Algorithms for propagation-aware underwater ranging and localization

Mención Internacional en el título de doctorWhile oceans occupy most of our planet, their exploration and conservation are one of the crucial research problems of modern time. Underwater localization stands among the key issues on the way to the proper inspection and monitoring of this significant part of our world. In this thesis, we investigate and tackle different challenges related to underwater ranging and localization. In particular, we focus on algorithms that consider underwater acoustic channel properties. This group of algorithms utilizes additional information about the environment and its impact on acoustic signal propagation, in order to improve the accuracy of location estimates, or to achieve a reduced complexity, or a reduced amount of resources (e.g., anchor nodes) compared to traditional algorithms. First, we tackle the problem of passive range estimation using the differences in the times of arrival of multipath replicas of a transmitted acoustic signal. This is a costand energy- effective algorithm that can be used for the localization of autonomous underwater vehicles (AUVs), and utilizes information about signal propagation. We study the accuracy of this method in the simplified case of constant sound speed profile (SSP) and compare it to a more realistic case with various non-constant SSP. We also propose an auxiliary quantity called effective sound speed. This quantity, when modeling acoustic propagation via ray models, takes into account the difference between rectilinear and non-rectilinear sound ray paths. According to our evaluation, this offers improved range estimation results with respect to standard algorithms that consider the actual value of the speed of sound. We then propose an algorithm suitable for the non-invasive tracking of AUVs or vocalizing marine animals, using only a single receiver. This algorithm evaluates the underwater acoustic channel impulse response differences induced by a diverse sea bottom profile, and proposes a computationally- and energy-efficient solution for passive localization. Finally, we propose another algorithm to solve the issue of 3D acoustic localization and tracking of marine fauna. To reach the expected degree of accuracy, more sensors are often required than are available in typical commercial off-the-shelf (COTS) phased arrays found, e.g., in ultra short baseline (USBL) systems. Direct combination of multiple COTS arrays may be constrained by array body elements, and lead to breaking the optimal array element spacing, or the desired array layout. Thus, the application of state-of-the-art direction of arrival (DoA) estimation algorithms may not be possible. We propose a solution for passive 3D localization and tracking using a wideband acoustic array of arbitrary shape, and validate the algorithm in multiple experiments, involving both active and passive targets.Part of the research in this thesis has been supported by the EU H2020 program under project SYMBIOSIS (G.A. no. 773753).This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Paul Daniel Mitchell.- Secretario: Antonio Fernández Anta.- Vocal: Santiago Zazo Bell

Model-based Sparse Component Analysis for Reverberant Speech Localization

In this paper, the problem of multiple speaker localization via speech separation based on model-based sparse recovery is studies. We compare and contrast computational sparse optimization methods incorporating harmonicity and block structures as well as autoregressive dependencies underlying spectrographic representation of speech signals. The results demonstrate the effectiveness of block sparse Bayesian learning framework incorporating autoregressive correlations to achieve a highly accurate localization performance. Furthermore, significant improvement is obtained using ad-hoc microphones for data acquisition set-up compared to the compact microphone array

Acoustic Source Localisation in constrained environments

Acoustic Source Localisation (ASL) is a problem with real-world applications across multiple domains, from smart assistants to acoustic detection and tracking. And yet, despite the level of attention in recent years, a technique for rapid and robust ASL remains elusive – not least in the constrained environments in which such techniques are most likely to be deployed. In this work, we seek to address some of these current limitations by presenting improvements to the ASL method for three commonly encountered constraints: the number and configuration of sensors; the limited signal sampling potentially available; and the nature and volume of training data required to accurately estimate Direction of Arrival (DOA) when deploying a particular supervised machine learning technique. In regard to the number and configuration of sensors, we find that accuracy can be maintained at state-of-the-art levels, Steered Response Power (SRP), while reducing computation sixfold, based on direct optimisation of well known ASL formulations. Moreover, we find that the circular microphone configuration is the least desirable as it yields the highest localisation error. In regard to signal sampling, we demonstrate that the computer vision inspired algorithm presented in this work, which extracts selected keypoints from the signal spectrogram, and uses them to select signal samples, outperforms an audio fingerprinting baseline while maintaining a compression ratio of 40:1. In regard to the training data employed in machine learning ASL techniques, we show that the use of music training data yields an improvement of 19% against a noise data baseline while maintaining accuracy using only 25% of the training data, while training with speech as opposed to noise improves DOA estimation by an average of 17%, outperforming the Generalised Cross-Correlation technique by 125% in scenarios in which the test and training acoustic environments are matched.Heriot-Watt University James Watt Scholarship (JSW) in the School of Engineering & Physical Sciences

