Adaptive Feature Representation To Improve Interpret And Accelerate Channel Estimation And Prediction For Shallow Water Acoustic Environments
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| Author | : Ryan A. McCarthy (PhD) |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2021 |
| Genre | : Underwater acoustics |
| ISBN | : |
In my doctoral dissertation I investigate new approaches to real-time channel estimation of underwater acoustic communications that complement existing estimation techniques. Modified sparse optimization algorithms have been used to improve channel estimation with some success. This work aims to improve these algorithms by applying pattern recognition through adaptive signal processing and machine learning to accelerate estimation time. Specifically, it investigates a model-agnostic geometric feature morphology based on braid theory to interpret diverse channel phenomena. The computational goal is to detect, separate and interpret multipath features in the channel delay spread across time, frequency, and varying degrees of channel sparsity. The main contribution of the thesis is development of braids feature representations and related channel tracking and learning algorithms to track salient bands of multipath activity. We develop robust signal processing and braided feature engineering approaches that evolve dynamically to the fluctuating channel multipath activity. To test the hypothesis that braids can track and adapt to diverse activity developing within the channel, simulated shallow water environments created through the well-known BELLHOP model and data from the SPACE08 field experiment are examined. Several simulated shallow water environments are examined with additive white Gaussian noise and varying degrees of activity to evaluate the performance of braiding and machine learning for shallow water acoustic channel estimation and interpretation. Performance is evaluated through visual confirmation and ground truths are provided by BELLHOP's outputs (e.g. eigenrays, arrivals, etc.). Results show that braids can evolve to capture dynamically changing multipath scattering activity in the shallow water acoustic channel. Furthermore, we demonstrate that leveraging braid feature representations with acoustic physics propagation models can successfully predict the number of reflectors in active channel multipath. We also demonstrate the significance of braid manifold representation in improving the computational speed for channel estimation. On average, this technique has improved estimation speed by ~.02 seconds as compared to the existing estimation techniques. These results suggest that braids can be used for useful pattern recognition to bridge the gap between purely statistical data analysis and physics-driven interpretation of the ocean acoustics that create the multipath channel delay spread. Beyond underwater acoustics, these feature learning techniques are broadly applicable to any paradigms where spectral features may evolve and intersect.
| Author | : Yi Zhang |
| Publisher | : |
| Total Pages | : |
| Release | : 2017 |
| Genre | : |
| ISBN | : |
This thesis develops approaches for estimating and predicting sparse shallow-water acoustic communication channels. The broadband shallow-water channel has three characterizations: a large dimension of channel impulse response caused by excessively long delay spread, fast temporal variability induced by scattering from the moving sea surface, and a sparse channel structure due to the resolvable paths. Traditional least square estimation techniques fail to utilize the sparse channel structure, and suffer from the limitations on the capability of estimating large-dimensional channels with rapid fluctuations. Compressed sensing also known as compressive sensing (CS), has been intensively studied recently. It has been applied in various areas such as imaging, radar, speech recognition, and data acquisition. Recently, applying CS to sparse channel estimation has been largely accepted. This thesis details the application of CS to sparse estimation of both time-invariant and time-varying shallow-water acoustic channels. Specifically, various reconstruction algorithms are used to find the sparse channel coefficients. However, a priori knowledge of channel sparsity is often not available in practice. The first part of the thesis proposes an improved greedy pursuit algorithm which iteratively identifies the sparse channel coefficients without requiring a priori knowledge of channel sparsity. Then, the proposed algorithm is employed to estimate both time-invariant and time-varying sparse channels. In addition, a comparative study of the state-of-the-art of various CS-based signal reconstruction algorithms is performed to gain better understanding of the mathematical insights. Furthermore, based on CS theory, different pilot placement choices will directly affect the performance of the channel estimation algorithm. The second part of the thesis investigates the pilot pattern design in sparse channel estimation. Unlike the equally spaced pilots for conventional channel estimation, randomly placed pilot tones are most used in existing CS-based channel estimation methods. In order to improve the efficiency of the optimal pilot pattern searching, a novel pilot pattern selection scheme is proposed based on the concatenated cyclic difference set. The performance of the proposed design is also compared with the existing search-based pilot placement methods. It should be noted that the proposed reconstruction algorithm and the pilot placement scheme are not restricted to underwater acoustic communication systems, but they can be applied so sparse channel estimation in other communication systems. Finally, an outdated channel estimation will lead to severe performance degradation when the channel varies rapidly. Hence, to predict future channel state information, an efficient sparse channel prediction scheme is proposed which does not require any statistical a priori knowledge of channels and noise. A receiver structure which combines a sparse channel estimator and a decision feedback based adaptive channel predictor is developed to further improve the prediction accuracy.Simulation results are shown to demonstrate the performance of the proposed algorithms and schemes. The study of this thesis contributes to a better understanding of the channel physical constraints on algorithm design and potential performance improvement.
