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Author: Yifan Wu Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Wideband source localization is an important problem in signal processing, and it has wide-range applications in underwater acoustics, indoor speaker localization, teleconferencing, and etc. Over the past few decades, there are significant amount of methods proposed for the wideband source localization. However, it still remains a challenging problem. This dissertation tackles the wideband source localization from data-driven and model-based perspectives. For the data-driven part, a novel deep learning framework for the sound source localization (SSL) was proposed. SSL is to estimate the locations of the sound sources based on the received signal from the microphone array. SSL in the reverberant environment can be challenging due to the multipath artifacts in the received signals. To tackle with this challenge, a deep learning framework based on multi-task learning and image translation (MTIT) network is proposed. MTIT utilizes the encoder-decoder structure and it consists of one encoder and two decoders. The encoder aims to obtain a compressed representation of the input while the two decoders focus on two tasks in parallel. One decoder focuses on mitigating the multipath caused by reverberation and the other decoder predicts the source location. Due to the explicit dereverberation module and the shared encoder (representation), the proposed localization framework can achieve superior performance and can generalize to the unseen data in the reverberant environment compared to the existing baseline methods. For the model-based part, gridless direction-of-arrival (DOA) estimation based on atomic norm minimization (ANM) for the multi-frequency signal was studied. ANM was formulated to an equivalent computationally feasible semi-definite program (SDP) problem. The dual certificate condition is given to certify the optimality. A fast algorithm implementation is given and the dual problem of the SDP is considered. The method is further generalized to the non-uniform array and non-uniform frequency case. Extensive theoretical analysis and numerical experiments demonstrate the superior performance of the proposed method compared to sparse Bayesian learning, the existing grid-based multi-frequency DOA estimation method.
Author: Yifan Wu Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Wideband source localization is an important problem in signal processing, and it has wide-range applications in underwater acoustics, indoor speaker localization, teleconferencing, and etc. Over the past few decades, there are significant amount of methods proposed for the wideband source localization. However, it still remains a challenging problem. This dissertation tackles the wideband source localization from data-driven and model-based perspectives. For the data-driven part, a novel deep learning framework for the sound source localization (SSL) was proposed. SSL is to estimate the locations of the sound sources based on the received signal from the microphone array. SSL in the reverberant environment can be challenging due to the multipath artifacts in the received signals. To tackle with this challenge, a deep learning framework based on multi-task learning and image translation (MTIT) network is proposed. MTIT utilizes the encoder-decoder structure and it consists of one encoder and two decoders. The encoder aims to obtain a compressed representation of the input while the two decoders focus on two tasks in parallel. One decoder focuses on mitigating the multipath caused by reverberation and the other decoder predicts the source location. Due to the explicit dereverberation module and the shared encoder (representation), the proposed localization framework can achieve superior performance and can generalize to the unseen data in the reverberant environment compared to the existing baseline methods. For the model-based part, gridless direction-of-arrival (DOA) estimation based on atomic norm minimization (ANM) for the multi-frequency signal was studied. ANM was formulated to an equivalent computationally feasible semi-definite program (SDP) problem. The dual certificate condition is given to certify the optimality. A fast algorithm implementation is given and the dual problem of the SDP is considered. The method is further generalized to the non-uniform array and non-uniform frequency case. Extensive theoretical analysis and numerical experiments demonstrate the superior performance of the proposed method compared to sparse Bayesian learning, the existing grid-based multi-frequency DOA estimation method.
Author: Ayoub Idelhaj Publisher: ISBN: Category : Languages : en Pages : 42
Book Description
This thesis investigates model-based and data-driven approaches for indoor localization using the Received Signal Strength Indicator (RSSI) of Wi-Fi signals. We study multiple model-based indoor localization approaches, including the free space path loss model, the log-distance path loss model, the International Telecommunication Union (ITU)model, and a nonlinear regression model. We examine their indoor localization accuracy using raw RSSI values, and filter RSSI values passed through a Moving Average filter and a Kalman filter. For data driven approaches, we employ a family of Extreme Learning Machine (ELM) algorithms including Basic-ELM, Online Sequential-ELM (OS-ELM),Hierarchical-ELM (H-ELM), and Kernel-ELM (K-ELM), to find the indoor position. We provide simulation results comparing the performances of both the Machine-learning based approaches and model-based approaches in terms of localization error to identify the algorithms with the lowest localization error.
