Digital Signal Processing for Optical Communications and Coherent LiDAR

Internet data traffic within data centre, access and metro networks is experiencing unprecedented growth driven by many data-intensive applications. Significant efforts have been devoted to the design and implementation of low-complexity digital signal processing (DSP) algorithms that are suitable for these short-reach optical links. In this thesis, a novel low-complexity frequency-domain (FD) multiple-input multiple-output (MIMO) equaliser with momentum-based gradient descent algorithm is proposed, capable of mitigating both static and dynamic impairments arising from the optical fibre. The proposed frequency-domain equaliser (FDE) also improves the robustness of the adaptive equaliser against feedback latencies which is the main disadvantage of FD adaptive equalisers under rapid channel variations. The development and maturity of optical fibre communication techniques over the past few decades have also been beneficial to many other fields, especially coherent light detection and ranging (LiDAR) techniques. Many applications of coherent LiDAR are also cost-sensitive, e.g., autonomous vehicles (AVs). Therefore, in this thesis, a low-cost and low-complexity single-photodiode-based coherent LiDAR system is investigated. The receiver sensitivity performance of this receiver architecture is assessed through both simulations and experiments, using two ranging waveforms known as double-sideband (DSB) amplitude-modulated chirp signal and single-sideband (SSB) frequency-modulated continuous-wave (FMCW) signals. Besides, the impact of laser phase noise on the ranging precision when operating within and beyond the laser coherence length is studied. Achievable ranging precision beyond the laser coherence length is quantified

An overview of lidar imaging systems for autonomous vehicles

Lidar imaging systems are one of the hottest topics in the optronics industry. The need to sense the surroundings of every autonomous vehicle has pushed forward a race dedicated to deciding the final solution to be implemented. However, the diversity of state-of-the-art approaches to the solution brings a large uncertainty on the decision of the dominant final solution. Furthermore, the performance data of each approach often arise from different manufacturers and developers, which usually have some interest in the dispute. Within this paper, we intend to overcome the situation by providing an introductory, neutral overview of the technology linked to lidar imaging systems for autonomous vehicles, and its current state of development. We start with the main single-point measurement principles utilized, which then are combined with different imaging strategies, also described in the paper. An overview of the features of the light sources and photodetectors specific to lidar imaging systems most frequently used in practice is also presented. Finally, a brief section on pending issues for lidar development in autonomous vehicles has been included, in order to present some of the problems which still need to be solved before implementation may be considered as final. The reader is provided with a detailed bibliography containing both relevant books and state-of-the-art papers for further progress in the subject.Peer ReviewedPostprint (published version

One-Bit Algorithm Considerations for Sparse PMCW Radar

Phase Modulated Continuous Wave (PMCW) radar an emerging technology for autonomous cars. It is more flexible than the current frequency modulated systems, offering better detection resolution, interference mitigation, and future development opportunities. The issue preventing PMCW adoption is the need for high sample-rate analog to digital converters (ADCs). Due to device limits, a large increase in cost and power consumption occurs for every added resolution bit for a given sampling rate. This thesis explores radar detection techniques for few-bit and 1-bit ADC measurements. 1-bit quantization typically results in poor amplitude estimation, which can limit detections if the target signals are weak. Time Varying quantization Thresholds (TVTs) are a way to preserve that amplitude information. An existing few-bit Fast Iterative Shrinkage Thresholding Algorithm (FISTA) was adapted to use 1-bit TVT quantization. Three test scenarios compared the original FISTA using 1 and 2-bit quantization to the TVT approach. Tests included widely spaced targets, adjacent targets, and high dynamic range targets. Performance metrics included normalized mean squared error (NMSE) of target amplitude estimation and Receiver operating characteristic (ROC) curves for detection accuracy. Results showed the TVT implementation operated over the widest range of SNR values, had the lowest amplitude estimate NMSE at high SNR, and comparable NMSE with 2-bit FISTA at low SNR. There was an $84-93\%$ reduction in NMSE compared to 1-bit FISTA without TVTs. Few-bit FISTA had the best detection rates at specific SNR values, but was more sensitive to noise. AUC values averaged across the full SNR range for TVT FISTA were the most robust, measuring $13-46\%$ higher than 1-bit FISTA and $48-74\%$ higher than 2-bit FISTA. Advisor: Andrew Harm