A two phase framework for visible light-based positioning in an indoor environment: performance, latency, and illumination

Recently with the advancement of solid state lighting and the application thereof to Visible Light Communications (VLC), the concept of Visible Light Positioning (VLP) has been targeted as a very attractive indoor positioning system (IPS) due to its ubiquity, directionality, spatial reuse, and relatively high modulation bandwidth. IPSs, in general, have 4 major components (1) a modulation, (2) a multiple access scheme, (3) a channel measurement, and (4) a positioning algorithm. A number of VLP approaches have been proposed in the literature and primarily focus on a fixed combination of these elements and moreover evaluate the quality of the contribution often by accuracy or precision alone. In this dissertation, we provide a novel two-phase indoor positioning algorithmic framework that is able to increase robustness when subject to insufficient anchor luminaries and also incorporate any combination of the four major IPS components. The first phase provides robust and timely albeit less accurate positioning proximity estimates without requiring more than a single luminary anchor using time division access to On Off Keying (OOK) modulated signals while the second phase provides a more accurate, conventional, positioning estimate approach using a novel geometric constrained triangulation algorithm based on angle of arrival (AoA) measurements. However, this approach is still an application of a specific combination of IPS components. To achieve a broader impact, the framework is employed on a collection of IPS component combinations ranging from (1) pulsed modulations to multicarrier modulations, (2) time, frequency, and code division multiple access, (3) received signal strength (RSS), time of flight (ToF), and AoA, as well as (4) trilateration and triangulation positioning algorithms. Results illustrate full room positioning coverage ranging with median accuracies ranging from 3.09 cm to 12.07 cm at 50% duty cycle illumination levels. The framework further allows for duty cycle variation to include dimming modulations and results range from 3.62 cm to 13.15 cm at 20% duty cycle while 2.06 cm to 8.44 cm at a 78% duty cycle. Testbed results reinforce this frameworks applicability. Lastly, a novel latency constrained optimization algorithm can be overlaid on the two phase framework to decide when to simply use the coarse estimate or when to expend more computational resources on a potentially more accurate fine estimate. The creation of the two phase framework enables robust, illumination, latency sensitive positioning with the ability to be applied within a vast array of system deployment constraints

Energy-Efficient Self-Organization of Wireless Acoustic Sensor Networks for Ground Target Tracking

With the developments in computing and communication technologies, wireless sensor networks have become popular in wide range of application areas such as health, military, environment and habitant monitoring. Moreover, wireless acoustic sensor networks have been widely used for target tracking applications due to their passive nature, reliability and low cost. Traditionally, acoustic sensor arrays built in linear, circular or other regular shapes are used for tracking acoustic sources. The maintaining of relative geometry of the acoustic sensors in the array is vital for accurate target tracking, which greatly reduces the flexibility of the sensor network. To overcome this limitation, we propose using only a single acoustic sensor at each sensor node. This design greatly improves the flexibility of the sensor network and makes it possible to deploy the sensor network in remote or hostile regions through air-drop or other stealth approaches. Acoustic arrays are capable of performing the target localization or generating the bearing estimations on their own. However, with only a single acoustic sensor, the sensor nodes will not be able to generate such measurements. Thus, self-organization of sensor nodes into virtual arrays to perform the target localization is essential. We developed an energy-efficient and distributed self-organization algorithm for target tracking using wireless acoustic sensor networks. The major error sources of the localization process were studied, and an energy-aware node selection criterion was developed to minimize the target localization errors. Using this node selection criterion, the self-organization algorithm selects a near-optimal localization sensor group to minimize the target tracking errors. In addition, a message passing protocol was developed to implement the self-organization algorithm in a distributed manner. In order to achieve extended sensor network lifetime, energy conservation was incorporated into the self-organization algorithm by incorporating a sleep-wakeup management mechanism with a novel cross layer adaptive wakeup probability adjustment scheme. The simulation results confirm that the developed self-organization algorithm provides satisfactory target tracking performance. Moreover, the energy saving analysis confirms the effectiveness of the cross layer power management scheme in achieving extended sensor network lifetime without degrading the target tracking performance

