An antenna array is an indispensable portion of a direction-of-arrival (DOA) estimation operation. A number of error sources in the arrays degrade the DOA estimation accuracy. Mutual coupling effect is one of the main error sources and should be corrected for any antenna array. In this thesis, a system theoretic approach is presented for mutual coupling characterization of antenna arrays. In this approach, the idea is to model the mutual coupling effect through a simple linear transformation between the measured and the ideal array data. In this context, a measurement reduction method (MRM) is proposed to decrease the number of calibration measurements. This new method dramatically reduces the number of calibration measurements for omnidirectional antennas. It is shown that a single calibration measurement is sufficient for uniform circular arrays when MRM is used. The method is extended for the arrays composed of non-omnidirectional (NOD) antennas. It is shown that a single calibration matrix can not properly model the mutual coupling effect in an NOD antenna array. Therefore, a sectorized calibration approach is proposed for NOD antenna arrays where the mutual coupling calibration is done in angular sectors. Furthermore, mutual coupling problem is also investigated for antenna arrays over a perfect electric conductor plate. In this case, reflections from the plate lead to gain/phase mismatches in the antenna elements. In this context, a composite matrix approach is proposed where mutual coupling and gain/phase mismatch are jointly modelled by using a single composite calibration matrix. The proposed methods are evaluated over DOA estimation accuracies using Multiple Signal Classification (MUSIC) algorithm. The calibration measurements are obtained using the numerical electromagnetic simulation tool FEKO. The evaluation results show that the proposed methods effectively realize the mutual coupling calibration of antenna arrays.

Orientador: Amauri Lopes / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-09T06:28:20Z (GMT). No. of bitstreams: 1 Krummenauer_Rafael_M.pdf: 1711180 bytes, checksum: e9aa73aac9d705c24c2bec02b202f76e (MD5) Previous issue date: 2007 / Resumo: Esta dissertação trata do problema de estimação de direção de chegada (DOA) de ondas planas usando um arranjo linear uniforme de sensores. Estamos interessados em situações nas quais a relação sinal-ruido 'e baixa e o espaçamento angular entre as fontes de sinal 'e pequeno. Baseamos nossa proposta nos m'etodos MODE, MODEX e MODEX Modificado, que sao metodos eficientes existentes na literatura. Inspirados em conceitos de filtragem linear e no criterio da maxima verossimilhança, propomos um procedimento que ameniza o efeito do ruido no resultado da estimação. Este procedimento consiste em filtrar os dados recebidos e modificar adequadamente a função de verossimilhan¸ca utilizada no processo de obtenção das estimativas. Simulações numericas mostram que o desempenho do metodo proposto 'e melhor que aqueles correspondentes aos m'etodos MODE, MODEX e MODEX Modificado, alcançando menores valores de erro quadratico medio e de polarização / Abstract: This work deals with the problem of estimating the direction of arrival (DOA) of plane waves using a uniform linear array of sensors. We are concerned with situations where the signal-to-noise ratio is low and the signal sources are spatially close. Our proposal is based on MODE, MODEX and Modified MODEX, that are efficient methods proposed in the literature. Inspired in concepts of linear filtering and in the maximum likelihood criterion, we propose a procedure that reduces the effect of noise in the estimation result. This procedure consists on filtering the received data and on modifying the likelihood function used to obtain the estimates. Numerical simulations show that the performance of the proposed method is better than those of MODE, MODEX and Modified MODEX methods, achieving lower mean square error and lower bias / Mestrado / Telecomunicações e Telemática / Mestre em Engenharia Elétrica

