Given a certain transmission frequency, Shannon spatial sampling limit de¯nes an upper bound for the antenna element spacing. Beyond this bound, the exceeded ambiguity avoids correct estimation of the signal parameters (i.e., array manifold crossing). This spacing limit is inversely proportional to the frequency of transmis- sion. Therefore, to meet a wider spectral support, the element spacing should be decreased. However, practical implementations of closely spaced elements result in a detrimental increase in electromagnetic mutual couplings among the sensors. Further- more, decreasing the spacing reduces the array angle resolution. In this dissertation, the problem of Direction of Arrival (DOA) estimation of broadband sources is ad- dressed when the element spacing of a Uniform Array Antenna (ULA) is inordinate. It is illustrated that one can resolve the aliasing ambiguity by utilizing the frequency diversity of the broadband sources. An algorithm, based on Maximum Likelihood Estimator (MLE), is proposed to estimate the transmitted data signal and the DOA of each source. In the sequel, a subspace-based algorithm is developed and the prob- lem of order estimation is discussed. The adopted signaling framework assumes a subband hopping transmission in order to resolve the problem of source associations and system identi¯cation. The proposed algorithms relax the stringent maximum element-spacing constraint of the arrays pertinent to the upper-bound of frequency transmission and suggest that, under some mild constraints, the element spacing can be conveniently increased. An approximate expression for the estimation error has also been developed to gauge the behavior of the proposed algorithms. Through con- ¯rmatory simulation, it is shown that the performance gain of the proposed setup is potentially signi¯cant, speci¯cally when the transmitters are closely spaced and under low Signal to Noise Ratio (SNR), which makes it applicable to license-free communication.

Array signal processing is an important branch in the field of signal processing. In recent years, it has developed dramatically. It can be applied in such fields as radio detection and ranging, communication, sonar, earthquake, exploration, astronomy and biomedicine. The field of direction of array signal processing can be classified into self-adaption array signal processing and spatial spectrum, in which spatial spectrum estimation theory and technology is still in the ascendant status, and become a main aspect in the course of array signal processing. Spatial spectrum estimation is focused on investigating the system of spatial multiple sensor arrays, with the main purpose of estimating the signal’s spatial parameters and the location of the signal source. The spatial spectrum expresses signal distribution in the space from all directions to the receiver. Hence, if one can get the signal’s spatial spectrum, then the direction of arrival (DOA) can be obtained. As thus, spatial spectrum estimation is also called DOA estimation. DOA technology research is important in array signal processing, which is an interdisciplinary technology that develops rapidly in recent years, especially the direction of arrival with multiple signal sources, the estimation of coherent signal sources, and the DOA estimation of broadband signals. DOA estimation has a wide application prospect in radar, sonar, communication, seismology measurement and biomedicine. Over the past few years, all kinds of algorithms which can be used in DOA estimation have made great achievements, the most classic algorithm among which is Multiple Signal Classification (MUSIC). In this thesis I will give an overview of the DOA estimation based on MUSIC algorithm.

