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.

Estimating an object’s position can be of great interest in several applications,and there exists many different methods to do so. One approach is with Directionof Arrival (DOA) measurements from receivers to use the triangulation techniqueto estimate one or more transmitter’s position. One algorithm which can find theDOA measurements from several transmitters is the MUltiple SIgnal Classification(MUSIC) algorithm. However, this still leaves a ambiguity problem which givesfalse solutions, so called ghost points, if the number of receivers is not sufficient.In this report solving this problem with the help of deep learning is studied. Thethesis’s main objective is to investigate and study whether it is possible to performpositioning with measurements from the MUSIC-algorithm using deep learningand image processing methods. A deep neural network is built in TensorFlow and trained and tested using datagenerated from MATLAB. This thesis’s setup consists of two receivers, which areused to locate two transmitters. The network uses two MUSIC spectra from thetwo receivers, and returns a probability distribution of where the transmittersare located. The results are compared with a traditional method and are analysed.The results presented in this thesis show that it is possible to perform positioningusing deep learning methods. However, there is a lot of room for improvementwith accuracy, which can be an important future research direction to explore.

Approved for public release, distribution is unlimited / High Frequency (HF) radar backscatter instruments are being developed and tested in the marine science and defense science communities for their abilities to sense surface parameters remotely in the coastal ocean over large areas. In the Navy context, the systems provide real-time mapping of ocean surface currents and waves critical for characterizing and forecasting the battle space environment. In this study, the performance of a network of four CODAR (Coastal Ocean Dynamics Application Radar) SeaSonde HF radars, using the Multiple Signal Classification (MUSIC) algorithm for direction finding, is described for the period between July to September 2003. Comparisons are made in Monterey Bay with moored velocity observations, with four radar baseline pairs, and with velocity observations from sixteen drifter deployments. All systems measure ocean surface current and all vector currents are translated into radial current components in the direction of the various radar sites. Measurement depths are 1 m for the HF radar-derived currents, 12 to 20 m for the ADCP bin nearest to the surface at the M1 mooring site, and 8 m for the drifter-derived velocity estimates. Comparisons of HF radar-M1 mooring buoy, HF radar-HF radar (baseline), and HF radar-drifter data yield improvements of - 1.7 to 16.7 cm/s rms differences and -0.03 to 0.35 correlation coefficients when measured antenna patterns are used. The mooring comparisons and the radar-to-radar baseline comparisons indicate angular shifts of 10Ê» to 30Ê» for radial currents produced using ideal antenna patterns and 0Ê» to 15Ê» angular shifts for radial currents produced using measured patterns. The comparisons with drifter-derived radial currents indicate that these angular biases are not constant across all look directions, even though the local antenna pattern distortions were taken into account through the use of measured antenna patterns. In particular, data from the SCRZ and MLNG radar sites show varied pointing errors across the range of angles covered. / Lieutenant Commander, Republic of Korea Navy

<p dir="ltr">Developing non-invasive and non-destructive testing in complex media continues to be a rich field of study (see e.g.[22, 28, 36, 76, 89] ). These types of tests have applications in medical imaging, geophysical exploration, and engineering where one would like to detect an interior region or estimate a model parameter. With the current rapid development of this enabling technology, there is a growing demand for new mathematical theory and computational algorithms for inverse problems in partial differential equations. Here the physical models are given by a boundary value problem stemming from Electrical Impedance Tomography (EIT), Diffuse Optical Tomography (DOT), as well as acoustic scattering problems. Important mathematical questions arise regarding existence, uniqueness, and continuity with respect to measured surface data. Rather than determining the solution of a given boundary value problem, we are concerned with using surface data in order to develop and implement numerical algorithms to recover unknown subregions within a known domain. A unifying theme of this thesis is to develop Qualitative Methods to solve inverse shape problems using measured surface data. These methods require very few a priori assumptions on the regions of interest, boundary conditions, and model parameter estimation. The counterpart to qualitative methods, iterative methods, typically require a priori information that may not be readily available and can be more computationally expensive. Qualitative Methods usually require more data.</p><p dir="ltr">This thesis expands the library of Qualitative Methods for elliptic problems coming from tomography and scattering theory. We consider inverse shape problems where our goal is to recover extended and small volume regions. For extended regions, we consider applying a modified version of the well-known Factorization Method [73]. Whereas for the small volume regions, we develop a Multiple Signal Classification (MUSIC)-type algorithm (see for e.g. [3, 5]). In all of our problems, we derive an imaging functional that will effectively recover the region of interest. The results of this thesis form part of the theoretical forefront of physical applications. Furthermore, it extends the mathematical theory at the intersection of mathematics, physics and engineering. Lastly, it also advances knowledge and understanding of imaging techniques for non-invasive and non-destructive testing.</p>

