## A Contribution to Efficient Direction Finding using Antenna Arrays (2017)

Bayesian Algorithms for Mobile Terminal Positioning in Outdoor Wireless Environments

The ability to reliably and cheaply localize mobile terminals will allow users to understand and utilize the what, where and when of the surrounding physical world. Therefore, mobile terminal location information will open novel application opportunities in many areas. The mobile terminal positioning problem is categorized into three different types according to the availability of (1) initial accurate location information and (2) motion measurement data. Location estimation refers to the mobile positioning problem when both the initial location and motion measurement data are not available. If both are available, the positioning problem is referred to as position tracking. When only motion measurements are available the problem is known as global localization. These positioning problems were solved within the Bayesian filtering framework in order to work under a common theoretical context. Filter derivation and implementation algorithms are provided with emphasis on ...

Khalaf-Allah, Mohamed — Leibniz University of Hannover

Theoretical aspects and real issues in an integrated multiradar system

In the last few years Homeland Security (HS) has gained a considerable interest in the research community. From a scientific point of view, it is a difficult task to provide a definition of this research area and to exactly draw up its boundaries. In fact, when we talk about the security and the surveillance, several problems and aspects must be considered. In particular, the following factors play a crucial role and define the complexity level of the considered application field: the number of potential threats can be high and uncertain; the threat detection and identification can be made more complicated by the use of camouflaging techniques; the monitored area is typically wide and it requires a large and heterogeneous sensor network; the surveillance operation is strongly related to the operational scenario, so that it is not possible to define a ...

Fortunati Stefano — University of Pisa

This thesis addresses a number of problems all related to parameter estimation in sensor array processing. The unifying theme is that some of these parameters are known before the measurements are acquired. We thus study how to improve the estimation of the unknown parameters by incorporating the knowledge of the known parameters; exploiting this knowledge successfully has the potential to dramatically improve the accuracy of the estimates. For covariance matrix estimation, we exploit that the true covariance matrix is Kronecker and Toeplitz structured. We then devise a method to ascertain that the estimates possess this structure. Additionally, we can show that our proposed estimator has better performance than the state-of-art when the number of samples is low, and that it is also efficient in the sense that the estimates have Cramér-Rao lower Bound (CRB) equivalent variance. In the direction of ...

Wirfält, Petter — KTH Royal Institute of Technology

Direction Finding In The Presence of Array Imperfections, Model Mismatches and Multipath

In direction finding (DF) applications, there are several factors affecting the estimation accuracy of the direction-of-arrivals (DOA) of unknown source locations. The major distortions in the estimation process are due to the array imperfections, model mismatches and multipath. The array imperfections usually exist in practical applications due to the nonidealities in the antenna array such as mutual coupling (MC) and gain/phase uncertainties. The model mismatches usually occur when the model of the received signal differs from the signal model used in the processing stage of the DF system. Another distortion is due to multipath signals. In the multipath scenario, the antenna array receives the transmitted signal from more than one path with different directions and the array covariance matrix is rank-deficient. In this thesis, three new methods are proposed for the problems in DF applications in the presence of array ...

Elbir, Ahmet M. — Middle East Technical Univresity

Enhancement of Speech Signals - with a Focus on Voiced Speech Models

The topic of this thesis is speech enhancement with a focus on models of voiced speech. Speech is divided into two subcategories dependent on the characteristics of the signal. One part is the voiced speech, the other is the unvoiced. In this thesis, we primarily focus on the voiced speech parts and utilise the structure of the signal in relation to speech enhancement. The basis for the models is the harmonic model which is a very often used model for voiced speech because it describes periodic signals perfectly. First, we consider the problem of non-stationarity in the speech signal. The speech signal changes its characteristics continuously over time whereas most speech analysis and enhancement methods assume stationarity within 20-30 ms. We propose to change the model to allow the fundamental frequency to vary linearly over time by introducing a chirp ...