Orientador: Amauri Lopes / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-09T08:58:42Z (GMT). No. of bitstreams: 1 Silva_FrancisleiJoseda_M.pdf: 1914501 bytes, checksum: f819de68e8cb02f4b9bc4a46d9787202 (MD5) Previous issue date: 2007 / Resumo: Este trabalho aborda o problema de estimação da direção de chegada (DOA) de ondas planas usando arranjo de sensores. Existem diversos estimadores para DOA relatados na literatura. Dentre os estimadores de alta resolução, se destacam os métodos MODE e MODEX, que possuem como base o estimador de máxima verossimilhança (MLE). Este trabalho apresenta o desenvolvimento dos métodos MODE, MODEX e de uma versão melhorada do MODEX, o método MODEX Modi?ed. Estes dois últimos estimadores produzem várias estimativas candidatas e usam o critério de máxima verossimilhança para selecionar aquelas que representam as melhores estimativas para os ângulos de chegada. Entretanto, para uma relação sinalruído baixa, estes métodos sofrem uma forte degradação na escolha das candidatas. Na busca de reduzir esta degradação, é apresentada uma proposta de ?ltragem nos sinais captados pelos sensores, com o objetivo de melhorar a relação sinalruído. São propostos dois projetos de ?ltro FIR: um por alocação de pólos e zeros, e outro por amostragem em freqüência. Os resultados obtidos mostram que esta proposta de ?ltragem é válida e que se consegue reduzir signi?cativamente a SNR do limiar de desempenho apresentado pelos métodos MODEX e MODEX Modi?ed. / Abstract: This work deals with the estimation of the direction of arrival (DOA) of plane waves using array of sensors. There are various estimators for DOA reported in literature. The MODE and MODEX methods, based on the maximum likelihood criterion, are the best high resolution DOA estimators. This work presents the development of these methods as well as of an improved version of the MODEX, named MODEX Modi?ed. MODEX and MODEX Modi?ed produce some estimates that are candidates for the DOA estimation and use the maximum likelihood criterion to select the best ones. However, for low signaltonoise ratio, the selection process suffers a strong performance degradation. In order to reduce this degradation, this work proposes to ?lter the received signals aiming to improve the signaltonoise ratio. Two FIR ?lters are considered: one composed by poles and zeros and another obtained by sampling in the frequency domain. Simulation results show that this proposal improves signi?cantly the performance of both MODEX and MODEX Modi?ed. / Mestrado / Telecomunicações e Telemática / Mestre em Engenharia Elétrica

[pt] Em sistemas de comunicações móveis, a modulação digital em fase (PSK)é amplamente utilizada em esquemas de transmissão em rádio-propagação. Trabalhos anteriores consideraram alguns métodos baseados no critério de máxima verossimilhança (MV) para estimação de direção-de-chegada de sinais genéricos que atingem um conjunto (array) de sensores. Esta tese propõe um novo estimador MV para a direção-de-chegada, desenvolvido especificamente para sistemas de comunicação PSK. Dois modelos de transmissão são concebidos para estimação dos parâmetros: um mais idealizado, considerando todas as portadoras alinhadas no tempo com o receptor, e outro que considera este desalinhamento na forma de retardo. O número de parâmetros a serem conjuntamente estimados é significativamente reduzido ao se calcular o valor esperado dos sinais medidos no array de antenas com relação µas fases de modulação (dados de informação). O desempenho do estimador em vários cenários simulados é apresentado e comparado ao desempenho do estimador MV clássico desenvolvido sem considerar uma estrutura específica para o sinal. Limitantes de Cramér-Rao para os cenários de portadora única também são calculados. O método proposto se mostra mais robusto por apresentar melhor desempenho que o estimador MV clássico em todas as simulações. / [en] In mobile communication systems, phase shift keying (PSK) modulation is widely used in digital transmission schemes. Previous works have considered several maximum likelihood (ML) methods for the direction-of-arrival (DOA) estimation of generic signals reaching a phased-array of sensors. This thesis proposes a new ML DOA estimator designed to be used in PSK communication systems. Two transmission models are considered for parameter estimation: a simpler one, considering all carrier clocks time-aligned with the receiver clock, and another that considers this misalignment as a delay for each carrier. The number of parameters to be jointly estimated is significantly reduced when the expected value of the antenna array measured signals with respect to the modulation phases is evaluated. The estimator performance in several simulation scenarios is presented and compared to the performance of a classic ML estimator designed for all sorts of signal models. Cramér-Rao bounds for single carrier scenarios are also evaluated. The proposed method robustly outperforms the classic ML estimator in all simulations.

No description available.

In this thesis, DOA estimation using array interpolation in randomly distributed sensor arrays is considered. Array interpolation is a technique in which a virtual array is obtained from the real array and the outputs of the virtual array, computed from the real array using a linear transformation, is used for direction of arrival estimation. The idea of array interpolation techniques is to make simplified and computationally less demanding high resolution direction finding methods applicable to the general class of non-structured arrays.In this study,we apply an interpolation technique for arbitrary array geometries in an attempt to extend root-MUSIC algorithm to arbitrary array geometries.Another issue of array interpolation related to direction finding is spatial smoothing in the presence of multipath sources.It is shown that due to the Vandermonde structure of virtual array manifold vector obtained from the proposed interpolation method, it is possible to use spatial smoothing algorithms for the case of multipath sources.

