This thesis addresses the design and optimization of sparse non-uniform optical phased arrays (OPAs) for advanced automotive LiDAR systems. As autonomous driving technologies advance, the demand for high-resolution, reliable, and compact LiDAR systems has become increasingly critical. Traditional uniform OPAs, while effective, face limitations regarding power consumption. This work introduces an innovative approach to designing sparse non-uniform OPAs that achieve desired performance metrics essential for automotive applications, including beamwidth, field of view, and sidelobe levels, while minimizing element count and, consequently, energy consumption.

Through mathematical modelling and simulation, we formulate the problem of sparse OPA design as an optimization problem, leveraging techniques from compressive sensing to identify the most efficient element arrangements. We propose using the sparse array synthesis method to formulate the sparse OPA design problem, utilizing algorithms such as LASSO, thresholding, and iterative reweighted l1-norm minimization to achieve optimal sparse configurations. Our results demonstrate substantial improvements in effectiveness, offering a practical solution to the constraints posed by current LiDAR systems. This thesis contributes to the field by providing a comprehensive framework for the design of sparse non-uniform OPAs, highlighting the trade-offs and benefits of various design strategies. The findings advance our understanding of OPA design principles.

Indoor positioning using Bluetooth addressed a great concern. The properties of narrowband usage, low energy consumption and universality on devices attract numerous customers and promote researchers to investigate potential of Bluetooth in indoor positioning field. It has already supported Angle-of-Arrival (AoA) and Angle-of-Departure (AoD) in angle domain for indoor localization. In range domain, using Received Signal Strength (RSS) is practicable but it cannot provide enough resolution. It is needed to develop a technique that can improve resolution for range finding feature. The challenges of localization in indoor environments are mainly from the multipath propagation of signals. In this thesis, a subspace-based super-resolution algorithm for time delay dispersion estimate is developed. Range finding is realized by computing time-of-arrival (ToA) parameter of signal arriving at direct line-of-sight (DLoS). An essential procedure before the developed algorithm is applied is that to correctly separate subspace into signal space and noise space. Techniques for subspace separation are investigated in this thesis. In this thesis, we want to explore the potential of indoor localization using Bluetooth narrowband radios. To start with, a data model according to the property of the conducted measurement data is developed. The conducted measurement data is radio channel measurements based on channel sounding technique. Then the data model is developed as channel impulse response model and multipath signals are indicated by different time delays. Since an accurate covariance matrix of measurement data is required for super-resolution algorithm, smoothing techniques is employed. The smoothing techniques considered are forward smoothing technique and forward-backward smoothing technique. For the purpose of obtaining an accurate subspace separation, two techniques are investigated in this thesis, namely MDL criteria algorithm and the threshold method. in order to investigate the performance and reliability of those two techniques, experiments are taken out using different parameter values. Comparison is made between the results of these two techniques. Afterwards, subspace-based super-resolution algorithm is taken into consideration. In this thesis, the super-resolution algorithm implemented is MUSIC algorithm. The functionality of MUSIC algorithm on narrowband radios measurements is tested and evaluated firstly by simulation experiments, which demonstrates the practicability of applying MUSIC algorithm on narrowband radios measurements. Then experiments are extended to the measurement data that conducted from real indoor environments, for the purpose of indoor localization realization using narrowband radios.

In the next generation of Bluetooth standard, the Bluetooth SIG wants to incorporate multiple antenna systems into the Bluetooth Low Energy specification to enable direction-finding features. The features are aimed to improve the accuracy of off-the-shelf Asset Tracking Profile (ATP) and Indoor Positioning Service (IPS) including two modes – Angle-of-Arrival (AoA) mode and Angle-of-Departure (AoD) mode. In this thesis, we only focus on the AoA mode.

The new standard raises several challenges. First, the direction finding algorithm shall be derived sincethe standard gives only the framework. The algorithm shall cope with dense multipath effects in indoorenvironments and identify the angle of Line-of-Sight (LOS) component. Second, the new standard specifiesthe usage of an RF switch such that a single receiver can access multiple antennas. This mechanism reducesthe device cost and complexity but poses difficulties to the array processing. There are inevitably informationloss during antenna switching. It also raises requirements of channel stationarity and efficient compensationof CFO. Third, towards the system implementation, practical considerations that deviate the ideal datamodelshall be taken into account. These considerations include the effect of mutual coupling (MC), and the phase imbalance of the RF switch. During this project, these effects have been studied to obtain insight on theinfluence on algorithmperformance and compensation techniques.