Nørholm, Sidsel Marie — Aalborg University

Advanced Signal Processing Techniques for Global Navigation Satellite Systems

This Dissertation addresses the synchronization problem using an array of antennas in the general framework of Global Navigation Satellite Systems (GNSS) receivers. Positioning systems are based on time delay and frequency-shift estimation of the incoming signals in the receiver side, in order to compute the user's location. Sources of accuracy degradation in satellite-based navigation systems are well-known, and their mitigation has deserved the attention of a number of researchers in latter times. While atmospheric-dependant sources (delays that depend on the ionosphere and troposphere conditions) can be greatly mitigated by differential systems external to the receiver's operation, the multipath effect is location-dependant and remains as the most important cause of accuracy degradation in time delay estimation, and consequently in position estimation, becoming a signal processing challenge. Traditional approaches to time delay estimation are often embodied in a communication systems framework. Indeed, ...

Fernandez-Prades, Carles — Universitat Politecnica de Catalunya

Performance Analysis of Bistatic Radar and Optimization methodology in Multistatic Radar System

This work deals with the problem of calculating the Cramer-Rao lower bounds (CRLBs) for bistatic radar channels. To this purpose we exploited the relation between the Ambiguity Function (AF) and the CRLB. The bistatic CRLBs are analyzed and compared to the monostatic counterparts as a function of the bistatic geometric parameters. In the bistatic case both geometry factors and transmitted waveforms play an important role in the shape of the AF, and therefore in the estimation accuracy of the target range and velocity. In particular, the CRLBs depend on the target direction of arrival, the bistatic baseline length, and the distance between the target and the receiver. The CRLBs are then used to select the optimum bistatic channel (or set of channels) for the tracking of a radar target moving along a trajectory in a multistatic scenario and for design ...

Stinco, Pietro — Universita di Pisa

MIMO Radars with Sparse Sensing

Multi-input and multi-output (MIMO) radars achieve high resolution of arrival direction by transmitting orthogonal waveforms, performing matched filtering at the receiver end and then jointly processing the measurements of all receive antennas. This dissertation studies the use of compressive sensing (CS) and matrix completion (MC) techniques as means of reducing the amount of data that need to be collected by a MIMO radar system, without sacrificing the system’s good resolution properties. MIMO radars with sparse sensing are useful in networked radar scenarios, in which the joint processing of the measurements is done at a fusion center, which might be connected to the receive antennas via a wireless link. In such scenarios, reduced amount of data translates into bandwidth and power saving in the receiver-fusion center link. First, we consider previously defined CS-based MIMO radar schemes, and propose optimal transmit antenna ...

Sun, Shunqiao — Rutgers, The State University of New Jersey

Polarization and Index Modulations: a Theoretical and Practical Perspective

Radiocommunication systems have evolved significantly in recent years in order to meet present and future demands. Historically, time, frequency and more recently, spatial dimensions have been used to improve capacity and robustness. Paradoxically, radiocommunications that leverage the polarization dimension have not evolved at the same pace. In particular, these communications are widely used by satellites, where several streams are multiplexed in each orthogonal polarization. Current communication trends advocate for simplifying and unifying different frameworks in order to increase flexibility and address future needs. Due to this, systems that do not require channel information are progressively gaining traction, as they help to improve the overall quality of the network instead of that of specific users only. The search for new paradigms aimed at improving the quality of wireless communications is unstoppable. In order to increase the capacity of current communications systems, ...

Henarejos, Pol — Universitat Politècnica de Catalunya

Bayesian Signal Processing Techniques for GNSS Receivers: from multipath mitigation to positioning

This dissertation deals with the design of satellite-based navigation receivers. The term Global Navigation Satellite Systems (GNSS) refers to those navigation systems based on a constellation of satellites, which emit ranging signals useful for positioning. Although the american GPS is probably the most popular, the european contribution (Galileo) will be operative soon. Other global and regional systems exist, all with the same objective: aid user's positioning. Initially, the thesis provides the state-of-the-art in GNSS: navigation signals structure and receiver architecture. The design of a GNSS receiver consists of a number of functional blocks. From the antenna to the fi nal position calculation, the design poses challenges in many research areas. Although the Radio Frequency chain of the receiver is commented in the thesis, the main objective of the dissertation is on the signal processing algorithms applied after signal digitation. These ...

