Optimal dictionary learning with application to underwater target detection from synthetic aperture sonar imagery

2014 Spring.Includes bibliographical references.K-SVD is a relatively new method used to create a dictionary matrix that best ts a set of training data vectors formed with the intent of using it for sparse representation of a data vector. K-SVD is flexible in that it can be used in conjunction with any preferred pursuit method of sparse coding including the orthogonal matching pursuit (OMP) method considered in this thesis. Using adaptive lter theory, a new fast OMP method has been proposed to reduce the computational time of the sparse pursuit phase of K-SVD as well as during on-line implementation without sacrificing the accuracy of the sparse pursuit method. Due to the matrix inversion required in the standard OMP, the amount of time required to sparsely represent a signal grows quickly as the sparsity restriction is relaxed. The speed up in the proposed method was accomplished by replacing this computationally demanding matrix inversion with a series of recursive "time-order" update equations by using orthogonal projection updating used in adaptive filter theory. The geometric perspective of this new learning is also provided. Additionally, a recursive method for faster dictionary learning is also discussed which can be used instead of the singular value decomposition (SVD) process in the K-SVD method. A significant bottleneck in K-SVD is the computation of the SVD of the reduced error matrix during the update of each dictionary atom. The SVD operation is replaced with an efficient recursive update which will allow limited in-situ learning to update dictionaries as the system is exposed to new signals. Further, structured data formatting has allowed a multi-channel extension of K-SVD to merge multiple data sources into a single dictionary capable of creating a single sparse vector representing a variety of multi-channel data. Another contribution of this work is the application of the developed methods to an underwater target detection problem using coregistered dual-channel (namely broadband and high-frequency) side-scan sonar imagery data. Here, K-SVD is used to create a more optimal dictionary in the sense of reconstructing target and non-target image snippets using their respective dictionaries. The ratio of the reconstruction errors is used as a likelihood ratio for target detection. The proposed methods were then applied and benchmarked against other detection methods for detecting mine-like objects from two dual-channel sonar datasets. Comparison of the results in terms of receiver operating characteristic (ROC) curve indicates that the dual-channel K-SVD based detector provides a detection rate of PD = 99% and false alarms rate of PFA = 1% on the first dataset, and PD = 95% and PFA = 5% on the second dataset at the knee point of the ROC. The single-channel K-SVD based detector on the other hand, provides PD = 96% and PFA = 4% on the first dataset, and PD = 96% and PFA = 4% on the second dataset at the knee point of the ROC. The degradation in performance for the second dataset is attributed to the fact that the system was trained on a limited number of samples from the first dataset. The coherence-based detector provides PD = 87% and PFA = 13% on the first dataset and PD = 86% and PFA = 14% on the second dataset. These results show excellent performance of the proposed dictionary learning and sparse coding methods for underwater target detection using both dual-channel sonar imagery

Underwater target detection using multichannel subband adaptive filtering and high-order correlation schemes

Includes bibliographical references.In this paper, new pre- and post-processing schemes are developed to process shallow-water sonar data to improve the accuracy of target detection. A multichannel subband adaptive filtering is applied to preprocess the data in order to isolate the potential target returns from the acoustic backscattered signals and improve the signal-to-reverberation ratio. This is done by estimating the time delays associated with the reflections in different subbands. The preprocessed results are then beamformed to generate an image for each ping of the sonar. The testing results on both the simulated and real data revealed the efficiency of this scheme in time-delay estimation and its capability in removing most of the competing reverberations and noise. To improve detection rate while significantly minimizing the incident of false detections, a high-order correlation (HOC) method for postprocessing the beamformed images is then developed. This method determines the consistency in occurrence of the target returns in several consecutive pings. The application of the HOC process to the real beamformed sonar data showed the ability of this method for removing the clutter and at the same time boosting the target returns in several consecutive pings. The algorithm is simple, fast, and easy to implement.This work was supported by the Office of Naval Research (ONR 321TS) under Contract N61331-94-K-0018

Interferometric Synthetic Aperture Sonar Signal Processing for Autonomous Underwater Vehicles Operating Shallow Water