In this study, a neural network(NN) based algorithm is proposed for real time multiple source tracking problem based on a previously reported work. The proposed algorithm namely modified neural network based multiple source tracking algorithm (MN-MUST) performs direction of arrival(DoA) estimation in three stages which are the detection, filtering and DoA estimation stages. The main contributions of this proposed system are: reducing the input size for the uncorrelated source case (reducing the training time) of NN system without degradation of accuracy and insertion of a nonlinear spatial filter to isolate each one of the sectors where sources are present, from the others. MN-MUST algorithm finds the targets correctly no matter whether the targets are located within the same angular sector or not. In addition as the number of targets exceeds the number of antenna elements the algorithm can still perform sufficiently well. Mutual coupling in array does not influence MN-MUST algorithm performance. iv MN-MUST algorithm is further improved for a cylindrical microstrip patch antenna array by using the advantages of directive antenna pattern properties. The new algorithm is called cylindrical patch array MN-MUST(CMN-MUST). CMN-MUST algorithm consists of three stages as MN-MUST does. Detection stage is exactly the same as in MN-MUST. However spatial filtering and DoA estimation stage are reduced order by using the advantages of directive antenna pattern of cylindirical microstrip patch array. The performance of the algorithm is investigated via computer simulations, for uniform linear arrays, a six element uniform dipole array and a twelve element uniform cylindrical microstrip patch array. The simulation results are compared to the previously reported works and the literature. It is observed that the proposed algorithm improves the previously reported works. The algorithm accuracy does not degrade in the presence of the mutual coupling. A uniform cylindrical patch array is successfully implemented to the MN-MUST algorithm. The implementation does not only cover full azimuth, but also improv the accuracy and speed. It is observed that the MN-MUST algorithm provides an accurate and efficient solution to the targettracking problem in real time.

In this thesis, two-dimensional (2-D) direction-of-arrival (DOA) estimation problem is considered. Usually, DOA estimation is considered in one dimension assuming a fixed elevation angle. While this assumption simplifies the problem, both the azimuth and elevation angles, namely, the 2-D DOA estimates are required in practical scenarios. In this thesis, planar array structures are considered for 2-D DOA estimation. In this context, V-shaped arrays are discussed and some of the important features of these arrays are outlined. A new method for the design of V-shaped arrays is presented for both isotropic and directional beam patterns. The design procedure is simple and can be applied for both uniform and nonuniform V-shaped sensor arrays. Closed form expressions are presented for the V-angle in order to obtain isotropic angle performance. While circular arrays have the isotropic characteristics, V-shaped arrays present certain advantages due to their large aperture for the same number of sensors and inter-sensor distance. The comparison of circular and V-shaped arrays is done by considering the azimuth and elevation Cramer-Rao Bounds (CRB). It is shown that V-shaped and circular arrays have similar characteristics for the sensor position errors while the uniform isotropic (UI) V-array performs better when there is mutual coupling and the sources are correlated. In the literature, there are several techniques for 2-D DOA estimation. Usually, fast algorithms are desired for this purpose since a search in two dimensions is a costly process. These algorithms have a major problem, namely, the pairing of the azimuth-elevation couples for multiple sources. In this thesis, a new fast and effective technique for this purpose is proposed. In this technique, a virtual array output is generated such that when the ESPRIT algorithm is used, the eigenvalues of the rotational transformation matrix have the 2-D angle information in both magnitude and phase. This idea is applied in different scenarios and three methods are presented for these cases. In one case, given an arbitrary array structure, array interpolation is used to generate the appropriate virtual arrays. When the antenna mutual coupling is taken into account, a special type of array structure, such as circular, should be used in order to apply the array interpolation. In general, the array mutual coupling matrix (MCM) should have a symmetric Toeplitz form. It is shown that the 2-D DOA performance of the proposed method approaches to the CRB by using minimum number of antennas in case of mutual coupling. This method does not require the estimation of the mutual coupling coefficients. While this technique is effective, it has problems especially when the number of sources increases. In order to improve the performance, MCM is estimated in the third approach. This new approach performs better, but it cannot be used satisfactorily in case of multipath signals. In this thesis, the proposed idea for fast 2-D DOA estimation is further developed in order to solve the problem when mutual coupling and multipath signals jointly exist. In this case, real arrays with some auxiliary sensors are used to generate a structured mutual coupling matrix. It is shown that the problem can be effectively solved when the array structure has a special form. Specifically, parallel uniform linear arrays (PULA) are employed for this purpose. When auxiliary sensors are used, a symmetric banded Toeplitz MCM is obtained for the PULA. This allows the application of spatial smoothing and ESPRIT algorithm for 2-D DOA estimation. The proposed algorithm uses triplets and presents closed form paired 2-D DOA estimates in case of unknown mutual coupling and multipath signals. Several simulations are done and it is shown that the proposed array structure and the method effectively solve the problem.