In this thesis, we formulated the data model for a single receiver using a uniformlinear multiple antennasystem with an RF switch. The importance of CFO compensation, channel stationarity, and the color of noiseare addressed. A maximum likelihood (ML) based CFO estimation algorithm is proposed. Furthermore, we modeled the effect of mutual coupling and imbalance of switch. Next, we analyzed why the delay estimation is not feasible within the context of Bluetooth LE. We proposed two Line-of-Sight direction identification (LOS-Id) algorithms based on the power signature in the data covariance matrix, which are referred to as MUSIC LOS-Id and CLEAN-MUSIC LOS-Id. Further performance improvements are achieved by making use of the frequency hopping feature of Bluetooth. By aggregatingmore than one packets at different frequencies, the performance can be improved substantially. This technique is called the multi-tone technique, or packet aggregation (PA).

For evaluating the effectiveness of the proposed methods and models, a Bluetooth LE simulator is built. The performance verification is divided into two phases that differentiate themselves by the channel model. In the first phase, a simulated channel model, which is obtained by applying the ray tracer in an empty rectangular room, is used. The mutual coupling effect is simulated using the Antenna Toolbox in Matlab. The switch characteristics are verified by measurements using a Vector Network Analyzer (VNA). In the second phase, the real channel is measured with the VNA. Three campaigns of measurements are carried out with a 1x4, 1x8, and 2x4 antenna array respectively. Performance is evaluated by applying both channel models. The simulations reveal that the multipath effect is the dominant influencing factor of the performance in our indoor scenario, while the mutual coupling and the switch imbalance have little influence. The results also show that both proposed LOS-Id algorithms yield satisfying accuracy. However, we paid less attention to the CLEAN-MUSIC algorithm because of its complexity even though it indeed performs better than MUSIC LOS-Id in our simulated scenario. Finally, the usage of multi-tone technique improves the LOS-Id performance substantially. With an 8-element ULA and aggregating 8 tones, the MUSIC LOS-Id algorithm can achieve 10 degrees of RMSE for 90% of transmission positions with measured channels, and 3 degrees of RMSE for 50% of transmission positions.

In this thesis, we study ranging algorithms in an indoor environment using narrow-band industrial, scientific, and medical (ISM) radio bands at 2.4 GHz. Previously, a phase difference approach implemented for this problem. However, the distance estimation is rather inaccurate for indoor ranging, mainly due to multipath and noise. This thesis studies several direction of arrival (DOA) techniques such as matched filter (MF), minimum variance distortionless response (MVDR), and multiple signal classification (MUSIC) to reduce the impact of indoor multipath. Forward-backward smoothing and Akaike information criterion - minimum descriptive length (AIC-MDL) also proposed to diminish the multipath effect further and estimates the number of separable multipath in the channel. Besides, MUSIC-like is discussed to prevent incorrect estimation number of sources. We test the proposed algorithm under different channel parameter value, compensate the bias, and show the performance improvement as the absolute bias value reduced up an order of magnitude.

Nowadays, indoor ranging and localization have become necessary in daily life. Due to the multi-path propagation and noise in the indoor environment, phase domain ranging method using multi-frequency has been proposed which achieves accurate estimation of indoor target. However, as the indoor communication is usually carried on Bluetooth Low Energy (BLE) or Zigbee, high efficiency is indispensable in the face of limited bandwidth and measuring time. Thus, in this thesis, we aim to reduce the number of frequencies used in the ranging while keeping an acceptable estimation accuracy. We at first build the signal model and study the ambiguity range of the problem. Then based on the estimation theory and the concept of compressive sensing (CS) theory, we take the Cramer Rao Lower Bound (CRLB) and the matrix coherence as the criteria and select the optimal subset on given tone set. To test the performance of selected subset, we utilize gridless reconstruction algorithms, noiseless global matched filter (NL-GMF) and atomic norm minimization (ANM), to estimate target distance with the subset in both simulated data and real data and provide the mean square error (MSE), the estimation probability and the successful estimation probability in various estimation conditions.