The goal of the research was to develop best practices for image signal processing method for InSAS systems for bathymetric height determination. Improvements over existing techniques comes from the fusion of Chirp-Scaling a phase preserving beamforming techniques to form a SAS image, an interferometric Vernier method to unwrap the phase; and confirming the direction of arrival with the MUltiple SIgnal Channel (MUSIC) estimation technique. The fusion of Chirp-Scaling, Vernier, and MUSIC lead to the stability in the bathymetric height measurement, and improvements in resolution. This method is computationally faster, and used less memory then existing techniques

Underwater target detection using multiple disparate sonar platforms

2010 Fall.Includes bibliographical references.The detection of underwater objects from sonar imagery presents a difficult problem due to various factors such as variations in the operating and environmental conditions, presence of spatially varying clutter, and variations in target shapes, compositions, and orientation. Additionally, collecting data from multiple platforms can present more challenging questions such as "how should I collaboratively perform detection to achieve optimal performance?", "how many platforms should be employed?", "when do we reach a point of diminishing return when adding platforms?", or more importantly "when does adding an additional platform not help at all?". To perform multi-platform detection and answer these questions we use the coherent information among all disparate sources of information and perform detection on the premise that the amount of coherent information will be greater in situations where a target is present in a region of interest within an image versus a situation where our observation strictly consists of background clutter. To exploit the coherent information among the different sources, we recast the standard Neyman-Pearson, Gauss-Gauss detector into the Multi-Channel Coherence Analysis (MCA) framework. The MCA framework allows one to optimally decompose the multi-channel data into a new appropriate coordinate system in order to analyze their linear dependence or coherence in a more meaningful fashion. To do this, new expressions for the log-likelihood ratio and J-divergence are formulated in this multichannel coordinate system. Using the MCA framework, the data of each channel is first whitened individually, hence removing the second-order information from each channel. Then, a set of linear mapping matrices are obtained which maximizes the sum of the cross-correlations among the channels in the mapped domain. To perform detection in the coordinate system provided by MCA, we first of all construct a model suited to this multiple sensor platform problem and subsequently represent observations in their MCA coordinates associated with the H1 hypothesis. Performing detection in the MCA framework results in a log-likelihood ratio that is written in terms of the MCA correlations and mapping vectors as well as a local signal-to-noise ratio matrix. In this coordinate system, the J-divergence, which is a measure of the difference in means of the likelihood ratio, can effectively be represented in terms of the multi-channel correlations and mapping vectors. Using this J-divergence expression, one can get a more clear picture of the amount of discriminatory information available for detection by analyzing the amount of coherent information present among the channels. New analytical and experimental results are also presented to provide better insight on the effects of adding a new piece of data to the multi-channel Gauss-Gauss detector represented in the MCA framework. To answer questions like those posed in the first paragraph, one must carefully analyze the amount of discriminatory information that is brought to the detection process when adding observations from an additional channel. Rather than attempting to observe the increase (or lack thereof) from the full detection problem it is advantageous to look at the change incrementally. To accomplish this goal, new updating equations for the likelihood ratio are derived that involve linearly estimating the new data from the old (already existing) and updating the likelihood ratio accordingly. In this case, the change in J-divergence can be written in terms of error covariance matrices under each hypothesis. We then derive a change in coordinate system that can be used to perform dimensionality reduction. This especially becomes useful when the data we wish to add exists in high-dimensional space. To demonstrate the usefulness of log-likelihood updating, we conduct two simulation studies. The first simulation corresponds to detecting the presence of dynamical structure in data we have observed and corresponds to a temporal updating scheme. The second is concerned with detecting the presence of a single narrow-band source using multiple linear sensor arrays in which case we consider a platform (or channel) updating scheme. A comprehensive study is carried out on the MCA-based detector on three data sets acquired from the Naval Surface Warfare Center (NSWC) in Panama City, FL. The first data set consists of one high frequency (HF) and three broadband (BB) side-looking sonar imagery coregistered over the same region on the sea floor captured from an Autonomous Underwater Vehicle (AUV) platform. For this data set we consider three different detection schemes using different combinations of these sonar channels. The next data set consists of one HF and only one BB beamformed sonar imagery again coregistered over the same region on the sea floor. This data set consists of not only target objects but also lobster traps giving us experimental intuition as how the multi-channel correlations change for different object compositions. The use of multiple disparate sonar images, e.g., a high frequency, high resolution sonar with good target definition and a multitude of lower resolution broadband sonar with good clutter suppression ability significantly improves the detection and false alarm rates comparing to situations where only single sonar is utilized. Finally, a data set consisting of synthetically generated images of targets with differing degrees of disparity such as signal-to-noise ratio (SNR), aspect angle, resolution, etc., is used to conduct a thorough sensitivity analysis in order to study the effects of different SNR, target types, and disparateness in aspect angle

