High resolution source localization in near field sensor arrays by MVDR technique

Research over the last decade has led to technological advances in high frequency active and passive detection technology and signal processing. An emerging application area is the standoff detection of concealed objects such as weapons and explosives using penetrating electromagnetic radiation such as terahertz waves (THz). Here sensor arrays are employed in the near field to image the concealed objects. A new approach is investigated to improve upon methods such as Fourier inversion and sum and delay beamforming. A method based on the Minimum Variance Distortionless Response (MVDR) filter technique is developed to localize source points in the electric field coming from a subject. To pinpoint near field sources with precision, this MVDR routine calculates filter responses along a plane that has direction of arrival angle and range axes. To understand its limitations, this new method is tested for angular resolution in various directions of arrival, ranges, and SNR levels. The results show that this technique has potential to accurately detect closely spaced point sources when only a few sensors are used to collect measurements

Autonomous Radar-based Gait Monitoring System

Features related to gait are fundamental metrics of human motion [1]. Human gait has been shown to be a valuable and feasible clinical marker to determine the risk of physical and mental functional decline [2], [3]. Technologies that detect changes in people’s gait patterns, especially older adults, could support the detection, evaluation, and monitoring of parameters related to changes in mobility, cognition, and frailty. Gait assessment has the potential to be leveraged as a clinical measurement as it is not limited to a specific health care discipline and is a consistent and sensitive test [4]. A wireless technology that uses electromagnetic waves (i.e., radar) to continually measure gait parameters at home or in a hospital without a clinician’s participation has been proposed as a suitable solution [3], [5]. This approach is based on the interaction between electromagnetic waves with humans and how their bodies impact the surrounding and scattered wireless signals. Since this approach uses wireless waves, people do not need to wear or carry a device on their bodies. Additionally, an electromagnetic wave wireless sensor has no privacy issues because there is no video-based camera. This thesis presents the design and testing of a radar-based contactless system that can monitor people’s gait patterns and recognize their activities in a range of indoor environments frequently and accurately. In this thesis, the use of commercially available radars for gait monitoring is investigated, which offers opportunities to implement unobtrusive and contactless gait monitoring and activity recognition. A novel fast and easy-to-implement gait extraction algorithm that enables an individual’s spatiotemporal gait parameter extraction at each gait cycle using a single FMCW (Frequency Modulated Continuous Wave) radar is proposed. The proposed system detects changes in gait that may be the signs of changes in mobility, cognition, and frailty, particularly for older adults in individual’s homes, retirement homes and long-term care facilities retirement homes. One of the straightforward applications for gait monitoring using radars is in corridors and hallways, which are commonly available in most residential homes, retirement, and long-term care homes. However, walls in the hallway have a strong “clutter” impact, creating multipath due to the wide beam of commercially available radar antennas. The multipath reflections could result in an inaccurate gait measurement because gait extraction algorithms employ the assumption that the maximum reflected signals come from the torso of the walking person (rather than indirect reflections or multipath) [6]. To address the challenges of hallway gait monitoring, two approaches were used: (1) a novel signal processing method and (2) modifying the radar antenna using a hyperbolic lens. For the first approach, a novel algorithm based on radar signal processing, unsupervised learning, and a subject detection, association and tracking method is proposed. This proposed algorithm could be paired with any type of multiple-input multiple-output (MIMO) or single-input multiple-output (SIMO) FMCW radar to capture human gait in a highly cluttered environment without needing radar antenna alteration. The algorithm functionality was validated by capturing spatiotemporal gait values (e.g., speed, step points, step time, step length, and step count) of people walking in a hallway. The preliminary results demonstrate the promising potential of the algorithm to accurately monitor gait in hallways, which increases opportunities for its applications in institutional and home environments. For the second approach, an in-package hyperbola-based lens antenna was designed that can be integrated with a radar module package empowered by the fast and easy-to-implement gait extraction method. The system functionality was successfully validated by capturing the spatiotemporal gait values of people walking in a hallway filled with metallic cabinets. The results achieved in this work pave the way to explore the use of stand-alone radar-based sensors in long hallways for day-to-day long-term monitoring of gait parameters of older adults or other populations. The possibility of the coexistence of multiple walking subjects is high, especially in long-term care facilities where other people, including older adults, might need assistance during walking. GaitRite and wearables are not able to assess multiple people’s gait at the same time using only one device [7], [8]. In this thesis, a novel radar-based algorithm is proposed that is capable of tracking multiple people or extracting walking speed of a participant with the coexistence of other people. To address the problem of tracking and monitoring multiple walking people in a cluttered environment, a novel iterative framework based on unsupervised learning and advanced signal processing was developed and tested to analyze the reflected radio signals and extract walking movements and trajectories in a hallway environment. Advanced algorithms were developed to remove multipath effects or ghosts created due to the interaction between walking subjects and stationary objects, to identify and separate reflected signals of two participants walking at a close distance, and to track multiple subjects over time. This method allows the extraction of walking speed in multiple closely-spaced subjects simultaneously, which is distinct from previous approaches where the speed of only one subject was obtained. The proposed multiple-people gait monitoring was assessed with 22 participants who participated in a bedrest (BR) study conducted at McGill University Health Centre (MUHC). The system functionality also was assessed for in-home applications. In this regard, a cloud-based system is proposed for non-contact, real-time recognition and monitoring of physical activities and walking periods within a domestic environment. The proposed system employs standalone Internet of Things (IoT)-based millimeter wave radar devices and deep learning models to enable autonomous, free-living activity recognition and gait analysis. Range-Doppler maps generated from a dataset of real-life in-home activities are used to train deep learning models. The performance of several deep learning models was evaluated based on accuracy and prediction time, with the gated recurrent network (GRU) model selected for real-time deployment due to its balance of speed and accuracy compared to 2D Convolutional Neural Network Long Short-Term Memory (2D-CNNLSTM) and Long Short-Term Memory (LSTM) models. In addition to recognizing and differentiating various activities and walking periods, the system also records the subject’s activity level over time, washroom use frequency, sleep/sedentary/active/out-of-home durations, current state, and gait parameters. Importantly, the system maintains privacy by not requiring the subject to wear or carry any additional devices

