Localization Performance of 1-Bit Passive Radars in NB-IoT Applications

Location-based services form an important use-case in emerging narrowband Internet-of-Things (NB-IoT) networks. Critical to this offering is an accurate estimation of the location without overlaying the network with additional active sensors. The massive number of devices, low power requirement, and low bandwidths restrict the sampling rates of NB-IoT receivers. In this paper, we propose a novel low-complexity approach for NB-IoT target delay estimation in cases where one-bit analog-to-digital-converters (ADCs) are employed to sample the received radar signal instead of high-resolution ADCs. This problem has potential applications in the design of inexpensive NB-IoT radar and sensing devices. We formulate the target estimation as a multivariate fractional optimization problem and solve it via Lasserre's semi-definite program relaxation. Numerical experiments suggest feasibility of the proposed approach yielding high localization accuracy with a very low number of 1-bit samples

Optimum Design for Sparse FDA-MIMO Automotive Radar

Automotive radars usually employ multiple-input multiple-output (MIMO) antenna arrays to achieve high azimuthal resolution with fewer elements than a phased array. Despite this advantage, hardware costs and desired radar size limits the usage of more antennas in the array. Similar trade-off is encountered while attempting to achieve high range resolution which is limited by the signal bandwidth. However, nowadays given the demand for spectrum from communications services, wide bandwidth is not readily available. To address these issues, we propose a sparse variant of Frequency Diverse Array MIMO (FDA-MIMO) radar which enjoys the benefits of both FDA and MIMO techniques, including fewer elements, decoupling, and efficient joint estimation of target parameters. We then employ the Cram\'{e}r-Rao bound for angle and range estimation as a performance metric to design the optimal antenna placement and carrier frequency offsets for the transmit waveforms. Numerical experiments suggest that the performance of sparse FDA-MIMO radar is very close to the conventional FDA-MIMO despite 50\% reduction in the bandwidth and antenna elements

A Statistically Efficient Estimator for Co-array Based DoA Estimation

Co-array-based Direction of Arrival (DoA) estimation using Sparse linear arrays (SLAs) has recently gained considerable interest in array processing due to the attractive capability of providing enhanced degrees of freedom. Although a variety of estimators have been suggested in the literature for co-array-based DoA estimation, none of them are statistically efficient. This work introduces a novel Weighted Least Squares (WLS) estimator for the co-array-based DoA estimation employing the covariance fitting method. Then, an optimal weighting is given so that the asymptotic performance of the proposed WLS estimator coincides with the Cram\'{e}r-Rao Bound (CRB), thereby ensuring statistical efficiency of resulting WLS estimator. This implies that the proposed WLS estimator has significantly better performance compared to existing methods in the literature. Numerical simulations are provided to corroborate the asymptotic statistical efficiency and the improved performance of the proposed estimator

CONSISTENT LEAST SQUARES ESTIMATOR FOR CO-ARRAY-BASED DOA ESTIMATION

Sparse linear arrays (SLAs), such as nested and co-prime arrays, have the attractive capability of providing enhanced degrees of freedom by exploiting the co-array model. Accordingly, co-array-based Direction of Arrivals (DoAs) estimation has recently gained considerable interest in array processing. The literature has suggested applying MUSIC on an augmented sample covariance matrix for co-array-based DoAs estimation. In this paper, we propose a Least Squares (LS) estimator for co-array-based DoAs estimation employing the covariance fitting method as an alternative to MUSIC. We show that the proposed LS estimator provides consistent estimates of DoAs of identifiable sources for SLAs. Additionally, an analytical expression for the large sample performance of the proposed estimator is derived. Numerical results illustrate the finite sample behavior in relation to the derived analytical expression. Moreover, the performance of the proposed LS estimator is compared to the co-array-based MUSIC

Optic Nerve Head Optical Coherence Tomography Angiography Findings after Coronavirus Disease