Neural network directed Bayes decision rule for moving target classification

Includes bibliographical references.In this paper, a new neural network directed Bayes decision rule is developed for target classification exploiting the dynamic behavior of the target. The system consists of a feature extractor, a neural network directed conditional probability generator and a novel sequential Bayes classifier. The velocity and curvature sequences extracted from each track are used as the primary features. Similar to hidden Markov model (HMM) scheme, several hidden states are used to train the neural network, the output of which is the conditional probability of occurring the hidden states given the observations. These conditional probabilities are then used as the inputs to the sequential Bayes classifier to make the classification. The classification results are updated recursively whenever a new scan of data is received. Simulation results on multiscan images containing heavy clutter are presented to demonstrate the effectiveness of the proposed methods.This work was funded by the Optoelectronic Computing Systems (OCS) Center at Colorado State University, under NSF/REC Grant 9485502

Signal Processing for Synthetic Aperture Sonar Image Enhancement

This thesis contains a description of SAS processing algorithms, offering improvements in Fourier-based reconstruction, motion-compensation, and autofocus. Fourier-based image reconstruction is reviewed and improvements shown as the result of improved system modelling. A number of new algorithms based on the wavenumber algorithm for correcting second order effects are proposed. In addition, a new framework for describing multiple-receiver reconstruction in terms of the bistatic geometry is presented and is a useful aid to understanding. Motion-compensation techniques for allowing Fourier-based reconstruction in widebeam geometries suffering large-motion errors are discussed. A motion-compensation algorithm exploiting multiple receiver geometries is suggested and shown to provide substantial improvement in image quality. New motion compensation techniques for yaw correction using the wavenumber algorithm are discussed. A common framework for describing phase estimation is presented and techniques from a number of fields are reviewed within this framework. In addition a new proof is provided outlining the relationship between eigenvector-based autofocus phase estimation kernels and the phase-closure techniques used astronomical imaging. Micronavigation techniques are reviewed and extensions to the shear average single-receiver micronavigation technique result in a 3 - 4 fold performance improvement when operating on high-contrast images. The stripmap phase gradient autofocus (SPGA) algorithm is developed and extends spotlight SAR PGA to the wide-beam, wide-band stripmap geometries common in SAS imaging. SPGA supersedes traditional PGA-based stripmap autofocus algorithms such as mPGA and PCA - the relationships between SPGA and these algorithms is discussed. SPGA's operation is verified on simulated and field-collected data where it provides significant image improvement. SPGA with phase-curvature based estimation is shown and found to perform poorly compared with phase-gradient techniques. The operation of SPGA on data collected from Sydney Harbour is shown with SPGA able to improve resolution to near the diffraction-limit. Additional analysis of practical stripmap autofocus operation in presence of undersampling and space-invariant blurring is presented with significant comment regarding the difficulties inherent in autofocusing field-collected data. Field-collected data from trials in Sydney Harbour is presented along with associated autofocus results from a number of algorithms

Algorithms and Data Structures for Automated Change Detection and Classification of Sidescan Sonar Imagery

During Mine Warfare (MIW) operations, MIW analysts perform change detection by visually comparing historical sidescan sonar imagery (SSI) collected by a sidescan sonar with recently collected SSI in an attempt to identify objects (which might be explosive mines) placed at sea since the last time the area was surveyed. This dissertation presents a data structure and three algorithms, developed by the author, that are part of an automated change detection and classification (ACDC) system. MIW analysts at the Naval Oceanographic Office, to reduce the amount of time to perform change detection, are currently using ACDC. The dissertation introductory chapter gives background information on change detection, ACDC, and describes how SSI is produced from raw sonar data. Chapter 2 presents the author\u27s Geospatial Bitmap (GB) data structure, which is capable of storing information geographically and is utilized by the three algorithms. This chapter shows that a GB data structure used in a polygon-smoothing algorithm ran between 1.3 – 48.4x faster than a sparse matrix data structure. Chapter 3 describes the GB clustering algorithm, which is the author\u27s repeatable, order-independent method for clustering. Results from tests performed in this chapter show that the time to cluster a set of points is not affected by the distribution or the order of the points. In Chapter 4, the author presents his real-time computer-aided detection (CAD) algorithm that automatically detects mine-like objects on the seafloor in SSI. The author ran his GB-based CAD algorithm on real SSI data, and results of these tests indicate that his real-time CAD algorithm performs comparably to or better than other non-real-time CAD algorithms. The author presents his computer-aided search (CAS) algorithm in Chapter 5. CAS helps MIW analysts locate mine-like features that are geospatially close to previously detected features. A comparison between the CAS and a great circle distance algorithm shows that the CAS performs geospatial searching 1.75x faster on large data sets. Finally, the concluding chapter of this dissertation gives important details on how the completed ACDC system will function, and discusses the author\u27s future research to develop additional algorithms and data structures for ACDC

