Alternating projections gridless covariance-based estimation for DOA

We present a gridless sparse iterative covariance-based estimation method based on alternating projections for direction-of-arrival (DOA) estimation. The gridless DOA estimation is formulated in the reconstruction of Toeplitz-structured low rank matrix, and is solved efficiently with alternating projections. The method improves resolution by achieving sparsity, deals with single-snapshot data and coherent arrivals, and, with co-prime arrays, estimates more DOAs than the number of sensors. We evaluate the proposed method using simulation results focusing on co-prime arrays.Comment: 5 pages, accepted by (ICASSP 2021) 2021 IEEE International Conference on Acoustics, Speech, and Signal Processin

Three more Decades in Array Signal Processing Research: An Optimization and Structure Exploitation Perspective

The signal processing community currently witnesses the emergence of sensor array processing and Direction-of-Arrival (DoA) estimation in various modern applications, such as automotive radar, mobile user and millimeter wave indoor localization, drone surveillance, as well as in new paradigms, such as joint sensing and communication in future wireless systems. This trend is further enhanced by technology leaps and availability of powerful and affordable multi-antenna hardware platforms. The history of advances in super resolution DoA estimation techniques is long, starting from the early parametric multi-source methods such as the computationally expensive maximum likelihood (ML) techniques to the early subspace-based techniques such as Pisarenko and MUSIC. Inspired by the seminal review paper Two Decades of Array Signal Processing Research: The Parametric Approach by Krim and Viberg published in the IEEE Signal Processing Magazine, we are looking back at another three decades in Array Signal Processing Research under the classical narrowband array processing model based on second order statistics. We revisit major trends in the field and retell the story of array signal processing from a modern optimization and structure exploitation perspective. In our overview, through prominent examples, we illustrate how different DoA estimation methods can be cast as optimization problems with side constraints originating from prior knowledge regarding the structure of the measurement system. Due to space limitations, our review of the DoA estimation research in the past three decades is by no means complete. For didactic reasons, we mainly focus on developments in the field that easily relate the traditional multi-source estimation criteria and choose simple illustrative examples.Comment: 16 pages, 8 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Multi-source parameter estimation and tracking using antenna arrays

This thesis is concerned with multi-source parameter estimation and tracking using antenna arrays in wireless communications. Various multi-source parameter estimation and tracking algorithms are presented and evaluated. Firstly, a novel multiple-input multiple-output (MIMO) communication system is proposed for multi-parameter channel estimation. A manifold extender is presented for increasing the degrees of freedom (DoF). The proposed approach utilises the extended manifold vectors together with superresolution subspace type algorithms, to achieve the estimation of delay, direction of departure (DOD) and direction of arrival (DOA) of all the paths of the desired user in the presence of multiple access interference (MAI). Secondly, the MIMO system is extended to a virtual-spatiotemporal system by incorporating the temporal domain of the system towards the objective of further increasing the degrees of freedom. In this system, a multi-parameter es- timation of delay, Doppler frequency, DOD and DOA of the desired user, and a beamformer that suppresses the MAI are presented, by utilising the proposed virtual-spatiotemporal manifold extender and the superresolution subspace type algorithms. Finally, for multi-source tracking, two tracking approaches are proposed based on an arrayed Extended Kalman Filter (arrayed-EKF) and an arrayed Unscented Kalman Filter (arrayed-UKF) using two type of antenna arrays: rigid array and flexible array. If the array is rigid, the proposed approaches employ a spatiotemporal state-space model and a manifold extender to track the source parameters, while if it is flexible the array locations are also tracked simultaneously. Throughout the thesis, computer simulation studies are presented to investigate and evaluate the performance of all the proposed algorithms.Open Acces

DOA Convergence of Unstructured Distributed Arrays with Time-varying and Space-varying Morphologies

This thesis mainly focuses on the research of the factors that influence the accuracy and efficiency of a UAV-based radio frequency (RF) and microwave data collection system. Swarming UAVs can be utilized to create the unstructured morphing antenna arrays that reduce aliasing and improve convergence in sub-space direction of arrival techniques. This thesis first reports on the ramifications of using unstructured antenna arrays based on sub-space techniques. This work evaluates the classical MUSIC algorithm and root-MUSIC algorithm, and Fourier domain root-MUSIC algorithm (FD Root-MUSIC). Compared to the MUSIC algorithm, the root-MUSIC algorithm avoids the search of spatial spectrum, reduces the computational complexity and improves the ability of real world applications. Then, this thesis comes up with the data model for the UAV swarming system. Based on the data model, this work examines the impact of UAV swarm density and heterogeneity on synthetic aperture DOA convergence. The synthetic aperture is derived from the displacement of distributed UAVs operating in a sparse volumetric swarm. Heterogeneity arises from the changing orientation of a UAV’s antenna and receiving pattern function as it swarms in the distributed cluster of UAVs. This alters the UAVs’ antenna pattern functions over time and alters the convergence and overall performance properties of vector-space direction of arrival techniques. This work evaluates the impact of the swarm density and orientation in this framework and studies the convergence and error using MUSIC algorithm. This work also discusses the impact of different type of errors introduced from UAV swarming. Furthermore, this thesis examines the DOA convergence performance of location-varying volumetric random array using MUSIC algorithm. Simulation and measurements for up to sixteen elements on a thirty-two-location test platform are provided and comparisons are made to benchmark their performance with theoretical expectations. MATLAB simulation indicates that the volumetric random arrays can be applied in a very noisy condition by increasing the iterations and multiplying the MUSIC spectrum and experimental observations demonstrate that the system accurately capture the azimuthal and elevation angles of the source. At last, this thesis investigates and designs the tunable FM band monopole and loop antennas to locate the FM broadcasting stations. The wavelength of the FM band is around three meters. This work uses lumped elements and meandering antenna structure technologies to reduce the antenna size and match the antenna. This work also uses the varactor diodes to tune the antenna. However, the antenna becomes electrically small and the antenna gain is so low that it cannot detect the FM signal from the local FM broadcasting stations

A Compact Formulation for the ℓ2,1\ell_{2,1} Mixed-Norm Minimization Problem

Parameter estimation from multiple measurement vectors (MMVs) is a fundamental problem in many signal processing applications, e.g., spectral analysis and direction-of- arrival estimation. Recently, this problem has been address using prior information in form of a jointly sparse signal structure. A prominent approach for exploiting joint sparsity considers mixed-norm minimization in which, however, the problem size grows with the number of measurements and the desired resolution, respectively. In this work we derive an equivalent, compact reformulation of the $\ell_{2,1}$ mixed-norm minimization problem which provides new insights on the relation between different existing approaches for jointly sparse signal reconstruction. The reformulation builds upon a compact parameterization, which models the row-norms of the sparse signal representation as parameters of interest, resulting in a significant reduction of the MMV problem size. Given the sparse vector of row-norms, the jointly sparse signal can be computed from the MMVs in closed form. For the special case of uniform linear sampling, we present an extension of the compact formulation for gridless parameter estimation by means of semidefinite programming. Furthermore, we derive in this case from our compact problem formulation the exact equivalence between the $\ell_{2,1}$ mixed-norm minimization and the atomic-norm minimization. Additionally, for the case of irregular sampling or a large number of samples, we present a low complexity, grid-based implementation based on the coordinate descent method