Closas, Pau — Universitat Politecnica de Catalunya

Three dimensional shape modeling: segmentation, reconstruction and registration

Accounting for uncertainty in three-dimensional (3D) shapes is important in a large number of scientific and engineering areas, such as biometrics, biomedical imaging, and data mining. It is well known that 3D polar shaped objects can be represented by Fourier descriptors such as spherical harmonics and double Fourier series. However, the statistics of these spectral shape models have not been widely explored. This thesis studies several areas involved in 3D shape modeling, including random field models for statistical shape modeling, optimal shape filtering, parametric active contours for object segmentation and surface reconstruction. It also investigates multi-modal image registration with respect to tumor activity quantification. Spherical harmonic expansions over the unit sphere not only provide a low dimensional polarimetric parameterization of stochastic shape, but also correspond to the Karhunen-LoÂ´eve (K-L) expansion of any isotropic random field on the unit sphere. Spherical ...

Li, Jia — University of Michigan

Bayesian methods for sparse and low-rank matrix problems

Many scientific and engineering problems require us to process measurements and data in order to extract information. Since we base decisions on information, it is important to design accurate and efficient processing algorithms. This is often done by modeling the signal of interest and the noise in the problem. One type of modeling is Compressed Sensing, where the signal has a sparse or low-rank representation. In this thesis we study different approaches to designing algorithms for sparse and low-rank problems. Greedy methods are fast methods for sparse problems which iteratively detects and estimates the non-zero components. By modeling the detection problem as an array processing problem and a Bayesian filtering problem, we improve the detection accuracy. Bayesian methods approximate the sparsity by probability distributions which are iteratively modified. We show one approach to making the Bayesian method the Relevance Vector ...

Sundin, Martin — Department of Signal Processing, Royal Institute of Technology KTH

Advanced Interference Suppression Techniques for Spread Spectrum Systems

Code division multiple access (CDMA) techniques have been widely employed by different wireless systems with many advantages. However, the performance of these systems is limited by interference. A number of different interference suppression techniques have been proposed, including multiuser detection, beamforming, adaptive supervised and blind algorithms, and transmit processing techniques requiring a limited feedback channel. Recently, CDMA techniques have also been combined with multicarrier and multiantenna schemes to further increase the system capacity and performance. This thesis investigates the existing algorithms and structures and proposes novel interference suppression algorithms for spread spectrum systems. Firstly we investigate blind constrained constant modulus (CCM) stochastic gradient (SG) receivers with a low-complexity variable step-size mechanism for downlink direct sequence CDMA (DS-CDMA) systems. This algorithm provides better performance than existing blind schemes in non-stationary scenarios. Convergence and tracking analyses of the proposed adaptation techniques are ...

Yunlong Cai — University of York

Algorithms and architectures for adaptive array signal processing

Antenna arrays sample propagating waves at multiple locations. They are employed e.g. in radar, sonar and wireless communication systems because of their capacity of spatial selectivity and localization of radiating sources. Current model-based algorithms make use of computationally demanding orthogonal matrix decompositions such as the singular value decomposition (SVD). On the other hand the data rates are often extremely high. Therefore, real-time execution of complex algorithms often requires parallel computing. We study the simultaneous design of new algorithms and parallel architectures for subspace tracking, for robust adaptive beamforming and for direction finding of narrow-band and wide-band sources. By structuring all recursive algorithms in a similar way, they can be mapped efficiently onto the Jacobi architecture, which was originally developed for SVD updating. The numerical and architectural aspects of this algorithm are improved by the use of a minimal parameterziation of ...

Vanpoucke, Filiep — Katholieke Universiteit Leuven

Antenna Selection Techniques in Single- and Multi-user Systems: A Cross-layer Approach

Over the last decade, the massive demand for high data-rate wireless applications has motivated the study and design of new communication technologies. Among all of them, multi-antenna schemes have been shown to provide remarkable benefits in terms of spectral efficiency. In order to achieve channel capacity bounds, some sort of pre-processing on the transmit side must be encompassed. Unless reciprocity between the forward and reverse links can be assumed, a feedback channel is required to convey channel state information. In such a context, transmit antenna selection emerges as an effective alternative requiring a low amount of information in the feedback channel. Antenna selection algorithms are usually designed in order to maximize physical layer performance but, recently, optimization criteria based on cross-layer designs are gaining popularity. This is mainly motivated by the inefficiency observed in the direct application of the Open ...

López Vicario, José — CTTC-Centre Tecnològic de Telecomunicacions de Catalunya

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