more complex and often degraded by noise and clutter. The commonly used constant false alarm rate (CFAR) threshold approach performs poorly in applications with frequent extended targets and a short measurement vector, as is the case here. Therefore, a static detection threshold is calculated using measurements of clutter made using the radar, allowing clutter measurements to be filtered out in known environments. The detection threshold is used to develop a heuristic sensor model for occupancy grid mapping. This results in a more reliable representation of the environment than is achieved using the detection threshold alone. A Gaussian mixture extended Kalman probability hypothesis density filter (GM-EK-PHD) is implemented to allow mapping in dynamic environments using the FMCW radar. These methods are used to produce maps of the environment that can be displayed to the driver of a large vehicle to better avoid collisions. The concepts developed in this thesis are validated using simulated and real data from a low-cost 24GHz FMCW radar developed at the Australian Centre for Field Robotics at the University of Sydney

Radar Interference Mitigation for Automated Driving: Exploring Proactive Strategies

Autonomous driving relies on a variety of sensors, especially on radars, which have unique robustness under heavy rain/fog/snow and poor light conditions. With the rapid increase of the amount of radars used on modern vehicles, where most radars operate in the same frequency band, the risk of radar interference becomes a compelling issue. This article analyses automotive radar interference and proposes several new approaches, which combine industrial and academic expertise, toward the path of interference-free autonomous driving

High-resolution three-dimensional imaging radar

A three-dimensional imaging radar operating at high frequency e.g., 670 GHz, is disclosed. The active target illumination inherent in radar solves the problem of low signal power and narrow-band detection by using submillimeter heterodyne mixer receivers. A submillimeter imaging radar may use low phase-noise synthesizers and a fast chirper to generate a frequency-modulated continuous-wave (FMCW) waveform. Three-dimensional images are generated through range information derived for each pixel scanned over a target. A peak finding algorithm may be used in processing for each pixel to differentiate material layers of the target. Improved focusing is achieved through a compensation signal sampled from a point source calibration target and applied to received signals from active targets prior to FFT-based range compression to extract and display high-resolution target images. Such an imaging radar has particular application in detecting concealed weapons or contraband

AGV RAD: AGV positioning system for ports using microwave doppler radar

Automation and intelligence have become an inevitable trend in the development of container terminals. The AGV (Automated Guided Vehicle) positioning is a primary problem to build the automated ports. Although the existing Ultra-High Frequency(UHF) RFID technology has good measurement accuracy and stability in the port AGV positioning, the exposed magnetic tags are easy to damage under the common heavy load, and its construction and maintenance cost is unbearable to most ports. Among the candidate technologies for the AGV positioning, microwave Doppler radar has a strong penetrating ability, and can work well in a complex environment (day and night, foggy, rainy). Therefore, the microwave Doppler radar-based AGV positioning system has attracted a lot of attention. In this thesis, a test system using the above technique was established, together with a NI myRIO real-time Wi-Fi compatible computation platform. Several computation algorithms were implemented to extract the accurate values of range and velocity. Wavelet denoising with the adapted threshold function was considered to filter noise contained in radar signals. In the frequency domain analysis, FFT and Chirp-Z Transform (CZT) joint algorithm was proposed to suppress the influence of fence effects and also improves real-time performance. In addition, 2D-FFT is used to calculate velocity of AGV. According to the port-like environment, the suitable AGV positioning algorithm and communication method based on microwave Doppler radars and NI myRIO-1900s also be proposed. The effectiveness of the proposed system was experimentally tested and several results are included in this thesis.Automação e inteligência artifical tornaram-se uma tendência inevitável no desenvolvimento dos terminais dos contentores. O posicionamento do VAG (Veículo Autónomo Guiado) é um dos problemas principais para construir as portas automatizadas. Embora a tecnologia RFID de frequência ultra-alta (UHF) existente tenha uma boa precisão e estabilidade de medição no posicionamento VAG dos portos, as etiquetas magnéticas expostas são fáceis de danificar sob a comum carga pesada e o seu habitual custo de construção e manutenção é insuportável para a maioria das portos. Entre as tecnologias para o posicionamento VAG, o radar Doppler de microondas possui uma forte capacidade de penetração e pode funcionar bem em ambientes complexos (dia, noite, nevoeiro e chuva). Portanto, o sistema de posicionamento VAG baseado em radar Doppler de microondas atraiu muita atenção. Nesta tese, foi estabelecido um sistema de teste usando a técnica acima mencionada, juntamente com uma plataforma de computação em tempo real, NI myRIO compatível com Wi-Fi. Vários algoritmos de computação foram envolvidos para extrair os valores precisos de distancia e velocidade. O “denoising” de wavelets com a função de limiar adaptado foi utilizado para filtrar o ruído nos sinais de radar. Na análise do domínio da frequência, o algoritmo conjunto FFT e Chirp-Z Transform (CZT) foi proposto para suprimir a influência dos efeitos de resolução e também melhorar o desempenho em tempo real. Além disso, o algoritmo 2D-FFT é usado para calcular a velocidade do VAG. De acordo com o ambiente dos portos, o algoritmo de posicionamento VAG e o método de comunicação adequado baseados em radares Doppler de microondas e NI myRIO-1900s também serão propostos. A eficiência do sistema proposto foi testada experimentalmente e vários resultados estão descritos nesta dissertação

Fusion of Video and Multi-Waveform FMCW Radar for Traffic Surveillance

Modern frequency modulated continuous wave (FMCW) radar technology provides the ability to modify the system transmission frequency as a function of time, which in turn provides the ability to generate multiple output waveforms from a single radar unit. Current low-power multi-waveform FMCW radar techniques lack the ability to reliably associate measurements from the various waveform sections in the presence of multiple targets and multiple false detections within the field-of-view. Two approaches are developed here to address this problem. The first approach takes advantage of the relationships between the waveform segments to generate a weighting function for candidate combinations of measurements from the waveform sections. This weighting function is then used to choose the best candidate combinations to form polar-coordinate measurements. Simulations show that this approach provides a ten to twenty percent increase in the probability of correct association over the current approach while reducing the number of false alarms in generated in the process, but still fails to form a measurement if a detection form a waveform section is missing. The second approach models the multi-waveform FMCW radar as a set of independent sensors and uses distributed data fusion to fuse estimates from those individual sensors within a tracking structure. Tracking in this approach is performed directly with the raw frequency and angle measurements from the waveform segments. This removes the need for data association between the measurements from the individual waveform segments. A distributed data fusion model is used again to modify the radar tracking systems to include a video sensor to provide additional angular and identification information into the system. The combination of the radar and vision sensors, as an end result, provides an enhanced roadside tracking system

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