Purpose: To quantify the microvasculature density of the optic nerve head (ONH) using optical coherence tomography angiography (OCTA) analysis in patients recovered from Coronavirus Disease 2019 (COVID-19). Methods: In a comparative cross-sectional, observational study, patients recovered from COVID- 19 whose initial diagnosis was confirmed by an rRT-PCR of a nasopharyngeal sample were included in this study. OCTA of ONH was performed in included patients and normal controls. Vascular density (VD) of the all vessels (AV) and small vessels (SV) inside the disc and radial peripapillary capillary (RPC) network density was measured in COVID-19 recovered patients and compared with similar parameters in an age-matched group of normal controls. Results: Twenty-five COVID-19 patients and twenty-two age-matched normal controls were enrolled in the study and one eye per participant was evaluated. The mean whole image SV VD in the COVID-19 group (49.31 ± 1.93) was not statistically significantly different from that in the control group (49.94 ±. 2.22; P = 0.308). A decrease in RPC VD was found in all AV and SV VD measured, which became statistically significant in whole peripapillary SV VD, peripapillary inferior nasal SV VD, peripapillary inferior temporal SV VD, peripapillary superior nasal SV VD, and grid-based AV VD inferior sector (P < 0.05). Inside disc SV VD in the COVID-19 group (49.43 ± 4.96) was higher than in the control group (45.46 ± 6.22) which was statistically significant (P = 0.021). Conclusion: Unremarkable decrease was found in ONH microvasculature in patients who had recovered from COVID-19. These patients may be at risk of ONH vascular complications. Increase in inner disc SV VD may be an indicator of ONH hyperemia and edema

Physics-Based Cognitive Radar Modeling and Parameter Estimation

We consider the problem of channel response estimation in cognitive fully adaptive radar (CoFAR). We show that this problem can be expressed as a constrained channel estimation problem exploiting the similarity between the channel impulse responses (CIRs) of the adjacent channels. We develop a constrained CIR estimation (CCIRE) algorithm enhancing estimation performance compared to the unconstrained CIR estimation where the similarity between the CIRs of the adjacent channels is not employed. Further, we we derive the Cram\'{e}r-Rao bound (CRB) for the CCIRE and show the optimality of the proposed CCIRE through comparing its performance with the derived CRB

Direction of Arrival Estimation and Localization Exploiting Sparse and One-Bit Sampling

Data acquisition is a necessary first step in digital signal processing applications such as radar, wireless communications and array processing. Traditionally, this process is performed by uniformly sampling signals at a frequency above the Nyquist rate and converting the resulting samples into digital numeric values through high-resolution amplitude quantization. While the traditional approach to data acquisition is straightforward and extremely well-proven, it may be either impractical or impossible in many modern applications due to the existing fundamental trade-off between sampling rate, amplitude quantization precision, implementation costs, and usage of physical resources, e.g. bandwidth and power consumption. Motivated by this fact, system designers have recently proposed exploiting sparse and few-bit quantized sampling instead of the traditional way of data acquisition in order to reduce implementation costs and usage of physical resources in such applications. However, before transition from the tradition data acquisition method to the sparsely sampled and few-bit quntized data acquisition approach, a study on the feasibility of retrieving information from sparsely sampled and few-bit quantized data is first required to be conducted. This study should specifically seek to find the answers to the following fundamental questions: 1-Is the problem of retrieving the information of interest from sparsely sampled and few-bit quantized data an identifiable problem? If so, what are the identifiability conditions? 2-Under the identifiability conditions: what are the fundamental performance bounds for the problem of retrieving the information of interest from sparsely sampled and few-bit quantized data? and how close are these performance bounds to those of retrieving the same information from the data acquired through the traditional approach? 3-Does there exist any computationally efficient algorithm for retrieving the information of interest from sparsely sampled and few-bit quantized data capable of achieving the corresponding performance bounds? My thesis focuses on finding the answers to the above fundamental questions for the problems of Direction of Arrival (DoA) estimation and localization, which are of the most important information retrieval problems in radar, wireless communication and array processing. Inthis regard, the first part of this thesis focuses on DoA estimation using Sparse Linear Arrays (SLAs). I consider this problem under three plausible scenarios from quantization perspective. Firstly, I 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, I introduce a novel estimator for the DoA estimation from SLA data employing the Weighted Least Squares (WLS) method. I 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, I 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, I study the performance limits of DoA estimation from one-bit SLA measurements through analyzing the identifiability conditions and the corresponding CRB. I also propose a new algorithm for estimating DoAs from one-bit quantized data. I 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. I develop a novel optimization-based framework for estimating DoAs from low-resolution multi-bit measurements. It is shown 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, I consider the problem of passive target localization 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. I 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, I propose a novel algorithm for target localization with the aggregated one-bit range vector. My 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, I 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