Direction Finding With Mutually Orthogonal Antennas

Estimating the direction-of-arrival of incident electromagnetic plane waves (a.k.a. direction finding or DF) has typically been accomplished in the past using arrays of spatially separated antennas. The spatial separation produces a delay in each antenna\u27s measured voltage due to the finite propagation time as the wave strikes each antenna in succession. In this thesis, we approach the problem differently by using three antennas that have been oriented in orthogonal directions but are co-located at the origin of a coordinate system. Being co-located, this mutually orthogonal arrangement of antennas cannot detect the propagation phase delay and must rely solely on the polarization properties of the incident waves. Using the vector effective height concept, three algorithms are formulated. The first algorithm estimates the direction-of-arrival by computing a vector that is perpendicular to the locus of the instantaneous electric field vector. The second and third algorithms are based on the well-known maximum likelihood and MUSIC algorithms. Simulation results show that each algorithm can estimate the direction-of-arrival with a root-mean-squared error within 1° or less when the incident wave is circularly polarized, the antennas are small compared to wavelength, and the signal-to-noise ratio is above 20dB

Mutual Coupling in Phased Arrays: A Review

The mutual coupling between antenna elements affects the antenna parameters like terminal impedances, reflection coefficients and hence the antenna array performance in terms of radiation characteristics, output signal-to-interference noise ratio (SINR), and radar cross section (RCS). This coupling effect is also known to directly or indirectly influence the steady state and transient response, the resolution capability, interference rejection, and direction-of-arrival (DOA) estimation competence of the array. Researchers have proposed several techniques and designs for optimal performance of phased array in a given signal environment, counteracting the coupling effect. This paper presents a comprehensive review of the methods that model and mitigate the mutual coupling effect for different types of arrays. The parameters that get affected due to the presence of coupling thereby degrading the array performance are discussed. The techniques for optimization of the antenna characteristics in the presence of coupling are also included

A review of closed-form Cramér-Rao Bounds for DOA estimation in the presence of Gaussian noise under a unified framework

The Cramér-Rao Bound (CRB) for direction of arrival (DOA) estimation has been extensively studied over the past four decades, with a plethora of CRB expressions reported for various parametric models. In the literature, there are different methods to derive a closed-form CRB expression, but many derivations tend to involve intricate matrix manipulations which appear difficult to understand. Starting from the Slepian-Bangs formula and following the simplest derivation approach, this paper reviews a number of closed-form Gaussian CRB expressions for the DOA parameter under a unified framework, based on which all the specific CRB presentations can be derived concisely. The results cover three scenarios: narrowband complex circular signals, narrowband complex noncircular signals, and wideband signals. Three signal models are considered: the deterministic model, the stochastic Gaussian model, and the stochastic Gaussian model with the a priori knowledge that the sources are spatially uncorrelated. Moreover, three Gaussian noise models distinguished by the structure of the noise covariance matrix are concerned: spatially uncorrelated noise with unknown either identical or distinct variances at different sensors, and arbitrary unknown noise. In each scenario, a unified framework for the DOA-related block of the deterministic/stochastic CRB is developed, which encompasses one class of closed-form deterministic CRB expressions and two classes of stochastic ones under the three noise models. Comparisons among different CRBs across classes and scenarios are presented, yielding a series of equalities and inequalities which reflect the benchmark for the estimation efficiency under various situations. Furthermore, validity of all CRB expressions are examined, with some specific results for linear arrays provided, leading to several upper bounds on the number of resolvable Gaussian sources in the underdetermined case

Crossed-dipole arrays for asynchronous DS-CDMA systems

Published versio

Direction of Arrival Estimation for Radio Positioning: a Hardware Implementation Perspective

Nowadays multiple antenna wireless systems have gained considerable attention due to their capability to increase performance. Advances in theory have introduced several new schemes that rely on multiple antennas and aim to increase data rate, diversity gain, or to provide multiuser capabilities, beamforming and direction finding (DF) features. In this respect, it has been shown that a multiple antenna receiver can be potentially used to perform radio localization by using the direction of arrival (DoA) estimation technique. In this field, the literature is extensive and gathers the results of almost four decades of research activities. Among the most cited techniques that have been developed, we find the so called high-resolution algorithms, such as multiple signal classification (MUSIC), or estimation of signal parameters via rotational invariance (ESPRIT). Theoretical analysis as well as simulation results have demonstrated their excellent performance to the point that they are usually considered as reference for the comparison with other algorithms. However, such a performance is not necessarily obtained in a real system due to the presence of non idealities. These can be divided into two categories: the impairments due to the antenna array, and the impairments due to the multiple radio frequency (RF) and acquisition front-ends (FEs). The former are strongly influenced by the manufacturing accuracy and, depending on the required DoA resolution, have to be taken into account. Several works address these issues in the literature. The multiple FE non idealities, instead, are usually not considered in the DoA estimation literature, even if they can have a detrimental effect on the performance. This has motivated the research work in this thesis that addresses the problem of DoA estimation from a practical implementation perspective, emphasizing the impact of the hardware impairments on the final performance. This work is substantiated by measurements done on a state-of-the-art hardware platform that have pointed out the presence of non idealities such as DC offsets, phase noise (PN), carrier frequency offsets (CFOs), and phase offsets (POs) among receivers. Particularly, the hardware platform will be herein described and examined to understand what non idealities can affect the DoA estimation performance. This analysis will bring to identify which features a DF system should have to reach certain performance. Another important issue is the number of antenna elements. In fact, it is usually limited by practical considerations, such as size, costs, and also complexity. However, the most cited DoA estimation algorithms need a high number of antenna elements, and this does not yield them suitable to be implemented in a real system. Motivated by this consideration, the final part of this work will describe a novel DoA estimation algorithm that can be used when multipath propagation occurs. This algorithm does not need a high number of antenna elements to be implemented, and it shows good performance despite its low implementation/computational complexity

Super Nested Arrays: Sparse arrays with Less Mutual Coupling than Nested Arrays

In array processing, mutual coupling between sensors has an adverse effect on the estimation of parameters (e.g., DOA). Sparse arrays, such as nested arrays, coprime arrays, and minimum redundancy arrays (MRAs), have reduced mutual coupling compared to uniform linear arrays (ULAs). With N denoting the number of sensors, these sparse arrays offer O(N^2) freedoms for source estimation because their difference coarrays have O(N^2)-long ULA segments. These arrays have different shortcomings: coprime arrays have holes in the coarray, MRAs have no closed-form expressions, and nested arrays have relatively large mutual coupling. This paper introduces a new array called the super nested array, which has all the good properties of the nested array, and at the same time reduces mutual coupling significantly. For fixed N, the super nested array has the same physical aperture, and the same hole-free coarray as does the nested array. But the number of sensor pairs with separation λ/2 is significantly reduced. Many theoretical properties are proved and simulations are included to demonstrate the superior performance of these arrays