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We propose a general approach towards feature extraction for identifying sonar targets based on their composition and geometry. The key idea is to discover the geometric connections between braid-like features within acoustic color topography that includes magnitude and phase information. Specifically, we characterize each target as a graph of intersecting braided features, detected across the complex-valued...
We examine petroleum forensics from a pattern recognition and feature separation perspective in this work. Apportioning the environmental impact of oil spills is important for marine pollution studies. Robust fingerprinting of an unknown sample from a petroleum-rich locale remains a data science challenge. Crude petroleum is a complex mixture, and as such, the fingerprint of a petroleum source can...
This paper proposes estimation of underwater acoustic channel in the delay-Doppler domain under dynamic sea conditions. The sparsity of the channel in the delay-Doppler domain is exploited via advanced compressive sensing (CS) technique to estimate the channel. The proposed use of CS with prior information takes care of relatively dominant but stable slowly time-varying channel component and the rapidly...
Disentangling the source-specific signature of petroleum biomarkers against the often stronger regional fingerprint has posed a fundamental challenge to oil-spill forensics in a petroleum-rich locale (e.g. Deepwater Horizon, Gulf of Mexico, April 2010). Two-dimensional gas chromatography (GC×GC) captures the joint peak distribution of hundreds of hydrocarbon biomarkers in crude petroleum through high-resolution...
Tracking the shallow water acoustic channel in real time has been a long standing challenge, particularly over moderate to rough sea conditions due to unpredictable oceanic activity that are difficult to track dynamically using a single algorithm. We build upon prior work to propose a constrained non-convex mixed norm solution that tracks the rapidly fluctuating shallow water acoustic channel as a...
Background Comprehensive two-dimensional gas chromatography $$(GC \times GC)$$ ( G C × G C ) provides high-resolution separations across hundreds of compounds in a complex mixture, thus unlocking unprecedented information for intricate quantitative interpretation. We exploit this compound diversity across the $$(GC \times GC)$$ ( G C × G C ) topography to provide quantitative...
A primary bottleneck to successful petroleum forensics [1,2] is disambiguation of fingerprint hierarchy. From an encoding perspective, this amounts to disentangling source-specific biomarker signatures against that of neighboring reservoirs, which are expected to share regional commonalities that decrease with increasing geographic distance.
We propose real-time channel tracking for underwater acoustic communications under dynamic sea conditions. The key idea is to employ sophisticated sparse sensing techniques that are cognizant of stable or slowly time-varying channel components against a transient background. Shallow water acoustic channel is generally challenging to track under moderate to rough sea conditions. This is primarily due...
We present a geometry-inspired characterization of target response for active sonar that exploits similarity between intra-class features to distinguish between different targets against environmental objects such as a rock. Key innovation is to represent feature manifolds as a set of ellipsoids, each of which geometrically encompasses a unique physical characteristic of the target's response. We...
We present a method of characterization of active sonar target response that makes use of physics-driven Gabor dictionaries. Key innovation is the combination of the useful characteristics of the Gabor wavelet (time/frequency localization) with an empirical approach to dictionary selection for underwater acoustics. We demonstrate over experimental field data that these features are both visually distinct...
Petroleum forensics for apportioning the environmental impact of oil spills necessitate quantitative differentiation between highly correlated biomarker distributions of neighboring oil sources. (GC × GC) generates high-resolution images that represent the complex hydrocarbon peak profiles of these petroleum biomarkers. As such, source differentiation reduces to the complex challenge of disambiguating...
We present complementary compound-cognizant data engineering techniques for feature compression and data indexing across two-dimensional gas chromatographic (GC×GC) datasets with petroleum forensics as the primary application. We propose single-linkage clustering of dominant compounds (targets) along with local interpretation across biomarker peak profiles. Our methods enable high-volume data compression,...
Rapidly fluctuating multipath arrivals along with unpredictable surface wave focusing events render the shallow water acoustic channel difficult to track using sparse or least-squared error (LSE) optimization techniques. This fundamental bottleneck is primarily due to the time-varying nature of the underlying distribution. In this work, we propose a complementary channel tracking technique that exploits...
We address the long-standing challenge of sonar target identification against weak ground truths and interfering scatter components by harnessing robust topographic elements of target resonance profiles. Our goal is to achieve unknown target discovery when supervised learning is not practical due to ground truth uncertainties. Specifically, we examine topographic manifolds to localize resonance components,...
We address the well-known challenge of detecting a target in non-stationary clutter in active sonar using dynamic time-frequency localization. The challenge is to track a target against non-stationary reverberation from the bottom and sea surface, as well as backscatter from biologics. Definition of "target" is application-specific, e.g. in a Naval application the target would typically...
Non-stationary reflections from moving sea surface, along with unpredictable oceanographic phenomena such as surface wave focussing render multipath interference in shallow water acoustics difficult to model or track in real time. Traditional equalization techniques have met limited success in underwater acoustic communications due to these rapid channel fluctuations as well as time-varying sparsity...
The shallow water acoustic channel is challenging to estimate and track due to rapid temporal fluctuations of its large delay spread. However, the impulse response and representations of its time-variability often exhibit a sparse structure that can be exploited to improve estimator performance. We propose a sparse reconstruction of the shallow water acoustic channel that employs a novel optimization...
Stationarity of the sparse coefficients as well as the sparseness of their support, along with incoherence assumptions related to restricted isometry, are fundamental to compressive sensing and sparse optimization. However, scientific study of many sparse processes encountered in nature as well as engineering applications necessitates solving ill-conditioned optimization metrics and tracking rapidly...
This paper proposes a computationally efficient approach to designing the maximum asymptotic efficiency (MAE) equalizer, which minimizes bit error rate as the signal-to-noise ratio approaches infinity. The MAE equalizer is implemented as a tapped delay line and hence has the same runtime complexity as the simple MMSE linear equalizer. However, design of the MAE equalizer involves finding the minimum...
We propose a vision for combining well-known array processing techniques with recent developments in the compressive sensing community to address a broad range of signal processing problems related to oceanography. Our goal is to set up a general mathematical framework to address and solve sensor array problems that involve time-varying sparse processes. Specifically we propose a compressive sensor...
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