A Short-Range FMCW Radar-Based Approach for Multi-Target Human-Vehicle Detection

In this article, a new microwave-radar-based technique for short-range detection and classification of multiple human and vehicle targets crossing a monitored area is proposed. This approach, which can find applications in both security and infrastructure surveillance, relies upon the processing of the scattered-field data acquired by low-cost off-The-shelf components, i.e., a 24 GHz frequency-modulated continuous wave (FMCW) radar module and a Raspberry Pi mini-PC. The developed method is based on an ad hoc processing chain to accomplish the automatic target recognition (ATR) task, which consists of blocks performing clutter and leakage removal with an infinite impulse response (IIR) filter, clustering with a density-based spatial clustering of applications with noise (DBSCAN) approach, tracking using a Benedict-Bordner $alpha$-$eta$ filter, features extraction, and finally classification of targets by means of a $k$-nearest neighbor ( $k$-NN) algorithm. The approach is validated in real experimental scenarios, showing its capabilities in correctly detecting multiple targets belonging to different classes (i.e., pedestrians, cars, motorcycles, and trucks)

Through-The-Wall Detection Using Ultra Wide Band Frequency Modulated Interrupted Continuous Wave Signals

Through-The-Wall-Detection (TTWD) techniques can improve the situational awareness of police and soldiers, and support first responders in search and rescue operations. A variety of systems for TTWD based on different waveforms have been developed and presented in the literature, e.g. radar systems based on pulses, noise or pseudo-noise waveforms, and frequency modulated continuous wave (FMCW) or stepped frequency continuous wave (SFCW) waveforms. Ultra wide band signals are normally used as they provide suitable resolution to discriminate different targets. A common problem for active radar systems for TTWD is the strong backscattered signal from the air-wall interface. This undesired signal can overshadow the reflections from actual targets, especially those with low radar cross section like human beings, and limit the dynamic range at the receiver, which could be saturated and blocked. Although several techniques have been developed to address this problem, frequency modulated interrupted continuous wave (FMICW) waveforms represent an interesting further approach to wall removal, which can be used as an alternative technique or combined with the existing ones. FMICW waveforms have been used in the past for ionospheric and ocean sensing radar systems, but their application to the wall removal problem in TTWD scenarios is novel. The validation of the effectiveness of the proposed FMICW waveforms as wall removal technique is therefore the primary objective of this thesis, focusing on comparing simulated and experimental results using normal FMCW waveforms and using the proposed FMICW waveforms. Initially, numerical simulations of realistic scenarios for TTWD have been run and FMICW waveforms have been successfully tested for different materials and internal structure of the wall separating the radar system and the targets. Then a radar system capable of generating FMICW waveforms has been designed and built to perform a measurement campaign in environments of the School of Engineering and Computing Sciences, Durham University. These tests aimed at the localization of stationary targets and at the detection of people behind walls. FMICW waveforms prove to be effective in removing/mitigating the undesired return caused by antenna cross-talk and wall reflections, thus enhancing the detection of targets

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

High-Dimensional Information Detection based on Correlation Imaging Theory

Radar is a device that uses electromagnetic(EM) waves to detect targets; it can measure the position parameters and motion parameters and extract target characteristics information by analyzing the reflected signal from the target. From the perspective of the radar theoretical basis of physics, the more than 70 years of development of radar are based on the EM field fluctuation theory of physics. Many theories have been developed towards one-dimensional signal processing. For example, a variety of threshold filtering have widely used as methods to resist interference during detection. The optimal state estimation describes the propagation process of the statistical characteristics of the target over time in the probability domain. Compressed sensing greatly improves the reconstructing efficiency of the sparse signal. These theories are one-dimensional information processing. The information obtained by them is a deterministic description of the EM field. The correlated imaging technique is from the high-order coherence property of the EM field, which uses the fluctuation characteristic of the EM field to realize non-local imaging. Correlated imaging radar, a combination of correlated imaging techniques and modern information theory, will provide a novel remote sensing detection and imaging method. More importantly, correlated imaging radar is a new research field. Therefore, a complete theoretical frame and application system should be urgently built up and improved. Based on the coherence theory of the EM field, the work in this thesis explores the method of determining the statistical characteristics of the EM field so that the high dimensional target information can be detected, including theoretical analysis, principle design, imaging modes, target detecting models, image reconstruction algorithms, the enhancement of visibility, and system design. The simulations and real experiments are set up to prove the theory's validity and the systems' feasibility

Design considerations for future geodesy missions and for space laser interferometry

[no abstract

Photodetectors

In this book some recent advances in development of photodetectors and photodetection systems for specific applications are included. In the first section of the book nine different types of photodetectors and their characteristics are presented. Next, some theoretical aspects and simulations are discussed. The last eight chapters are devoted to the development of photodetection systems for imaging, particle size analysis, transfers of time, measurement of vibrations, magnetic field, polarization of light, and particle energy. The book is addressed to students, engineers, and researchers working in the field of photonics and advanced technologies

Ground-based remote sensing of warm low-level stratified clouds - new perspectives and applications