실내 다중 음원 환경에 적용 가능한 음향 신호 처리 기법과 그 응용

학위논문(박사) -- 서울대학교대학원 : 공과대학 전기·정보공학부, 2022. 8. 김성철.최근 음향 신호 처리에 대한 연구가 증가하고 있다. 음향 신호 처리를 통해 유의미한 정보를 얻어내 유용하게 활용할 수 있기 때문이다. 따라서 본 논문에서는 실내 환경에서 취득한 소리에 적용 가능한 음향 신호 처리 기법에 관한 내용을 다룬다. 처음으로는 잔향이 높고 잡음이 많은 실내 환경에서 녹음한 음원 신호로부터 음원 위치를 추정하는 기법을 소개한다. 기존 음원 위치 추정 기법인 에너지 기반 위치 추정, 시간 지연 기반 위치 추정 및 SRP-PHAT 기반 위치추정 기법의 경우 잔향이 높아 소리가 울리는 실내 환경에 적용하면 그 정확도가 떨어진다. 반면 본 논문에서는 여러개의 마이크로 구성된 마이크 어레이로 부터 최적의 성능을 낼 수 있는 마이크의 조합을 찾아낼 수 있는 비용 함수를 새로이 정의한다. 이 비용함수 값이 최저가 되는 마이크 조합을 찾아내 해당 마이크로 음원 위치 추정을 진행한 결과 기존 기법 대비 거리 오차가 줄어든 것을 확인하였다. 다음으로는 손실이 발생한 녹음 음원에서 손실된 값을 복원하는 기법을 소개한다. 본 기법에서 목표로 삼는 음원은 여러 개의 사인파형 신호가 합쳐져서 들어오는 음원이다. 무향실에는 여러개의 음원이 존재하지만 마이크는 단 한개만 있는 상황을 가정한다. 사인 파형은 오일러 공식에 기반해 지수 함수 꼴로 변형할 수 있고, 만약 지수함수 구성 항 중 일부가 등비수열을 따르는 경우 본 논문에서 소개하는 기법을 이용해 해당 등비수열의 구성값을 구할 수 있다. 본 문제를 풀기 위해 랜덤 포크라는 개념을 새로이 도입했다. 본 기법을 이용해 신호를 복원한 결과, 신호 복원 정확도는 기존의 압축 센싱 기반 복원기법 및 DNN 기반 복원 기법보다 그 정확도가 높았다. 마지막으로 본 논문에서는 이전에 소개한 SSRF 기법을 기반으로 합쳐진 신호를 분리하는 기법을 소개한다. 본 기법에서는 이전과 같이 사인 파형의 신호가 합쳐져서 들어오는 상황을 가정한다. 거기에 더해 이전 기법에서는 모든 사인 파형이 동시에 재생되는 상황을 가정한 반면, 본 기법에서는 각기 다른 음원이 마이크로 부터 각각 다른 거리만큼 떨어져 있어서 모두 다른 시간 지연을 가지고 마이크로 도달하는 상황을 가정한다. 이렇게 서로 다른 시간지연을 갖고 하나의 마이크로 도달하는 사인파형의 신호가 합쳐진 상황에서 각각의 신호를 분리한다. 본 논문에서 소개하는 기법은 크게 음원 갯수 추정, 시간 지연 추정 및 신호 분리의 세 개 단계로 구성된다. 기존의 음향 신호 분리 기법들이 음원의 갯수에 대한 정보를 미리 알아야 한다거나, 시간지연이 없는 신호에 대해서만 적용이 가능했다면, 본 기법은 사전에 음원 갯수에 대한 정보가 없어도 적용 가능하다는 장점이 있다. 해당 기법은 SSRF 기법을 기반으로 하는데, SSRF 문제를 푸는 과정에서 구해지는 방정식의 계수 값이 변하는 지점을 시간 지연으로 추정한다. 그리고 시간 지연 값의 변화가 몇 번 발생하는가에 따라 음원의 갯수를 추정한다. 마지막으로 모든 신호가 합쳐진 최종 구간에서 SSRF 문제를 풀어 개별 신호를 구성하는 값을 구해내 신호 분리를 완료한다. 본 기법은 여러 가정이 필요한 기존의 ICA 기반 음향 신호 분리 및 YG 음향 신호 분리에 비해 더 정확한 신호분리 결과를 내는 것을 확인하였다.Recently, research on acoustic signal processing is increasing. This is because meaningful information can be obtained and utilized usefully from acoustic signal processing. Therefore, this paper deals with the acoustic signal processing techniques for sound recorded in the indoor environment. First, we introduce a method for estimating the location of a sound source under indoor environment where there are high reverberation and lots of noise. In the case of existing methods such as interaural level difference (ILD) based localization, time difference of arrival (TDoA) based localization, and steered response power phase transformation (SRP-PHAT) based localization, the accuracy is lowered when applied under recordings from indoor environment with high reverberation. However in this paper, we define a new cost function that can find an optimal combination of microphone pair which results in highest performance. The microphone pair with the lowest value of cost function was chosen as an optimal pair, and the source location was estimated with the optimal microphone pair. It was confirmed that the distance error was reduced compared to existing methods. Next, a technique for recovering the lost sample value from the recorded signal called sketching and stacking with random fork (SSRF) is introduced. In this technique, the target sound source is a superposition of several sinusoidal signals. It is assumed that there are multiple sound sources in the anechoic chamber, but there is only one microphone. It is trivial that a sinusiodal wave can be transformed into an exponential function based on Euler's formula. If some of the terms