Higher Order Statistics (HOS) and Second Order Statistics (SOS) approaches have certain advantages and disadvantages in signal processing applications. HOS approach provides more statistical information for non-Gaussian signals. On the other hand, SOS approach is more robust to the estimation errors than the HOS approach, especially when the number of observations is small. In this thesis, HOS and SOS approaches are jointly used in order to take advantage of both methods. In this respect, the joint use of HOS and SOS approaches are introduced for online calibration of sensor arrays with arbitrary geometries. Three different problems in online array calibration are considered and new algorithms for each of these problems are proposed. In the first problem, the positions of the randomly deployed sensors are completely unknown except the two reference sensors and HOS and SOS approaches are used iteratively for the joint Direction of Arrival (DOA) and sensor position estimation. Iterative HOS-SOS algorithm (IHOSS) solves the ambiguity problem in sensor position estimation by observing the source signals at least in two different frequencies and hence it is applicable for wideband signals. The conditions on these frequencies are presented. IHOSS is the first algorithm in the literature which finds the DOA and sensor position estimations in case of randomly deployed sensors with unknown coordinates. In the second problem, narrowband signals are considered and it is assumed that the nominal sensor positions are known. Modified IHOSS (MIHOSS) algorithm uses the nominal sensor positions to solve the ambiguity problem in sensor position estimation. This algorithm can handle both small and large errors in sensor positions. The upper bound of perturbations for unambiguous sensor position estimation is presented. In the last problem, an online array calibration method is proposed for sensor arrays where the sensors have unknown gain/phase mismatches and mutual coupling coefficients. In this case, sensor positions are assumed to be known. The mutual coupling matrix is unstructured. The two reference sensors are assumed to be perfectly calibrated. IHOSS algorithm is adapted for online calibration and parameter estimation, and hence CIHOSS algorithm is obtained. While CIHOSS originates from IHOSS, it is fundamentally different in many aspects. CIHOSS uses multiple virtual ESPRIT structures and employs an alignment technique to order the elements of rows of the actual array steering matrix. In this thesis, a new cumulant matrix estimation technique is proposed for the HOS approach by converting the multi-source problem into a single source one. The proposed algorithms perform well even in the case of correlated source signals due to the effectiveness of the proposed cumulant matrix estimate. The iterative procedure in all the proposed algorithms is guaranteed to converge. Closed form expressions are derived for the deterministic Cram&acute / er-Rao bound (CRB) for DOA and unknown calibration parameters for non-circular complex Gaussian noise with unknown covariance matrix. Simulation results show that the performances of the proposed methods approach to the CRB for both DOA and unknown calibration parameter estimations for high SNR.

Direction of Arrival estimation using unsteered antenna arrays, unlike mechanically scanned or phased arrays, requires complex algorithms which perform poorly with small aperture arrays or without a large number of observations, or snapshots. In general, these algorithms compute a sample covriance matrix to obtain the direction of arrival and some require a prior estimate of the number of signal sources. Herein, artificial neural network architectures are proposed which demonstrate improved estimation of the number of signal sources, the true signal covariance matrix, and the direction of arrival. The proposed number of source estimation network demonstrates robust performance in the case of coherent signals where conventional methods fail. For covariance matrix estimation, four different network architectures are assessed and the best performing architecture achieves a 20 times improvement in performance over the sample covariance matrix. Additionally, this network can achieve comparable performance to the sample covariance matrix with 1/8-th the amount of snapshots. For direction of arrival estimation, preliminary results are provided comparing six architectures which all demonstrate high levels of accuracy and demonstrate the benefits of progressively training artificial neural networks by training on a sequence of sub- problems and extending to the network to encapsulate the entire process.