Author: Dominic V. Poerio Publisher: ISBN: 9780438241404 Category : Languages : en Pages : 230
Book Description
Chemometrics is an interdisciplinary field aimed at extracting information from chemically relevant systems via data-driven means, primarily using the tools of modern statistical and machine learning theory. This dissertation concerns the development of novel methodology in the field of chemometrics for the advancement of numerous applications, including interpretation of spectral data, calibration transfer of multivariate regression models, and adaptive model building for predictions on dynamic systems. ☐ In most applications of data-driven modeling, a single model is built utilizing all of the available data. In chemical data, there are often localized portions of the data that are more or less informative for the specific task at hand. Numerous potential advantages are possible when modeling these local aspects of the data independently in an ensemble model, such as better prediction accuracy or enhanced model interpretation. The focus of this dissertation is the construction of such ensemble models. Two paradigms of local modeling are investigated in this work: time/wavelength-localized modeling and frequency-localized modeling. Utilizing these localization frameworks, we investigate novel methodology for diverse regression tasks. Chapter 1 of this dissertation serves to introduce the background and unifying theory of the concepts utilized throughout the remainder of the dissertation. ☐ In Chapter 2, a static modeling method under a wavelength-localized paradigm combining sparse partial least squares and stacked interval partial least squares is presented. The combination of variable selection and local model weighting permits a straightforward interpretation of the model regression vector when applied to spectral data. The proposed method also performs favorably, in terms of prediction error, when compared to other variable selection and model weighting methods. A number of experiments on the effects of outliers and measurement resolution are also undertaken. ☐ In Chapter 3, a static modeling method using frequency-localization via the discrete wavelet transform paired with orthogonal projection for the calibration transfer of regression models based on spectral data is described. We show that the proposed method is competitive with standard calibration transfer methods. Additional experiments show that the method is superior to standard methods when applying transferred models onto spectra from unseen instruments. ☐ In Chapter 4, a dynamic modeling method using frequency-localization via the undecimated wavelet transform paired with recursive partial least squares for the soft sensing of chemical processes is investigated. We show that the method greatly improves standard adaptive modeling by down-weighting noise that is present in the process variables. It is also shown that the improvement compared to the standard method is statistically significant irrespective of the memory used when updating the model. ☐ In Chapter 5, a dynamic modeling method using time-localization via a large number of overlapping models with memory attenuation for soft sensing of chemical processes is outlined. Covariance based variable selection is utilized on each local model to account for the presence of distinct states in the process data and to create diversity in the ensemble. Experiments conducted at various updating frequencies indicate that the method represents a statistical improvement in prediction error compared to the standard method, as well as the proposed method without variable selection. ☐ In Chapter 6, a dynamic modeling method using self-correction strategies to select local modeling regions and adjust model memory for improved soft sensing is developed. The method uses a regression based on a neural network hidden layer input to the recursive partial least squares algorithm. Additionally, a memory diverse ensemble paired with greedy weight updating is utilized to allow real-time model memory adjustment. We show that modeling is superior compared to other local soft sensors at statistically significant levels, and that the parameters allow enhanced data interpretation. ☐ In Chapter 7, the conclusions of the research are given, as well as numerous potential future directions.
Author: Mats Viberg Publisher: Academic Press ISBN: 0124116213 Category : Technology & Engineering Languages : en Pages : 1013
Book Description
This third volume, edited and authored by world leading experts, gives a review of the principles, methods and techniques of important and emerging research topics and technologies in array and statistical signal processing. With this reference source you will: Quickly grasp a new area of research Understand the underlying principles of a topic and its application Ascertain how a topic relates to other areas and learn of the research issues yet to be resolved Quick tutorial reviews of important and emerging topics of research in array and statistical signal processing Presents core principles and shows their application Reference content on core principles, technologies, algorithms and applications Comprehensive references to journal articles and other literature on which to build further, more specific and detailed knowledge Edited by leading people in the field who, through their reputation, have been able to commission experts to write on a particular topic
Author: Enyang Xu Publisher: ISBN: 9781267663641 Category : Languages : en Pages :
Book Description
Given the increasing number of location-based applications in wireless networks, efficient solution to the problem of source localization and tracking has become more and more important. In this dissertation, we present our works on improving source localization and tracking in wireless networks. First we investigate localization algorithms for the time difference of arrival (TDOA) measurement model. Taking into account the colored measurement noise, we adopt a minmax principle to develop semidefinite relaxation algorithms that can be reliably solved using semidefinite programming with low complexity. The reduction of algorithm complexity is achieved through a simple but effective method for reference node selection among participating measurement nodes such that only a subset of selective time-differences of signal arrival are exploited. Our estimation methods are less sensitive to the source locations and can be used either as the final location estimate or as the initial point for traditional search algorithms. We also consider the more practical time of arrival (TOA) measurement model, in which the source start transmission time is unknown. We present two new methods that utilize semidefinite programming relaxation for direct source localization. We further address the issue of robust estimation given measurement errors and inaccuracy in the locations of receiving sensors. Our results demonstrate some potential advantages of source localization based on the direct TOA data over time-difference preprocessing. Next, we investigate the emitter source tracking problem in which a mobile tracking sensor and multiple anchored sensors cooperate to track and estimate a mobile source node locations. We propose a min-max approximation approach to estimate the location for tracking which can be efficiently solved via semidefinite programming relaxation, and apply a cubic function for mobile sensor navigation. We jointly estimate the location of the mobile sensor and the target to improve the tracking accuracy. To further improve the system performance, we propose a weighted tracking algorithm by using the measurement information more efficiently. Our results demonstrate that the proposed algorithm provides good tracking performance and can quickly direct the mobile sensor to follow the mobile target. Lastly, we consider the multi source localization problem. With nknown source node index of each received signal, we need to identify the source of each received signal as well as estimate the locations. The combinational nature of the receive signal mixing and ordering makes the problem very complicated. We propose two algorithms based on semidefinite relaxation, and provide corresponding refinement method for each algorithm. In addition, we consider the localization problem given ambiguous anchor node measurement. We propose to identify the ambiguous anchor nodes based on the estimated noise amplitude. After identifying the ambiguous anchor nodes, we can remove the incorrect TOA measurements and obtain more accurate location estimation. In summary, the works presented in this dissertation provide several effective approaches to localization and tracking in wireless networks under different measurement models. The proposed algorithms improve the performance with relatively low complexity. As the demands for location based service rise, our methods will have applications in real systems.
Author: Yifan Wu
Author: Yifan Wu
Author: Seth Benton
Author: Ayoub Idelhaj
Author: Dominic V. Poerio
Author: Anshul Kant Saxena
Author: Mats Viberg
Author: Enyang Xu
Author: 
Author: Acoustical Society of America
Author: