Please click on this link and join the online PhD defense.
Members of the defense committee:
- Prof. Dr. Marcus Völp, University of Luxembourg, Chairman
- Prof. Dr. Geert Leus, Delft University of Technology, The Netherlands, Deputy Chairman
- Prof. Dr. Björn Ottersten, University of Luxembourg, Supervisor
- Prof. Dr.Moeness Amin, Villanova University, Pennsylvania USA, Member
- Dr. Bhavani Shankar Mysore, University of Luxembourg, Member
Abstract:
In the recent years, in order to reduce implementation costs and physical resources usage, there has been a growing tendency to exploit sparse and one-bit sampling in applications such as array processing, radar and wireless communication. This strategy for collecting data necessitates devising new algorithms for information retrieval in such applications. This fact has motivated further research in areas such as Directional of Arrival (DoA) estimation and source/target localization in the recent years.
In this context, the first part of this thesis focuses on DoA estimation using Sparse Linear Arrays (SLAs). We consider this problem under three plausible scenarios from quantization perspective. Firstly, we assume that an SLA quantized the received signal to a large number of bits per samples such that the resulting quantization error can be neglected. Although the literature presents a variety of estimators under such circumstances, none of them are (asymptotically) statistically efficient. Motivated by this fact, we introduce a novel estimator for the DoA estimation from SLA data employing the Weighted Least Squares (WLS) method. We analytically show that the large sample performance of the proposed estimator coincides with the Cram'{e}r-Rao Bound (CRB), thereby ensuring its asymptotic statistical efficiency. Next, we study the problem of DoA estimation from one-bit SLA measurements. The analytical performance of DoA estimation from one-bit SLA measurements has not yet been studied in the literature and performance analysis in the literature has be limited to simulations studies. Therefore, we study the performance limits of DoA estimation from one-bit SLA measurements through analyzing the identifiability conditions and the corresponding CRB. We also propose a new algorithm for estimating DoAs from one-bit quantized data. We investigate the analytical performance of the proposed method through deriving a closed-form expression for the covariance matrix of its asymptotic distribution and show that it outperforms the existing algorithms in the literature. Finally, the problem of DoA estimation from low-resolution multi-bit SLA measurements, e.g. $2$ or $4$ bit per sample, is studied. We develop a novel optimization-based framework for estimating DoAs from low-resolution multi-bit measurements. It is show that increasing the sampling resolution to $2$ or $4$ bits per samples could significantly increase the DoA estimation performance compared to the one-bit sampling case while the power consumption and implementation costs are still much lower compared to the high-resolution sampling scenario.
In the second part of the thesis, the problem of target localization is addressed. Firstly, we consider the problem of passive target from one-bit data in the context of Narrowband Internet-of-Things (NB-IoT).
In the recently proposed narrowband IoT (NB-IoT) standard, which trades off bandwidth to gain wide area coverage, the location estimation is compounded by the low sampling rate receivers and limited-capacity links. We address both of these NB-IoT drawbacks by consider a limiting case where each node receiver employs one-bit analog-to-digital-converters and propose a novel low-complexity nodal delay estimation method. Then, to support the low-capacity links to the fusion center (FC), the range estimates obtained at individual sensors are converted to one-bit data. At the FC, we propose a novel algorithm for target localization with the aggregated one-bit range vector. Our overall one-bit framework not only complements the low NB-IoT bandwidth but also supports the design goal of inexpensive NB-IoT location sensing.
Secondly, in order to reduce bandwidth usage for performing high precision time of arrival-based localization, we developed a novel sparsity-aware target localization algorithm with application to automotive radars.
The thesis concludes with summarizing the main research findings and some remarks on future directions and open problems.