압축센싱 기반 접근법을 이용한 수중음향 소음원의 위치 추정 기법 연구

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 조선해양공학과, 2021. 2. 성우제.삼차원 음향 소음원의 위치추정은 잠수체, 산란체, 캐비테이션 소음원의 분석을 위해 필수적인 과정이다. 전통적인 빔형성 기법은 강인한 위치 추정 결과를 제공하나, 하나의 소음원의 위치만을 구분할 수 있는 저해상도의 결과를 보인다. 고해상도의 위치 추정 결과를 얻기 위해 최근 압축센싱 기반의 위치 추정 기법들이 사용되어 지고 있다. 압축센싱 기법은 희소성을 가진 신호의 획득,처리,복원에 효과적인 방법이며 영상처리, 수중음향, 최적화 문제 등에서 널리 활용되어지고 있다. 수중 소음원의 위치 추정을 위하여 압축센싱 기밥의 기법들이 적용되어 왔으며 전통적인 빔형성 기법에 비하여 해상도 측면에서 더 나은 성능을 보여주었다. 하지만 이러한 해상도 측면의 성능 향상에도 불구하고 압축센싱 기반의 방법은 여전히 문제점들을 가지고 있다. 첫번째, 압축센싱 기법은 전통적인 빔형성 기법에 비해 수치 연산 과정이 불안전성을 가진다. 비록 고해상도의 성능을 보여주나 압축센싱 기법은 수치해석 과정에서 불안정한 모습을 보여주며, 안정적인 복원을 저해한다. 두번째, 기저불일치로 인한 오차가 정확한 소음원의 위치 추정을 저해한다. 게다가 3차원 소음원의 위치 추정 문제는 이러한 기저 불일치를 해결할 수 있는 기법이 아직까지 개발되지 못하였다. 본 논문은 기존의 압축센싱 기반의 위치 추정 기법이 가지는 문제점을 파악하고 3차원 위치 추정 문제를 다룰 수 있는 향상된 압축센싱 기법을 소개한다. 탐색 공간 사이의 높은 상관관계로 인하여 발생하는 해의 불안정성을 해결아기 위하여 ``다중주파수 상관 처리기법"을 소개하고, 3차원 위치 추정문제에서 기저불일치 문제를 해결할 수 있는 ``유동 탐색 격자 기법"을 소개한다. 제안된 기법은 전통적인 빔형성 기법에 비하여 정확한 위치 추정 결과를 제공하며 실험 데이터를 통한 위치 추정결과를 이용하여 이러한 주장을 뒷받침하였다. 본 연구에서는 수중음향 소음원의 3차원 위치 추정 문제를 중점적으로 다루었으나, 제안된 기법은 소나 및 레이더, 음향 소음원 위치 추정 문제에도 효과적으로 적용될 수 있을 것으로 기대된다.Three-dimensional acoustic localization is an essential process to analyze the underwater sound sources such as submarine, scatterer, marine cavitation. Traditional beamforming processors provide robust localization results, however, the results show a low-resolution result which only reveals one dominant source location. In order to obtain the high resolution localization results, compressive sensing(CS) based approaches have been used recently. CS technique is an effective way for acquiring, processing, reconstructing the sparse signal and has wide applicability to many research fields such as image processing, underwater acoustics and optimization problems. For localizing the underwater acoustic sources, CS-based approaches have been adopted in many research fields and have shown better localization performance compared to the traditional beamforming processors in terms of resolution. Despite the performance improvement in resolution, there are still problems that need to be resolved when using the CS-based method. First, the CS-based method does not appear to be robust compared with the traditional beamforming processors. CS-based method provides high-resolution results, however, it suffers from computational instability which hinders the stable reconstruction. Second, basis mismatch error hindrances estimating the exact source locations. Moreover, there is no basis mismatch estimation technique applicable to 3D source localization problem. This dissertation points out the limitation of conventional CS-based localization method and introduces the advanced CS-based localization method which deals with 3D source localization problem. The ``coherent multiple-frequency processing" is introduced to overcome the instability of solution induced by high correlation of spatial grids and ``flexible searching-grid technique" is introduced to solve the basis mismatch problem which is developed for 3D source localization problem. The suggested techniques provide more accurate localization results compared to traditional beamforming processors or conventional CS-based beamforming processors and the arguments are backed with actual experimental data which was conducted in a cavitation tunnel. Though underwater acoustic source localization problems are presented in this dissertation, the proposed technique can be extended to many research fields, such as radar detection, sonar detection, ultrasound imaging.1 Introduction 2 1.1 Issue 1 : Computational Stability 4 1.2 Issue 2 : Basis Mismatch 5 1.3 Organization of the Dissertation 5 2 CS techniques for three-dimensional source localization 9 2.1 Compressive Sensing (CS) 9 2.2 Block-Sparse Compressive Sensing (BSCS) 11 2.3 Sparse Bayesian learning (SBL) 12 2.4 Off-Grid Sparse Bayesian Inference (OGSBI) 14 3 3D CS-based source localization method using multiple-frequency components 18 3.1 Introduction 18 3.2 Block-sparse Compressive Sensing for Incipient Tip Vortex Cavitation Localization 24 3.2.1 System framework for incipient tip vortex cavitation localization 24 3.2.2 Incoherent multiple-frequency localization with compressive sensing 26 3.2.3 Coherent multiple-frequency localization with block-sparse compressive sensing 28 3.3 Localization Results for Incipient TVC 32 3.3.1 Transducer source experiment 33 3.3.2 Incipient TVC Noise Source Experiment 36 3.4 Conclusion 41 3.5 Acknowledgments 43 4 3D CS-based source localization method by reducing the basis mismatch error 48 4.1 Introduction 48 4.2 Off grid system framework for 3D source localization 50 4.2.1 System framework for 3-dimensional off gird source localization 50 4.2.2 Coherent multiple-frequency localization with block-sparse Bayesian learning technique 53 4.2.3 3-dimensional off grid source localization method 55 4.3 Simulation and Experiment Results 62 4.4 Conclusion 65 5 Summary 70 Abstract (In Korean) 73Docto

Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version

Sparse Bayesian information filters for localization and mapping

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2008This thesis formulates an estimation framework for Simultaneous Localization and Mapping (SLAM) that addresses the problem of scalability in large environments. We describe an estimation-theoretic algorithm that achieves significant gains in computational efficiency while maintaining consistent estimates for the vehicle pose and the map of the environment. We specifically address the feature-based SLAM problem in which the robot represents the environment as a collection of landmarks. The thesis takes a Bayesian approach whereby we maintain a joint posterior over the vehicle pose and feature states, conditioned upon measurement data. We model the distribution as Gaussian and parametrize the posterior in the canonical form, in terms of the information (inverse covariance) matrix. When sparse, this representation is amenable to computationally efficient Bayesian SLAM filtering. However, while a large majority of the elements within the normalized information matrix are very small in magnitude, it is fully populated nonetheless. Recent feature-based SLAM filters achieve the scalability benefits of a sparse parametrization by explicitly pruning these weak links in an effort to enforce sparsity. We analyze one such algorithm, the Sparse Extended Information Filter (SEIF), which has laid much of the groundwork concerning the computational benefits of the sparse canonical form. The thesis performs a detailed analysis of the process by which the SEIF approximates the sparsity of the information matrix and reveals key insights into the consequences of different sparsification strategies. We demonstrate that the SEIF yields a sparse approximation to the posterior that is inconsistent, suffering from exaggerated confidence estimates. This overconfidence has detrimental effects on important aspects of the SLAM process and affects the higher level goal of producing accurate maps for subsequent localization and path planning. This thesis proposes an alternative scalable filter that maintains sparsity while preserving the consistency of the distribution. We leverage insights into the natural structure of the feature-based canonical parametrization and derive a method that actively maintains an exactly sparse posterior. Our algorithm exploits the structure of the parametrization to achieve gains in efficiency, with a computational cost that scales linearly with the size of the map. Unlike similar techniques that sacrifice consistency for improved scalability, our algorithm performs inference over a posterior that is conservative relative to the nominal Gaussian distribution. Consequently, we preserve the consistency of the pose and map estimates and avoid the effects of an overconfident posterior. We demonstrate our filter alongside the SEIF and the standard EKF both in simulation as well as on two real-world datasets. While we maintain the computational advantages of an exactly sparse representation, the results show convincingly that our method yields conservative estimates for the robot pose and map that are nearly identical to those of the original Gaussian distribution as produced by the EKF, but at much less computational expense. The thesis concludes with an extension of our SLAM filter to a complex underwater environment. We describe a systems-level framework for localization and mapping relative to a ship hull with an Autonomous Underwater Vehicle (AUV) equipped with a forward-looking sonar. The approach utilizes our filter to fuse measurements of vehicle attitude and motion from onboard sensors with data from sonar images of the hull. We employ the system to perform three-dimensional, 6-DOF SLAM on a ship hull

AUV SLAM and experiments using a mechanical scanning forward-looking sonar

Navigation technology is one of the most important challenges in the applications of autonomous underwater vehicles (AUVs) which navigate in the complex undersea environment. The ability of localizing a robot and accurately mapping its surroundings simultaneously, namely the simultaneous localization and mapping (SLAM) problem, is a key prerequisite of truly autonomous robots. In this paper, a modified-FastSLAM algorithm is proposed and used in the navigation for our C-Ranger research platform, an open-frame AUV. A mechanical scanning imaging sonar is chosen as the active sensor for the AUV. The modified-FastSLAM implements the update relying on the on-board sensors of C-Ranger. On the other hand, the algorithm employs the data association which combines the single particle maximum likelihood method with modified negative evidence method, and uses the rank-based resampling to overcome the particle depletion problem. In order to verify the feasibility of the proposed methods, both simulation experiments and sea trials for C-Ranger are conducted. The experimental results show the modified-FastSLAM employed for the navigation of the C-Ranger AUV is much more effective and accurate compared with the traditional methods