| Author | : H.G. Urban |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 763 |
| Release | : 2012-12-06 |
| Genre | : Science |
| ISBN | : 9400953615 |
The NATO Advanced Study Institute on Adaptive Methods in Underwater Acoustics was held on 30 July - 10 August 1984 in LLineburg, Germany. The Institute was primarily concerned with signal processing for underwater appl ica tions. The majority of the presentations, when taken together, yield a definite picture of the present status of understanding of adaptive and high resolution processing, setting out the progress achieved over the past four years together with the major problem areas remaining. Major effort was made to obtain a commensurate contribution of tutorial and advanced research papers. It is my hope that the material in this volume may be equally well suited for students getting an introduction to some of the basic problems in underwater signal processing and for the professionals who may obtain an up-to-date overview of the present state of the art. This might be especially useful in view of the controversy and lack of adequate interrelationships which have marked this rapidly expanding field in the past. Practical reinforcement of this picture is provided by the material concerning digital and optical processing technology, giving some guidance to achievable adaptive and high resolution techniques with current processing devices. The formal programme was extended and detailed by a series of six evening work shops on specific topics, during which informal discussions took place among the participants. Summaries of these workshops are also included in these Proceedings.
| Author | : Yonglin Zhang |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2022 |
| Genre | : |
| ISBN | : |
The ocean covers more than 70% of the earth, which provides rich biological, chemical, mineral and space resources for the development of human civilization. The cause of managing the ocean is of strategic importance to our economic development and national security. Acoustic wave is the most widely used and mature underwater information transmission carrier known to mankind. Underwater acoustic (UWA) communication technology is one of the main technical supports to carry out various marine activities, but it is challenged by the complex marine environment, specifically in terms of propagation loss, UWA environmental noise, multipath propagation characteristics, Doppler expansion, spatial and temporal variation effects and other scientific issues, which restricts the improvements of the bit error rate (BER) performance, communication rate, communication distance, robustness and other indicators. The current level of development of UWA communication technology is difficult to fully meet the needs of practical applications.Channel estimation is an effective technical means to solve the problems of multipath effect and temporal- spatial variation characteristics. Recent breakthroughs in adaptive methods and machine learning in various fields have brought new opportunities for the further development of UWA channel estimation technology, but also raised new technical problems, such as the use of channel structure characteristics, sample scarcity training, label missing training, domain mismatch caused by environmental changes. These problems make the effectiveness and applicability of the new method seriously restricted, and it is difficult to bring out the proper information sensing ability.Based on the frontier of intelligent ocean and marine information science, this thesis focuses on the scientific problems of UWA communication in complex marine environment according to the development needs of national marine strategy, aims at the key technical problems faced by adaptive and machine learning channel estimation methods, such as analysis of channel cluster sparsity characteristics, limited data, label missing, domain mismatch, etc., and introduces optimization methods, neural network model design and analysis, data augmentation methods, transfer learning and other recent academic results. We have explored the mechanism of adaptive and machine learning based channel estimation methods and finally proposed a series of new methods for channel estimation based on adaptive signal processing and machine learning.
| Author | : Andrea Caiti |
| Publisher | : Springer |
| Total Pages | : 332 |
| Release | : 2017-04-30 |
| Genre | : Science |
| ISBN | : 9789402404616 |
Thisvolume contains thecollection of papers from the second workshop on Expe- mental Acoustic Inversion Techniques for Exploration of theShallow Water Environment. Theworkshopthemefollowedtheoriginalconceptofthe rstworkshop, heldinCarvoeiro, Portugal, in 1999, i.e., to focus on experiments and experimental techniques for acoustic sensing in the shallow ocean. More than forty leading international scientists were invited to meet in the picturesque town of St. Angelo on the island of Ischia, in June 2004, to discuss progress in the application of new experimental techniques for exploration and assessment of shallowwater environments. Acoustic techniques provide the most effective means for remote sensing of ocean and sea oor processes, and for probing the structure beneath the sea oor. No other energy propagates as ef ciently in the ocean: radio waves and visible light are severely limited in range because the ocean is a highly conductive medium. However, sound from bre- ing waves and coastal shipping can be heard throughout the ocean, and marine mammals communicate acoustically over basin scale distances.
