Multiple-Input, Multiple-Output (MIMO) processing techniques for wireless communication are of interest for next-generation systems because of their potential to dramatically improve capacity in some propagation environments. When used in applications such as wireless LAN and cellular telephony, the MIMO processing methods must be adapted for the situation where a base station is communicating with many users simultaneously. This dissertation focuses on the downlink of such a channel, where the base station and all of the users have antenna arrays. If the transmitter has advance knowledge of the users' channel transfer functions, it can use that information to minimize the interuser interference due to the signals that are simultaneously transmitted to other users. If the transmitter assumes that all receivers treat the interference as noise, finding a solution that optimizes the use of resources is very difficult. This work proposes two classes of solutions to this problem. First, by forcing some or all of the interference to zero, it is possible to achieve a sub-optimal solution in closed-form. Second, a class of iterative solutions can be derived by extending optimal algorithms for multi-user downlink beamforming to accommodate receivers with multiple antennas. The closed-form solutions generally require less computation, but the iterative solutions offer improved performance are more robust to channel estimation errors, and thus may be more useful in practical applications. The performance of these algorithms were tested under realistic channel conditions by testing them on channels derived from both measurement data and a statistical model of an indoor propagation environment. These tests demonstrated both the ability of the channel to support multiple users, and the expected amount of channel estimation error due to movement of the users, with promising results. The success of any multi-user MIMO processing algorithm is ultimately dependent on the degree of correlation between the users' channels. If a base station is required to support a large number of users, one way to ensure minimal correlation between users' channels is to select groups of users whose channels are most compatible. The globally optimal solution to this problem is not possible without an exhaustive search, so a channel allocation algorithm is proposed that attempts to intelligently select groups of users at a more reasonable computational cost.

Establishing secret keys from the commonly-observed randomness of reciprocal wireless propagation channels has recently received considerable attention. In this work we propose improved strategies for channel estimation between MIMO or beamforming systems for secret key generation. The amount of mutual information that can be extracted from the channel matrix estimates is determined by the quality of channel matrix estimates. By allocating increased energy to channel estimation for higher gain beamforming combinations at the expense of low-gain combinations, key establishment performance can be increased. Formalizing the notion of preferential energy allocation to the most efficient excitations is the central theme of this dissertation. For probing with beamforming systems, we formulate a theoretically optimal probing strategy that upper bounds the number of key bits that can be generated from reciprocal channel observations. Specifically, we demonstrate that the eigenvectors of the channel spatial covariance matrix should be used as beamformer weights during channel estimation and we optimize the energy allocated to channel estimation for each beamformer weight under a total energy constraint. The optimal probing strategy is not directly implementable in practice, and therefore we propose two different modifications to the optimal algorithm based on a Kronecker approximation to the spatial covariance matrix. Though these approximations are suboptimal, they each perform well relative to the upper bound. To explore how effective an array is at extracting all of the information available in the propagation environment connecting two nodes, we apply the optimal beamformer probing strategy to a vector current basis function expansion on the array volume. We prove that the resulting key rate is a key rate spatial bound that upper bounds the key rate achievable by any set of antenna arrays probing the channel with the same total energy constraint. For MIMO systems we assume the channel is separable with a Kronecker model, and then for that model we propose an improved probing strategy that iteratively optimizes the energy allocation for each node using concave maximization. The performance of this iterative approach is better than that achieved using the traditional probing strategy in many realistic probing scenarios.

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第23309号 / 情博第745号 / 新制||情||127(附属図書館) / 京都大学大学院情報学研究科知能情報学専攻 / (主査)准教授 吉井 和佳, 教授 河原 達也, 教授 西野 恒, 教授 田中 利幸 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第21218号 / 情博第671号 / 新制||情||116(附属図書館) / 京都大学大学院情報学研究科通信情報システム専攻 / (主査)教授 佐藤 亨, 教授 山本 衛, 教授 松田 哲也 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

With the expansion of unmanned aircraft system (UAS) technologies, there is a growing need for UAS Traffic Management (UTM) systems to promote safe operation and development. To be successful, these UTM systems must be able to detect and track multiple drones in the presence of clutter. This paper examines the implementation of different algorithms on a compact, X-band, frequency modulated continuous wave (FMCW) radar in an effort to enable more accurate detection and estimation of drones. Several algorithms were tested through post processing on actual radar data to determine their accuracy and usefulness for this system. A promising result was achieved through the application of pulse-Doppler processing. Post processing on recorded radar data showed that a moving target indicator successfully separated a target from clutter. An improvement was also noted for the implementation of phase comparison monopulse which accurately estimated angle of arrival (AOA) and required fewer computations than digital beamforming.The second part of this thesis explains the work done on an adaptive broadband, real time beamformer for RF interference (RFI) mitigation. An effective communication system is reliable and can counteract the effects of jamming. Beamforming is an appropriate solution to RFI. To assist in this process FPGA firmware was developed to prepare signals for frequency domain beamforming. This system allows beamforming to be applied to 150 MHz of bandwidth. Future implementation will allow for signal reconstruction after beamforming and demodulation of a communication signal.