Space-Time Parameter Estimation in Radar Array Processing

This thesis is about estimating parameters using an array of spatially distributed sensors. The material is presented in the context of radar array processing, but the analysis could be of interest in a wide range of applications such as communications, sonar, radio astronomy, seismology, and medical diagnosis. The main theme of the thesis is to analyze the fundamental limitations on estimation performance in sensor array signal processing. To this end, lower bounds on the estimation accuracy as well as the performance of the maximum likelihood (ML) and weighted least-squares (WLS) estimators are studied. The focus in the first part of the thesis is on asymptotic analyses. It deals with the problem of estimating the directions of arrival (DOAs) and Doppler frequencies with a sensor array. This problem can also be viewed as a two-dimensional (2-D) frequency estimation problem. The ML and WLS estimators for this problem amount to multidimensional, highly non-linear optimization problems which would be expensive to solve in real-time in a radar system. Therefore, simplifications of this problem are of great interest. It is shown in this thesis that, under some circumstances, the 2-D problem decouples into 1-D problems. This means a dramatic reduction in computational complexity with insignificant loss of accuracy. The second part contains a performance analysis of the ML DOA estimator under conditions of low signal-to-noise ratio (SNR) and a small number of data samples. It is well known that the ML estimator exhibits a threshold effect, i.e. a rapid deterioration of estimation accuracy below a certain SNR. This effect is caused by outliers and is not captured by standard analysis tools. In this thesis, approximations to the mean square estimation error and probability of outlier are derived that can be used to predict the threshold region performance of the ML estimator with high accuracy. Moreover, these approximations alleviate the need for time-consuming computer simulations when evaluating the ML performance