AN INFORMATION THEORETIC APPROACH TO INTERACTING MULTIPLE MODEL ESTIMATION FOR AUTONOMOUS UNDERWATER VEHICLES

Accurate and robust autonomous underwater navigation (AUV) requires the fundamental task of position estimation in a variety of conditions. Additionally, the U.S. Navy would prefer to have systems that are not dependent on external beacon systems such as global positioning system (GPS), since they are subject to jamming and spoofing and can reduce operational effectiveness. Current methodologies such as Terrain-Aided Navigation (TAN) use exteroceptive imaging sensors for building a local reference position estimate and will not be useful when those sensors are out of range. What is needed are multiple navigation filters where each can be more effective depending on the mission conditions. This thesis investigates how to combine multiple navigation filters to provide a more robust AUV position estimate. The solution presented is to blend two different filtering methodologies utilizing an interacting multiple model (IMM) estimation approach based on an information theoretic framework. The first filter is a model-based Extended Kalman Filter (EKF) that is effective under dead reckoning (DR) conditions. The second is a Particle Filter approach for Active Terrain Aided Navigation (ATAN) that is appropriate when in sensor range. Using data collected at Lake Crescent, Washington, each of the navigation filters are developed with results and then we demonstrate how an IMM information theoretic approach can be used to blend approaches to improve position and orientation estimation.Lieutenant, United States NavyApproved for public release. Distribution is unlimited

Machine learning methods for discriminating natural targets in seabed imagery

The research in this thesis concerns feature-based machine learning processes and methods for discriminating qualitative natural targets in seabed imagery. The applications considered, typically involve time-consuming manual processing stages in an industrial setting. An aim of the research is to facilitate a means of assisting human analysts by expediting the tedious interpretative tasks, using machine methods. Some novel approaches are devised and investigated for solving the application problems. These investigations are compartmentalised in four coherent case studies linked by common underlying technical themes and methods. The first study addresses pockmark discrimination in a digital bathymetry model. Manual identification and mapping of even a relatively small number of these landform objects is an expensive process. A novel, supervised machine learning approach to automating the task is presented. The process maps the boundaries of ≈ 2000 pockmarks in seconds - a task that would take days for a human analyst to complete. The second case study investigates different feature creation methods for automatically discriminating sidescan sonar image textures characteristic of Sabellaria spinulosa colonisation. Results from a comparison of several textural feature creation methods on sonar waterfall imagery show that Gabor filter banks yield some of the best results. A further empirical investigation into the filter bank features created on sonar mosaic imagery leads to the identification of a useful configuration and filter parameter ranges for discriminating the target textures in the imagery. Feature saliency estimation is a vital stage in the machine process. Case study three concerns distance measures for the evaluation and ranking of features on sonar imagery. Two novel consensus methods for creating a more robust ranking are proposed. Experimental results show that the consensus methods can improve robustness over a range of feature parameterisations and various seabed texture classification tasks. The final case study is more qualitative in nature and brings together a number of ideas, applied to the classification of target regions in real-world sonar mosaic imagery. A number of technical challenges arose and these were surmounted by devising a novel, hybrid unsupervised method. This fully automated machine approach was compared with a supervised approach in an application to the problem of image-based sediment type discrimination. The hybrid unsupervised method produces a plausible class map in a few minutes of processing time. It is concluded that the versatile, novel process should be generalisable to the discrimination of other subjective natural targets in real-world seabed imagery, such as Sabellaria textures and pockmarks (with appropriate features and feature tuning.) Further, the full automation of pockmark and Sabellaria discrimination is feasible within this framework