Climate change and an increasing global population increase the pressure on the global community, in particular the Global North, to initiate drastic changes in mindset, lifestyle and resource management to sustain a habitable environment, to minimize natural hazards, and thereby, to protect those who are most vulnerable and least responsible for the current condition of our planet. Among others, the shift from fossil to renewable energy sources (renewables) plays a key role in transforming a high-carbon society into a zero-carbon one. The integration of renewables into existing energy systems or even the design of an energy system consisting of renewables only are still challenging tasks that require interdisciplinary research. Such research is conducted by the University of Cologne hosting the project Energy Transitions and Climate Change to support both specific and interdisciplinary research for investigating open questions and creating new ones related to renewable energies and climate change. Here, results of an interdisciplinary study are discussed investigating the uncertainty of predicted energy systems in Germany based on the analysis of Reanalysis data. Among others, this uncertainty depends on the accuracy of estimated solar energy from photovoltaic panels and is most sensitive to changes in direct solar radiation. Hence, to assesses the uncertainty of predicted energy systems based on reanalysis data, the accuracy of the latter itself, especially estimated solar radiation, must be characterized well. Uncertainties in direct solar radiation in reanalysis data depend to high extent on the prediction of clouds, especially those clouds that are abundant and have a high albedo at visible wavelengths such as stratocumulus clouds (Sc). Thus, understanding their formation and evolution constitutes an important topic for renewable energy applications. To evaluate models and parameterizations implemented into reanalysis, accurate observations of Sc are necessary, which is the main topic of this work: how accurately can we retrieve the liquid water content (LWC) of warm low-level stratified clouds, in particular Sc, using ground-based remote sensing? The three key publications of this cumulative thesis try to answer this question from different perspectives: Publication I evaluates the performance of a new W-band radar-radiometer (JOYRAD-94) that can be used to derive physical properties of clouds. It is shown, by comparing JOYRAD-94 to a co-located radar, that it is capable of measuring radar reflectivity at 94 GHz with an accuracy of about 0.5 dB. The comparison also revealed a new method to dealiase radar Doppler spectra using two co-located radars enabling cloud observations with both high vertical resolution and large unambiguous Doppler velocity. Additionally, JOYRAD-94 is equipped with a passive microwave radiometer (MWR) channel at 89 GHz enabling the retrieval of the liquid water path with an uncertainty of about 15 g/m² when the integrated water vapor is known with an accuracy of 2 kg/m² from an external source. Optimal beam matching between the radar and the radiometer of JOYRAD-94 is accomplished by receiving the active and passive signals over the same antenna. This is a novelty in ground-based remote sensing. The advantage of optimally matched beams for cloud remote sensing is discussed in Publication II that investigates how the accuracy of a commonly used LWC retrieval (henceforth StandFrisch), combining radar and MWR, changes when the instruments are displaced to each other, i.e. observe different cloud scenes. It is found that displacing the instruments by 10 m increases the relative retrieval uncertainty of retrieved LWC by 10 % in the entire profile. At 100 m displacement, the relative error reaches 30 %. Moreover, it is shown that studying LWC at cloud edges requires optimally matched beams, i.e. a displacement by 10 m does already yield unreasonable results. Publication III assess the accuracy of StandFrisch for various compositions of Sc. StandFrisch is capable of retrieving LWC in non-drizzling Sc. However, once drizzle is present, StandFrisch does not obtain reasonable estimates of LWC. Publication III provides a modification of StandFrisch, the ModFrisch, that allows retrieving LWC in both drizzling and non-drizzling Sc with an accuracy of 20 %. The findings of the three publications increase the accuracy of a commonly used LWC retrieval technique for warm low-level stratified clouds and characterize the retrieval's uncertainties. Therefore this thesis makes an important contribution to better understand micro-physical processes in Sc, which drive cloud formation and evolution. Moreover, more accurate LWC profiles can help to improve the evaluation of models and their parameterizations, which are, for example, implemented in reanalysis data. Well characterized models and their data are inevitable for various applications such as weather and climate predictions, as well as estimating future energy systems

A Millimeter-Wave Radar Microfabrication Technique and Its Application in Detection of Concealed Objects.

Millimeter-wave (MMW) radars are envisioned for a number of safety and security applications such as collision-avoidance, navigation and standoff target detection in all weather conditions. This work focuses on two MMW radar applications: (1) phenomenology of radar backscatter from the human body for the purpose of identification and detection of concealed objects on the body (2) microfabrication of advanced MMW radar to achieve compact and low-cost systems for autonomous navigation. In MMW band, the wavelength (1 mm ~ 1 cm) is long enough to allow signal penetration through cluttered atmosphere and clothing with little attenuation and short enough to allow for fabrication of small-size radar systems. Hence, this frequency band is well suited for the design of small sensors capable of obstacle detection and navigation in heavily cluttered environment and detecting hidden objects carried by individuals. For this purpose, a novel non-imaging approach is developed for distinction of walking human body and concealed carried object using polarimetric backscatter Doppler spectrum. This approach does not need radiometric calibration of the radar and preparation of the subject for radar interrogation. It is shown that a coherent polarimetric radar at W-band (95 GHz) or higher frequencies can be used for standoff detection of concealed carried objects. Motivated by these results, the thesis also includes an investigation on developing a technology for compact MMW radar systems. A micromachined, high-resolution, compact and low-power imaging MMW radar operating at 240 GHz intended for obstacle detection in complex environment is introduced. A frequency scanning antenna array micromachined from three layers of stacked silicon wafers is designed to provide 20 beamwidth in azimuth and 80 in elevation with azimuthal beam scanning range of ± 250. The frequency beam scanning is enabled by a meander rectangular waveguide with a slot array on its broad wall to feed linear microstrip patch antennas microfabricated on a suspended Parylene membrane. This technique offers high fabrication precision; provide easy fabrication and integration with active devices. The performances of the passive components of the radar system are verified using a WR-3 S-parameter and a near-field measurement systems.Ph.D.Electrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91484/1/mvahid_1.pd