Multiple Signal Classification (MUSIC) Algorithm based and Wigner Distribution-Principal Component Analysis (WD-PCA) based classification techniques are very recently suggested resonance region approaches for electromagnetic target classification. In this thesis, performances of these two techniques will be compared concerning their robustness for noise and their capacity to handle large number of candidate targets. In this context, classifier design simulations will be demonstrated for target libraries containing conducting and dielectric spheres and for dielectric coated conducting spheres. Small scale aircraft targets modeled by thin conducting wires will also be used in classifier design demonstrations.

In this thesis, optimization of the geometry of non-uniform arrays for direction finding yielding unambiguous results is studied. A measure of similarity between the array response vectors is defined. In this measure, the effects of antenna array geometry, source placements and antenna gains are included as variable parameters. Then, assuming that the antenna gains are known and constant, constraints on the similarity function are developed and described to result in unambiguous configurations and maximum resolution. The problem stated is solved with two different methods, the MATLAB optimization toolbox, and genetic algorithm in which different genetic codings are also studied. The performance of the MUSIC algorithm with the optimized array geometries are investigated through computer simulations. The direction of arrival estimates are obtained using the optimized array geometry on the MUSIC algorithm along with the effects of different parameters. Statistics of the true and probable erroneous arrival angles and the probability of gross error are obtained as a measure of performance. It is observed that the proposed optimization process for the array geometry gave rise to unambiguous results for direction finding.

This thesis presents a novel aspect and polarization invariant electromagnetic target recognition technique in resonance region based on use of MUSIC algorithm for the extraction of natural-resonance related target features. In the suggested method, the feature patterns called &ldquo / MUSIC Spectrum Matrices (MSMs)&rdquo / are constructed for each candidate target at each reference aspect angle using targets&rsquo / scattered data at different late-time intervals. These individual MSMs correspond to maps of targets&rsquo / natural-resonance related power distributions. All these patterns are first used to obtain optimal late-time interval for classifier design and a &ldquo / Fused MUSIC Spectrum Matrix (FMSM)&rdquo / is generated over this interval for each target by superposing MSMs. The resulting FMSMs include more complete information for target resonances and are almost insensitive to aspect and polarization. In case of multiple target recognition, the relative locations of a multi-target group and separation distance between targets are also important factors. Therefore, MSM features are computed for each multi-target group at each &ldquo / reference aspect/topology&rdquo / combination to determine the optimum late-time interval. The FMSM feature of a given multi-target group is obtained by the superposition of all these aspect and topology dependent MSMs. In both single and multiple target recognition cases, the resulting FMSM power patterns are main target features of the designed classifier to be used during real-time decisions. At decision phase, the unknown test target is classified either as one of the candidate targets or as an alien target by comparing correlation coefficients computed between MSM of test signal and FMSM of each candidate target.

State of the art direction finding (DF) systems usually have several antennas in order to increase accuracy and robustness to certain factors. In this thesis, a three antenna DF system is built and evaluated. While more antennas give better DF performance, a three antenna system is useful for system simplicity and many of the problems in DF systems can be observed and evaluated easily. This system can be used for both azimuth and elevation direction of arrival (DOA) estimation. The system is composed of three monopole antennas, an RF front end, A/D converters and digital signal processing (DSP) units. A number of algorithms are considered, such as, three channel interferometer, correlative interferometer, LSE (least square error) based correlative interferometer and MUSIC (multiple signal classification) algorithms. Different problems in DF systems are investigated. These are gain/phase mismatch of the receiver channels, mutual coupling between antennas, multipath signals and multiple sources. The advantages and disadvantages of different algorithms are outlined.

Abstract The focus of this thesis is to study direction of arrival (DoA) estimation algorithms for reconfigurable leaky-wave antennas and advanced antenna arrays. Directional antennas can greatly improve the spectrum reuse, interference avoidance, and object and people localization. DoA estimation algorithms have also been shown to be useful for applications such as positioning for user tracking and location-based services in wireless local area networks (WLANs). The main goal is to develop novel DoA estimation algorithms for both advanced antenna arrays and composite right/left-handed (CRLH) leaky-wave antennas (LWAs). The thesis introduces novel modifications to existing DoA estimation algorithms and shows how these can be modified for real-time DoA estimation using both antenna types. Three modified DoA estimation algorithms for CRLH-LWAs are presented: 1) modified multiple signal classification (MUSIC), 2) power pattern cross-correlation (PPCC), and 3) adjacent power pattern ratio (APPR). Additionally, the APPR algorithm is also applied to advanced antenna arrays. The thesis also presents improvements to the modified MUSIC and APPR algorithms. The complexity of the algorithms is reduced by selecting a smaller number of received signals from different directions. The results show that the selection of the radiation patterns is very important and that the proposed algorithms can successfully estimate the DoA, even in a real-world environment. Based on the results, this thesis provides a good starting point for future research of DoA estimation algorithms to enhance the performance of future-generation wireless networks and the accuracy of localization. / Tiivistelmä Tässä väitöskirjassa tutkitaan suunnanestimointialgoritmeja uudelleen konfiguroituville vuotoaaltoantenneille (LWA, leaky wave antenna) ja kehittyneille antenniryhmille. Suuntaavilla antenneilla voidaan parantaa huomattavasti spektrin uudelleen käyttöä ja esineiden ja ihmisten sijaintipaikannusta sekä pienentää häiriöitä. Suunnanestimointialgoritmit ovat myös osoittautuneet hyödylliseksi esimerkiksi seuranta- ja sijaintipaikannuspalvelusovelluksille langattomissa lähiverkoissa. Työn päätavoite on kehittää uusia suunnanestimointialgoritmeja sekä kehittyneille antenniryhmille että vuotoaaltoantenneille (composite right/left-handed (CRLH) LWA). Työssä osoitetaan, miten olemassa olevia suunnanestimointialgoritmeja voidaan muokata uudella tavalla, jotta ne soveltuisivat molemmille antennityypeille reaaliaikaiseen suunnanestimointiin. Vuotoaaltoantennille on kehitetty kolme erilaista suunnanestimointialgoritmia: 1) muunneltu MUSIC- (multiple signal classification), 2) säteilykyvioiden tehojen ristikorrelaatio- (PPCC, power pattern cross correlation) ja 3) vierekkäisten säteilykuvioiden tehosuhdealgoritmi (APPR, adjacent power pattern ratio). APPR-algoritmia on myös käytetty kehittyneelle antenniryhmälle. Työssä esitetään myös parannuksia muunnelluille MUSIC- ja APPR-algoritmeille. Algoritmien kompleksisuutta voidaan pienentää valitsemalla vähemmän vastaanotettuja signaaleja. Tulokset osoittavat, että signaalien valinta on hyvin tärkeää ja ehdotetut algoritmit estimoivat onnistuneesti saapuvan signaalin suunnan todellisessa mittausympäristössä. Yhteenvetona voidaan sanoa, että tämä väitöstyö on hyvä lähtökohta suunnanestimointialgoritmitutkimukselle, jonka tavoitteena on parantaa tulevien sukupolvien langattomien verkkojen suorituskykyä ja paikannuksen tarkkuutta.