Characterising Spatial and Temporal Ionospheric Variability with a Network of Oblique Angle-of-arrival and Doppler Ionosondes

Ionospheric variability exists on a broad range of scales, and routinely impacts skywave propagation modes of high frequency radio waves, to the detriment of radar and communication systems. In order to better understand the electron density structures associated with such variability at mid-latitudes, a network of oblique angle-of-arrival (AoA) and Doppler ionosondes were installed in central and northern Australia as part of the ELOISE campaign in 2015. This thesis analyses observations from the ELOISE AoA ionosondes, with a focus on characterising the influence of medium- to large- scale gradients and signatures of travelling ionospheric disturbances (TIDs). Following an overview of the experiment, the design and calibration of the new ionosonde system is described. With multi-channel receivers connected to each element of two twin-arm arrays, a total of eleven AoA paths of between 900 and 2700 km were collected, including nine with interleaved Doppler measurements using a special channel scattering function (CSF) capability. On-board signal processing was developed to perform real-time clear channel evaluation and CSF scheduling, and generate the AoA ionograms and delay-Doppler images with fitted electron density profiles. In further offline analysis, peak detection and mode classification was carried out, to support reflection point mapping and tilt estimation. Significant testing and validation of the new ionosonde before and after the experiment revealed AoA uncertainties on the scale of 0.2–0.5° in bearing and 0.4–0.9° in elevation. Having identified a low-elevation bias, models of tropospheric refraction and antenna mutual coupling effects were considered as possible correction strategies, but ultimately an empirical approach based on aggregated ionospheric returns was implemented. Small-scale (intra-dwell) ionospheric variability also has the potential to compromise results, through unresolved multi-mode mixing, and this has been investigated using a combination of spatial and temporal variability metrics derived from the CSF data. The analysis of large quantities of F2 peak data shows persistent diurnal patterns in the oblique AoA observables that are also well-captured by a conventional data-assimilative ionospheric model, even without the benefit of AoA and Doppler inputs. Furthermore, Doppler measurements are reproduced remarkably well using just the midpoint fitted profiles. A statistical study has quantified the level of consistency between observations and model, to provide greater confidence in the results. Many of the geophysical features can be interpreted as ionospheric gradients, as evident in the tilt estimates, and horizontally moving structures such as TIDs, using a form of Doppler-based drift analysis. While signatures of TIDs vary considerably, two simple wave-like perturbation models have been evaluated to help classify quasi-periodic behaviour in the AoA observations, as well as understand the directional filtering effect imposed by the path geometry. In some cases, a set of TID parameters can be determined by eye, but in others automatic parameter inversion techniques may be more viable. Two such techniques were implemented but results using both real and synthetic data demonstrated some significant limitations. Finally, attempts to relate TID signatures across multiple paths shows promise, but there still appears to be a strong dependence on path geometry that is difficult to eliminate.Thesis (Ph.D.) -- University of Adelaide, School of Physical Sciences, 202

Towards a Common Software/Hardware Methodology for Future Advanced Driver Assistance Systems

The European research project DESERVE (DEvelopment platform for Safe and Efficient dRiVE, 2012-2015) had the aim of designing and developing a platform tool to cope with the continuously increasing complexity and the simultaneous need to reduce cost for future embedded Advanced Driver Assistance Systems (ADAS). For this purpose, the DESERVE platform profits from cross-domain software reuse, standardization of automotive software component interfaces, and easy but safety-compliant integration of heterogeneous modules. This enables the development of a new generation of ADAS applications, which challengingly combine different functions, sensors, actuators, hardware platforms, and Human Machine Interfaces (HMI). This book presents the different results of the DESERVE project concerning the ADAS development platform, test case functions, and validation and evaluation of different approaches. The reader is invited to substantiate the content of this book with the deliverables published during the DESERVE project. Technical topics discussed in this book include:Modern ADAS development platforms;Design space exploration;Driving modelling;Video-based and Radar-based ADAS functions;HMI for ADAS;Vehicle-hardware-in-the-loop validation system