| Author | : Samar Kaddouri |
| Publisher | : |
| Total Pages | : 120 |
| Release | : 2015 |
| Genre | : Adaptive signal processing |
| ISBN | : |
The underwater channel poses numerous challenges for acoustic communication. Acoustic waves suffer long propagation delay, multipath, fading, and potentially high spatial and temporal variability. In addition, there is no typical underwater acoustic channel; every body of water exhibits quantiably different properties. Underwater acoustic modems are traditionally operated at low frequencies. However, the use of broadband, high frequency communication is a good alternative because of the lower background noise compared to low-frequencies, considerably larger bandwidth and better source transducer efficiency. One of the biggest problems in the underwater acoustic communications at high frequencies is time-selective fading, resulting in the Doppler spread. While many Doppler detection, estimation and compensation techniques can be found in literature, the applications are limited to systems operating at low frequencies contained within frequencies ranging from a few hundred Hertz to around 30 kHz. This dissertation proposes two robust channel estimation techniques for simultaneous transmissions using multiple sources and multiple receivers (MIMO) that closely follows the rapidly time-varying nature of the underwater channel. The first method is a trended least square (LS) estimation that combines the traditional LS method with an empirical modal decomposition (EMD) based trend extraction algorithm. This method allows separating the slow fading modes in the MIMO channels from the fast-fading ones and thus achieves a close tracking of the channel impulse response time fluctuations. This dissertation also outlines a time-varying underwater channel estimation method based on the channel sparsity characteristic. The sparsity of the underwater communication channel is exploited by using the MIMO P-iterative greedy orthogonal matching pursuit (MIMO-OMP) algorithm for the channel estimation. Both techniques are demonstrated in a fully controlled environment, using simulated and experimental data. To test the proposed channel estimation techniques, an acoustic model for a MIMO channel is developed using the method of images applied to a completely closed three-dimensional duct with a pressure release surface boundary and five rigid walls. The MIMO simulated channel provides the strength and delay of all echoes forming the channel. Both simulation and experimental results show a signicant improvement in the estimation of the channel impulse response, thus validating the two proposed algorithms.
| Author | : |
| Publisher | : |
| Total Pages | : 23 |
| Release | : 2004 |
| Genre | : |
| ISBN | : |
Channel-estimate-based equalizers are adaptive coherent equalizers for which observations of the received signal are used to estimate channel parameters and these estimates are used to calculate the equalizer filter weights. Traditional channel-estimate- based equalizers calculate filter weights assuming that the estimates of the channel parameters are perfect. This work presents a common framework for evaluating both the performance of channel-estimate- based equalizers when the channel estimates are perfect (i.e. the minimal achievable error of the equalizer) and the degradation in performance of these equalizers due to errors in the channel estimates (i.e. the excess error). For the three type of equalizers considered (DFE Linear MMSE and Passive Time Reversal) the expressions for minimal achievable error take the form of the results from classical estimation theory for estimation error achieved by MMSE and matched filter estimators. These expressions are interpreted to give insights into the characteristics of "good" and "bad" channels. For the case when the channel estimates are MMSE estimates of the channel-impulse response, the excess error is shown to be proportional to the 2-norm of the calculated feedforward filter weight vector of the equalizer. This result is analogous to the "white noise gain" result characterizing the sensitivity of adaptive array processors to mismatch. This result is used to evaluate the relative sensitivity of all three types of equalizers to environmental mismatch. The analytic predictions of equalizer performance are compared with observed performance using data from several field experiments in different underwater acoustic environments. The expressions for minimal achievable error and excess error give insights into potential methods of improving the robustness to channel mismatch of adaptive equalizers such as the DFE. Several of these methods are implemented and evaluated.