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The research presented in this dissertation is on the development of advanced reduced rank adaptive signal processing for airborne radar space-time adaptive processing (STAP) and steering vector mismatch robustness. This is an important area of research in the field of airborne radar signal processing since practical STAP algorithms should be robust against various kinds of mismatch errors. The clutter return in an airborne radar has widely spread Doppler frequencies; therefore STAP, a two-dimensional adaptive filtering algorithm is required for effective clutter and jamming cancellation. Real-world effects in nonhomogeneous environments increase the number of adaptive degrees of freedom required to adequately suppress interference. The increasing computational complexity and the need to estimate the interference from a limited sample support make full rank STAP impractical. The research presented here shows that the reduced rank multistage Wiener filter (MWF) provides significant subspace compression better than any previous techniques in a nonhomogeneous environment. In addition, the impact of steering vector mismatch will also be examined on the MWF. In an airborne radar environment, it is well known that calibration errors and steering vector mismatch can seriously degrade adaptive array performance and result in signal cancellation. These errors can be caused by many non-ideal factors such as beam steering angle errors, multipath propagation, and phase errors due to array imperfections. Since the MWF centrally features the steering vector on its formulation, it is important to assess the impact of steering vector mismatch. In this dissertation, several novel techniques for increasing robustness are examined and applied to the MWF. These include derivative constraints, quiescent pattern control (QPC) techniques, and covariance matrix tapers (CMT). This research illustrates that a combination of CMT and QPC, denoted CMTQ, is very effective at mitigating the impact of steering vector mismatch. Use of CMTQ augmentation provides the steering vector mismatch robustness that we desire while improving the reduced-rank and reduced sample characteristics of the MWF. Results using Monte Carlo simulations and experimental Multichannel Airborne Radar Measurements (MCARM) data confirm that the use of CMTQ gives superior performance to steering vector errors at a much reduced rank and sample support as compared to conventional techniques. / Ph. D.

This research investigates signal processing involving a single electromagnetic vector-sensor, with an emphasis on the problem regarding signal-selective narrowband direction-of-arrival (DOA) estimation in the presence of interference. The approach in this thesis relies on a high-resolution ESPRIT-based algorithm. Unlike spatially displaced arrays, the sensor cannot estimate the DOA of sources using phase differences between the array elements, as the elements are spatially co-located. However, the sensor measures the full electromagnetic field vectors, so the DOA can be estimated through the Poynting vector. Limited information is available in the open literature regarding signal-selective DOA estimation for a single electromagnetic vector-sensor. In this thesis, it is shown how the Uni-Vector-Sensor-ESPRIT (UVS-ESPRIT) algorithm that relies on a time-series invariance and was originally devised for deterministic harmonic sources can be applied to non-deterministic sources. Additionally, two algorithms, one based on cyclostationarity and the other based on fourth-order cumulants, are formulated based on the UVS-ESPRIT algorithm and are capable of selectively estimating the source DOA in the presence of interference based on the statistical properties of the sources. The cyclostationarity-based UVS-ESPRIT algorithm is capable of selectively estimating the signal-of-interest DOA when the sources have the same carrier frequency, and thus overlap in frequency. The cumulant-based UVS-ESPRIT algorithm devised for this sensor relies on the independent component analysis algorithm JADE and is capable of selectively estimating the signal-of-interest DOA through the fourth-order cumulants only, is robust to spatially colored noise, and is capable of estimating the DOA of more sources than sensor elements. / Master of Science / Electromagnetic vector-sensors are specialized sensors capable of capturing the full electromagnetic field vectors at a single point in space. Direction-of-arrival (DOA) estimation is the problem of estimating the spatial-angular parameters of one or more wavefronts impinging on an array. For a single electromagnetic vector-sensor, the array elements are not spatially displaced, but it is still possible to estimate the direction-of-arrival through the Poynting vector, which relates the electric and magnetic field vectors to the direction of propagation of an electromagnetic wave. Although direction-of-arrival estimation is a well-established area of research, there is limited discussion in the open literature regarding signal-selective DOA estimation in the presence of interference for a single electromagnetic vector-sensor. This research investigates this problem and discusses how the high-resolution Uni-Vector-Sensor-ESPRIT (UVS-ESPRIT) algorithm may be applied to non-deterministic sources. ESPRIT based algorithms capable of selectively estimating the source DOA are formulated based on the cyclostationarity and higher-order statistics of the sources, which are approaches known to be robust to interference. The approach based on higher-order statistics is also robust to spatially colored noise and is capable of estimating the DOA of more sources than sensor elements. The formulation of the UVS-ESPRIT for higher-order statistics relies on the application of the independent component analysis algorithm JADE, an unsupervised learning technique. Overall, this research investigates signal-selective direction-of-arrival estimation using an ESPRIT-based algorithm for a single electromagnetic vector-sensor.

In teleconferencing the audio quality is key to conducting successful meetings. The conference room setting imposes various challenges on the speech signal processing, such as noise and interfering signals, reverberation, or participants positioned far from the telephone unit. This work aims at improving the received speech signal of a conference telephone by implementing sound source localization and beamforming. The implemented microphone array signal processing techniques are compared to the performance of an existing multi-microphone solution and evaluated under various conditions using a planar uniform circular array. Recordings of test-sequences for the evaluation were performed using a custom-built array mockup. The implemented algorithms did not show good enough performance to motivate the increased computational complexity compared to the existing solution. Moreover, an increase in number of microphones used was concluded to have little or no effect on the performance of the methods. The type of microphone used was, however, concluded to have impact on the performance and a subjective listening evaluation indicated a preference for omnidirectional microphones which is recommended to investigate further. / God ljudkvalitet är en grundsten för lyckade telefonmöten. Miljön i ett konferens-rum medför ett flertal olika utmaningar för behandlingen av mikrofonsignalerna: det kan t.ex. vara brus och störningar, eller att den som talar befinner sig långt från telefonen. Målet med detta arbete är att förbättra den talsignal som tas upp av en konferenstelefon genom att implementera lösningar för lokalisering av talaren och riktad ljudupptagning med hjälp av ett flertal mikrofoner. De implementerade metoderna jämförs med en befintlig lösning och utvärderas under olika brusscenarion för en likformig cirkulär mikrofonkonstellation. För utvärderingen användes testsignaler som spelades in med en specialbyggd enhet. De implementerade algoritmerna kunde inte uppvisa en tillräcklig förbättring i jämförelse med den befintliga lösningen för att motivera den ökade beräkningskomplexitet de skulle medföra. Dessutom konstaterades att en fördubbling av antalet mikrofoner gav liten eller ingen förbättring på metoderna. Vilken typ av mikrofon som användes konstaterades däremot påverka resultatet och en subjektiv utvärdering indikerade en preferens för de rundupptagande mikrofonerna, en skillnad som föreslås undersökas vidare.

This work deals with the further development of a method for Direction of Arrival (DOA) estimation based on the Discrete Fourier Transform (DFT) of the sensor array output. In the existing DFT-based algorithm, relatively high SNR is considered, and it is assumed that a large number of sensors are available. In this study an overview of some of the most commonly used DOA estimation techniques will be presented. Then the performance of the DFT method will be analyzed and compared with the performance of existing techniques. Two main objectives will be studied, firstly the reduction of the number of sensors and secondly the performance of the DFT based technique in the presence of noise. Experimental simulations will be presented to illustrate that in absence of noise, the proposed method is very fast and using just one snapshot is sufficient to accurately estimate DOAs. Also, in presence of noise, the method is still relatively fast and using a small number of snapshots, it can accurately estimate DOAs. The above mentioned properties are the result of taking an average of the peaks of the DFTs, X_n (k), obtained from a sequence of N_s snapshots. With N_s sufficiently large, the average over N_s snapshots approaches expected value. Also, the conditions that should be satisfied to avoid overlapping of main-lobes, and thus loosing the DOA of some signals, in the DFT spectrum are examined. This study further analyzes the performance of the proposed method as well as two other commonly used algorithms, MUSIC and conventional beamformer. An extensive simulation was conducted and different features of the spatial DFT technique, such as accuracy, resolution, sensitivity to noise, effect of multiple snapshots and the number of sensors were evaluated and compared with those of existing techniques. The simulations indicate that in most aspects the proposed spatial DFT algorithm outperforms the other techniques. / Graduate