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Estimation of Doppler shift
We focus on the former and offer a method to estimate the Doppler shift for a synchronization signal consisting of a chirp signal with a positive (up) time-frequency slop modulation and a chirp signal with a negative (down) slop. Our work offers an analytic expression how to derive the Doppler shift from the relative shift of the matched filter's matching point for the two signals. The derivations are offered precisely for linear and quadratic chirp signals, and are approximated for higher order chirp signal. We also offer insights of what is the best chirp signal in terms of Doppler shift resilience to use as a synchronization signal
Detection of low signal-to-clutter (SCR) targets using active sonar
Overcoming false alarms in active acoustic target detection by clustering the reflection pattern.
Bayesian Optimal Joint Channel Estimation and Decoding for Internet of Underwater Things
A novel Bayesian-optimal joint channel estimation and decoding scheme for efficient Internet of Underwater Things (IoUT) transmission, where the channel impulse response (CIR) is longer than the length of payload sequence, and the extremely long intersymbol interference (ISI) will contaminate the whole packet duration.
Dolphin whistles detection by phase Tracking
In this study, we aim for a fully blind detection, i.e., without pre-determining the dolphin’s type, and concentrate on sensitivity to noise by finding some specific characteristics of a dolphin whistle. In particular, we rely on the continuity of the whistle’s phase to make a whistle absence/presence decision.
Feature Set for Classification of Underwater Objects in Optical and Sonar Data
Object matching from both underwater sonar and optical sensors have great potential for applications such as mapping, object recognition, and autonomous navigation. Feature matching methods, like scale-invariant feature transform (SIFT), usually utilize gradient information to detect feature points, whereas contour-based features often use geometrical descriptors such as roughness, circularity, and solidity. However, due to the different characteristics of sonar and optical imagery, current descriptors cannot be directly applied over sonar-optical image pairs. Light attenuation, optic disturbance due to concentration of particles, inhomogeneity of water, and water turbidity are some of the factors that generate intensity differences between the two sensor types. This project proposes a feature characterization method that can match man-made objects found in underwater optic-acoustic image pairs.
A Multispectral Target Detection in Sonar Imagery
With the aim of detecting objects in complex environments where pixel-intensity or shape-based recognition fail, we propose to utilize the expected spectral diversity of reflections from man-made objects, compared to the relatively flat frequency response of reflections from natural objects such as rocks.
Underwater Receivers Deployment for Time Difference of Arrival based Localization
We seek to maximize the covered area, constrained by the number of receivers, thus the node to be localized can lie outside the area between the anchors while information about the node to by localized route and dynamics is unknown.
Optimum Deployment Strategy of Drifters for Water Current Evaluation
In this work, we aim to develop a mechanism that predicts the minimum number of drifters required to represent a water current velocity field, and to determine their deployment location. Our research quastion is generic; what is the optimal number of drifters and respective deployment positions for a given numerical model?
Design of efficient Lagrange drifter
Design and implement an underwater device that can drift with the ocean’s current while remaining in the same depth. Device has a thruster, underwater acoustic communication, and payload sensors.
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