Millimetre-wave radar development for high resolution detection

Automotive technology today is focusing on autonomous vehicle development. The sensors for these systems include radars due to their robustness against adverse weather conditions such as rain, fog, ash or snow. In this constant search for advancement, high resolution systems play a central role in target detection and avoidance. In this PhD project, these methods have been researched and engineered to leverage the best radar resolution for collision avoidance systems. The first part of this thesis will focus on the existing systems consisting of the state-of-the-art at the time of writing and explain what makes a high resolution radar and how it can cover the whole field of view. The second part will focus on how a non-uniform sparse radar system was simulated, developed and benchmarked for improved radar performance up to 40% better than conventional designs. The third part will focus on signal processing techniques and how these methods have achieved high resolution and detection: large virtual aperture array using Multiple Input Multiple Output (MIMO) systems, beampattern multiplication to improve side-lobe levels and compressive sensing. Also, the substrate-integrated waveguide (SIW) antennas which have been fabricated provide a bandwidth of 1.5GHz for the transmitter and 2GHz at the receiver. This has resulted in a range resolution of 10 cm. The four part of this thesis presents the measurements which have been carried out at the facilities within Heriot-Watt University and also at Netherlands Organisation for Applied Scientific Research (TNO). The results were better than expected since a two transmitter four receiver system was able to detect targets which have been separated at 2.2◦ in angle in the horizontal plane. Also, compressive sensing was used as a high resolution method for obtaining fine target detection and in combination with the multiplication method showed improved detection performance with a 20 dB side-lobe level suppression. The measurement results from the 6-months placements are presented and compared with the state-of the art, revealing that the developed radar is comparable in performance to high-grade automotive radars developed in the industry

MIMO-employed coherent photonic-radar (MIMO-Co-PHRAD) for detection and ranging

Recently, the photonics-radar technology comes out as an attractive candidate in the arena of smart autonomous transportation, surveillance, and navigation-related applications owing to provide wide-spectra to attain improved and precise radar-resolutions. On the other hand, microwave radars, due to limited bandwidth, are incapable of coping with the demands of next-generation radar technology. Moreover, the atmospheric fluctuations become more prominent at higher frequencies and affect the radar’s performance significantly. Subsequently, the authors develop a 2 × 2 multi-input multi-output (MIMO) employed linear frequency-modulated continuous-wave coherent photonic-radar system (MIMO-Co-PHRAD) using OptiSystem™ and MATLAB™ to attain a prolonged detection-range with an enhanced visibility. The developed MIMO-Co-PHRAD is investigated with heterodyne- and homodyne-detection approaches under weak-to-strong regimes of the atmospheric fluctuations like Rain and Fog. A comparison is also drawn for both the demonstrated MIMO-equipped laser-driven coherent photonic-radar systems. The performance of both the developed MIMO-Co-PHRAD systems is evaluated by measuring the intensity of reflected-echoes, signal-to-noise ratio, and range-Doppler patterns. A contrast with the single-input single-output coherent photonic-radar (SISO-Co-PHRAD) is also established to validate the robustness of the demonstrated MIMO-Co-PHRAD

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

Enabling Technology in Optical Fiber Communications: From Device, System to Networking

This book explores the enabling technology in optical fiber communications. It focuses on the state-of-the-art advances from fundamental theories, devices, and subsystems to networking applications as well as future perspectives of optical fiber communications. The topics cover include integrated photonics, fiber optics, fiber and free-space optical communications, and optical networking

A Study of Types of Sensors used in Remote Sensing

Of late, the science of Remote Sensing has been gaining a lot of interest and attention due to its wide variety of applications. Remotely sensed data can be used in various fields such as medicine, agriculture, engineering, weather forecasting, military tactics, disaster management etc. only to name a few. This article presents a study of the two categories of sensors namely optical and microwave which are used for remotely sensing the occurrence of disasters such as earthquakes, floods, landslides, avalanches, tropical cyclones and suspicious movements. The remotely sensed data acquired either through satellites or through ground based- synthetic aperture radar systems could be used to avert or mitigate a disaster or to perform a post-disaster analysis

Respiration and Activity Detection based on Passive Radio Sensing in Home Environments

The pervasive deployment of connected devices in modern society has significantly changed the nature of the wireless landscape, especially in the license free industrial, scientific and medical (ISM) bands. This paper introduces a deep learning enabled passive radio sensing method that can monitor human respiration and daily activities through leveraging unplanned and ever-present wireless bursts in the ISM frequency band, and can be employed as an additional data input within healthcare informatics. Wireless connected biomedical sensors (Medical Things) rely on coding and modulating of the sensor data onto wireless (radio) bursts which comply with specific physical layer standards like 802.11, 802.15.1 or 802.15.4. The increasing use of these unplanned connected sensors has led to a pell-mell of radio bursts which limit the capacity and robustness of communication channels to deliver data, whilst also increasing inter-system interference. This paper presents a novel methodology to disentangle the chaotic bursts in congested radio environments in order to provide healthcare informatics. The radio bursts are treated as pseudo noise waveforms which eliminate the requirement to extract embedded information through signal demodulation or decoding. Instead, we leverage the phase and frequency components of these radio bursts in conjunction with cross ambiguity function (CAF) processing and a Deep Transfer Network (DTN). We use 2.4GHz 802.11 (WiFi) signals to demonstrate experimentally the capability of this technique for human respiration detection (including through-the-wall), and classifying everyday but complex human motions such as standing, sitting and falling