of the exponential function follow a geometric sequence, those values can be obtained using SSRF. To solve this problem, the concept of a random fork is newly introduced. Comparing the recovery error based on SSRF with existing methods such as compressive sensing based technique and deep neural network (DNN) based technique, the accuracy of SSRF based signal recovery was higher. Finally, this paper introduces a blind source separation (BSS) technique for based on the previously introduced SSRF technique. In this technique, as before, it is assumed that the sinusoidal waves are superposed. In addition, while the previous technique assumed a situation where all sinusoidal waves were emitted simultaneously, this technique assumed a situation where different sound sources were separated by different distances from the microphone and arrived at the microphone with different time delays. Under these assumptions, a new BSS method for separating single signals from the mixture based on SSRF is introduced. The SSRF BSS is mainly composed of three steps: estimation of the number of sound sources, estimation of time delay, and signal separation. While the existing BSS methods require information on the source number to be known a priori, SSRF BSS does not require source number. Whereas existing BSS methods can only be applied to signals without time delay, SSRF BSS method has the advantage in that it can be applied to the mixture of signals with different time delays. It was confirmed that SSRF BSS produces more accurate separation results compared to the existing independent component analysis (ICA) BSS and Yu Gang (YG) BSS.1 INTRODUCTION 2 IMPROVING ACOUSTIC LOCALIZATION PERFORMANCE BY FINDING OPTIMAL PAIR OF MICROPHONES BASED ON COST FUNCTION 5 2.1 Motivation 5 2.2 Conventional Acoustic Localization Methods 8 2.2.1 Interaural Level Difference 8 2.2.2 Time Difference of Arrival 12 2.2.3 Steered Response Power Phase Transformation 14 2.3 System Model 17 2.3.1 Experimental Scenarios 17 2.3.2 Definition of Cost Function 18 2.4 Results and Discussion 20 2.5 Summary 22 3 ACOUSTIC SIGNAL RECOVERY BASED ON SKETCHING AND STACKING WITH RANDOM FORK 24 3.1 Motivation 24 3.2 SSRF Signal Model 26 3.2.1 Source Signal Model 26 3.2.2 Sampled Signal Model 26 3.2.3 Corrupted Signal Model 27 3.3 SSRF Problem Statement 28 3.4 SSRF Methodology 28 3.4.1 Geometric Sequential Representation 29 3.4.2 Definition of Random Fork 30 3.4.3 Informative Matrix 31 3.4.4 Data Augmentation 32 3.4.5 Solution of SSRF Problem 33 3.4.6 Reconstruction of Corrupted Samples 37 3.5 Performance Analysis 37 3.5.1 Simulation Set-up 37 3.5.2 Reconstruction Error According to Bernoulli Parameter and Number of Signals 38 3.5.3 Detailed Comparison between SSRF and DNN 40 3.5.4 SSRF Result for Signal with Additive White Gaussian Noise 42 3.6 Summary 43 4 SINGLE CHANNEL ACOUSTIC SOURCE NUMBER ESTIMATION AND BLIND SOURCE SEPARATION BASED ON SKETCHING AND STACKING WITH RANDOM FORK 44 4.1 Motivation 44 4.2 SSRF based BSS System Model 48 4.2.1 Simulation Scenarios 48 4.3 SSRF based BSS Methodology 52 4.3.1 Source Number and ToA Estimation based on SSRF 52 4.3.2 Signal Separation 55 4.4 Results and Discussion 57 4.4.1 Source Number and ToA Estimation Results 57 4.4.2 Separation of the Signal 59 4.5 Summary 61 5 CONCLUSION 64 Abstract (In Korean) 75박