| Author | : Ballard Justin Smith Blair |
| Publisher | : |
| Total Pages | : 215 |
| Release | : 2011 |
| Genre | : Acoustic models |
| ISBN | : |
Underwater wireless communication is quickly becoming a necessity for applications in ocean science, defense, and homeland security. Acoustics remains the only practical means of accomplishing long-range communication in the ocean. The acoustic communication channel is fraught with difficulties including limited available bandwidth, long delay-spread, time-variability, and Doppler spreading. These difficulties reduce the reliability of the communication system and make high data-rate communication challenging. Adaptive decision feedback equalization is a common method to compensate for distortions introduced by the underwater acoustic channel. Limited work has been done thus far to introduce the physics of the underwater channel into improving and better understanding the operation of a decision feedback equalizer. This thesis examines how to use physical models to improve the reliability and reduce the computational complexity of the decision feedback equalizer. The specific topics covered by this work are: how to handle channel estimation errors for the time varying channel, how to use angular constraints imposed by the environment into an array receiver, what happens when there is a mismatch between the true channel order and the estimated channel order, and why there is a performance difference between the direct adaptation and channel estimation based methods for computing the equalizer coefficients. For each of these topics, algorithms are provided that help create a more robust equalizer with lower computational complexity for the underwater channel.
| Author | : |
| Publisher | : |
| Total Pages | : |
| Release | : 2019 |
| Genre | : |
| ISBN | : |
Abstract : Influenced by environmental conditions, underwater acoustic (UWA) communication channels exhibit spatial and temporal variations, posing significant challenges for UWA networking and applications. This dissertation develops statistical signal processing approaches to model and predict variations of the channel and relevant environmental factors. Firstly, extensive field experiments are conducted in the Great Lakes region. Three types of the freshwater river/lake acoustic channels are characterized in the aspects of statistical channel variations and sound propagation loss, including stationary, mobile and under-ice acoustic channels. Statistical data analysis shows that relative to oceanic channels, freshwater river/lake channels have larger temporal coherence, higher correlation among densely distributed channel paths, and less sound absorption loss. Moreover, variations of the under-ice channels are less severe than those in open water in terms of multipath structure and Doppler effect. Based on the observed channel characteristics, insights on acoustic transceiver design are provided, and the following two works are developed. online modeling and prediction of slowly-varying channel parameters are investigated, by exploiting their inherent temporal correlation and correlation with water environment. The temporal evolution of the channel statistics is modeled as the summation of a time-varying environmental process, and a Markov latent process representing unknown or unmeasurable physical mechanisms. An algorithm is developed to recursively estimate the unknown model parameters and predict the channel parameter of interest. The above model and the recursive algorithm are further extended to the channel that exhibits periodic dynamics. The proposed models and algorithms are evaluated via extensive simulations and data sets from two shallow-water experiments. The experimental results reveal that the average channel-gain-to-noise-power ratio, the fast fading statistics, and the average delay spread can be well predicted. The inhomogeneity of the sound speed distribution is challenging for Autonomous underwater vehicles (AUVs) communications and acoustic signaling-based AUV localization due to the refraction effect. Based on the time-of-flight (TOF) measurements among the AUVs, a distributed and cooperative algorithm is developed for joint sound speed estimation and AUV tracking. The joint probability distribution of the time-of-flight (TOF) measurements, the sound speed parameters and the AUV locations are represented by a factor graph, based on which a Gaussian message passing algorithm is proposed after the linearization of nonlinear measurement models. Simulation results show that the AUV locations and the sound speed parameters can be tracked with satisfying accuracy. Moreover, significant localization improvement can be achieved when the sound speed stratification effect is taken into consideration.
| Author | : Jie Yang |
| Publisher | : |
| Total Pages | : 160 |
| Release | : 2007 |
| Genre | : |
| ISBN | : 9781109992212 |
In shallow water environments, sound propagation experiences multiple interactions with the surface/bottom interfaces, with hydrodynamic disturbances such as internal waves, and with tides and fronts. It is thus very difficult to make satisfactory predictions of sound propagation in shallow water. Given that many of the ocean characteristics can be modeled as stochastic processes, the statistical measure, spatial coherence, is consequently an important quantity. Spatial coherence provides valuable information for array performance predictions. However, for the case of long-range, low frequency propagation, studies of spatial coherence influenced by various environmental parameters are limited insofar as having the appropriate environmental data with which to model and interpret the results.