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The demand of wireless communication has increased significantly in the past few decades due to huge demand to deliver multimedia content instantly. The expansion of mobile content paired with affordable mobile devices has opened a new trend for having access to the latest information on mobile devices. This trend is made possible by the technology of smart antenna systems as well as array signal processing algorithms. Array signal processing is not limited to wireless communication, but also found in other applications such as radar, sonar and automotive. One of the important components in array signal processing is its ability to estimate the direction of incoming signals known as directional-of-arrival (DOA). The performance of DOA algorithms depends on the steering vector since it contains information about the direction of incoming signals. One of the main factors to affect the DOA estimation is the array geometries since the array factor of the array geometries determines the definition of the steering vector. Another issue in DOA estimation is that the DOA algorithms are designed based on the ideal assumption that the antenna arrays are free from imperfection conditions. In practice, ideal conditions are extremely difficult to obtain and thus the imperfect conditions will severely degraded the performance of DOA estimation. The imperfect conditions include the presence of mutual coupling between elements and are also characteristic of directional antenna. There are three topics being discussed in this thesis. The first topic being investigated is new geometry of antenna array to improve the performance of DOA estimation. Two variants of the circular-based array are proposed in this thesis: semi-circular array and oval array. Another proposed array is Y-bend array, which is a variant of V-shape array. The proposed arrays are being put forward to offer a better performance of DOA estimation and have less acquired area compared with the circular array. It is found out that the semi-circular array has 5.7% better estimation resolution, 76% lower estimation error, and 20% higher estimation consistency than the circular array. The oval array improves the estimation resolution by 33%, estimation error by 60%, and estimation consistency by 20% compared with the circular array. In addition, for the same number of elements, the oval array requires 12.5% to 15% less area than the circular array. The third proposed array, Y-bend array, has 23% smaller estimation resolution, 88% lower estimation error, and 7% higher estimation consistency than the V-shape array. Among the proposed arrays, the semi-circular possessed the best performance with 25% smaller estimation resolution, ten times smaller estimation error, and 5% higher estimation consistency over the other proposed arrays. Secondly, this thesis investigates the DOA estimation algorithm when using the directional antenna array. In this case, a new algorithm is proposed in order to suit the characteristics of the directional antenna array. The proposed algorithm is a modified version of the Capon algorithm, one of the algorithms in beamforming category. In elevation angle estimation, the proposed algorithm achieves estimation resolution up to 1°. The proposed algorithm also manages to improve the estimation error by 80% and estimation consistency by 10% compared with the Capon algorithm. In azimuth angle estimation, the proposed algorithm achieves 20 times lower estimation error and 20% higher estimation consistency than the Capon algorithm. These simulation results show that the proposed algorithm works effectively with the directional antenna array. Finally, the thesis proposes a new method in DOA estimation process for directional antenna array. The proposed method is achieved by means of modifying covariance matrix calculation. Simulation results suggest that the proposed method improves the estimation resolution by 5° and the estimation error by 10% compared with the conventional method. In summary, this thesis has contributed in three main topics related to DOA estimation; array geometry design, algorithm for the directional antenna array, and method in DOA estimation process for the directional antenna array.

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Sensor arrays are used in many applications where their ability to localize signal sources is essential. For many applications, it is necessary to estimate the direction-of-arrival (DOA) of target sources. Although there are many DOA estimation methods available, most of them are valid only for narrowband signals where time delay can be approximated as a phase shift. This thesis focuses on DOA estimation algorithms for wideband sources. Specifically, this thesis proposes the pruned fast beamformer which can reduce the number of computations of Delay-and-Sum (DS) beamforming by using a multi-resolution structure. For high resolution methods, signal subspace methods are required. Most of the subspace techniques for wideband signals decompose the received wideband signals into several bands of narrowband signals through bandpass filtering. Then, there are two different ways of processing decomposed signals. The incoherent methods process each band independently by a given narrowband method and average the results. The coherent methods attempt to modulate the signals in each band so that they can be combined coherently. In this thesis, a new DOA estimator, which is called TOPS, is developed to avoid disadvantages of both the incoherent and the coherent methods. The new method which can be categorized as a non-coherent method is tested and compared with other methods. It exhibits many desirable features for a number of applications where the sources are wideband such as acoustic direction finding.

A direction of arrival (DoA) estimation technique for broadband microwave signals is proposed using a single ultrawideband antenna. It is inspired by the sound source localization ability of a human auditory system using just one ear (monaural localization). By exploiting the incident angle-dependent frequency response of a wideband antenna, the DoA of a broadband microwave signal can be estimated. The DoA estimation accuracies are evaluated for two antenna configurations and microwave signals with different signal-to-noise ratios. Encouraging the DoA estimation performance of the proposed technique is demonstrated in both simulation and experiment.

Sensor arrays play an instrumental role in a variety of applications, including radar, sonar, radio astronomy, and wireless communications. Employing an array of sensors permits direction-of-arrival (DOA) estimation, interference suppression, and imaging of spatial distributions of sources or scatterers. Linear and planar array geometries can have sensors with uniform or non-uniform spacings. Non-uniform arrays require much fewer sensors to achieve comparable performance to uniform arrays in terms of the spatial resolution and the number of resolvable sources or scatterers. This dissertation proposes novel signal processing methods for narrowband passive (receive-only) sensing and imaging. The focus is on source estimation using linear and planar passive arrays with uniform and non-uniform geometries. Algorithm development for the non-uniform arrays is facilitated by a virtual array structure, called the difference coarray, which comprises pairwise differences of physical sensor positions. The difference coarray naturally arises from the passive sensing signal model. High-resolution DOA estimation techniques, such as the subspace-based methods, are computationally expensive, especially for arrays that span large apertures. Further, performance of such methods deteriorates for coherent sources. We propose efficient and effective Fourier-based iterative techniques for DOA estimation of coherent and uncorrelated sources using linear and planar arrays with both uniform and non-uniform geometries. The considered non-uniform arrays include those with uniform and non-uniform difference coarrays. The proposed DOA estimation techniques build on the iterative interpolated beamformer, which employs an estimate-and-subtract strategy to successively extract the sources and refines the estimates via an interpolation and spectral leakage subtraction scheme. We enable iterative beamforming in the coarray domain for linear and rectangular arrays, specifically compensating for non-uniformity of difference coarrays to yield asymptotically unbiased DOA estimates. We also design the iterative interpolated beamformer for oversampled and undersampled uniform circular arrays under the manifold separation framework, which permits the application of DOA estimation techniques that were developed for uniform linear arrays to arbitrary array geometries, such as circular arrays. The proposed iterative beamforming techniques not only estimate the source DOAs, but also provide source power/amplitude estimates. As such, these Fourier-based methods are applicable to narrowband passive imaging systems for providing an accurate estimate of the distribution of source intensity or amplitude as a function of angle. / Electrical and Computer Engineering

Today the multisensor array signal processing of noisy measurements has received much attention. The classical problem in array signal processing is determining the location of an energy-radiating source relative to the location of the array, in other words, direction-of-arrival (DOA) estimation. One is considering the signal estimation problem when together with the signal(s) of interest some noise and interfering signals are present. In this report a direction-of-arrival estimation system is described based on an antenna array for detecting arrival angles in azimuth plane of signals pitched by the antenna array. For this, the Multiple Signal Classication (MUSIC) algorithmis first of all considered. Studies show that in spite of its good reputation and popularity among researches, it has a certain limit of its performance. In this subspace-based method for DOA estimation of signal wavefronts, the term corresponding to additive noise is initially assumed spatially white. In our paper, we address the problem of DOA estimation of multiple target signals in a particular noise situation - in correlated spherically isotropic noise, which, in many practical cases, models a more real context than under the white noise assumption. The purpose of this work is to analyze the behaviour of the MUSIC algorithm and compare its performance with some other algorithms (such as the Capon and the Classical algorithms) and, uppermost, to explore the quality of the detected angles in terms of precision depending on different parameters, e.g. number of samples, noise variance, number of incoming signals. Some modifications of the algorithms are also done is order to increase their performance. Program MATLAB is used to conduct the studies. The simulation results on the considered antenna array system indicate that in complex conditions the algorithms in question (and first of all, the MUSIC algorithm) are unable to automatically detect and localize the DOA signals with high accuracy. Other algorithms andways for simplification the problem (for example, procedure of denoising) exist and may provide more precision but require more computation time.

In this work, a novel method is proposed to optimize the array geometry for DOA estimation. The method is based on minimization of fine error variances with the constraint that the gross error probability is below a certain threshold. For this purpose, a metric function that reflects the gross and fine error characteristics of the array is offered. Theoretical analyses show that the minimization of this metric function leads to small DOA estimation error variance and small gross error probability. Analyses have been carried out under the assumptions of planar array geometry, isotropic array elements and AWGN. Genetic algorithm is used as an optimization tool and performance simulation is performed by comparing the DOA estimation errors of optimized array to a uniform circular array (UCA). Computer simulations support the theoretical analyses and show that the method proposed leads to significant improvement in array geometry in terms of DOA estimation performance.