Nonlinear time-warping made simple: a step-by-step tutorial on underwater acoustic modal separation with a single hydrophone

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Bonnel, J., Thode, A., Wright, D., & Chapman, R. Nonlinear time-warping made simple: a step-by-step tutorial on underwater acoustic modal separation with a single hydrophone. The Journal of the Acoustical Society of America, 147(3), (2020): 1897, doi:10.1121/10.0000937.Classical ocean acoustic experiments involve the use of synchronized arrays of sensors. However, the need to cover large areas and/or the use of small robotic platforms has evoked interest in single-hydrophone processing methods for localizing a source or characterizing the propagation environment. One such processing method is “warping,” a non-linear, physics-based signal processing tool dedicated to decomposing multipath features of low-frequency transient signals (frequency f  1 km). Since its introduction to the underwater acoustics community in 2010, warping has been adopted in the ocean acoustics literature, mostly as a pre-processing method for single receiver geoacoustic inversion. Warping also has potential applications in other specialties, including bioacoustics; however, the technique can be daunting to many potential users unfamiliar with its intricacies. Consequently, this tutorial article covers basic warping theory, presents simulation examples, and provides practical experimental strategies. Accompanying supplementary material provides matlab code and simulated and experimental datasets for easy implementation of warping on both impulsive and frequency-modulated signals from both biotic and man-made sources. This combined material should provide interested readers with user-friendly resources for implementing warping methods into their own research.This work was supported by the Office of Naval Research (Task Force Ocean, project N00014-19-1-2627) and by the North Pacific Research Board (project 1810). Original warping developments were supported by the French Delegation Generale de l'Armement

Underwater Source Localization based on Modal Propagation and Acoustic Signal Processing

Acoustic localization plays a pivotal role in underwater vehicle systems and marine mammal detection. Previous efforts adopt synchronized arrays of sensors to extract some features like direction of arrival (DOA) or time of flight (TOF) from the received signal. However, installing and synchronizing several hydrophones over a large area is costly and challenging. To tackle this problem, we use a single-hydrophone localization system which relies on acoustic signal processing methods rather than multiple hydrophones. This system takes modal dispersion into consideration and estimates the distance between sound source and receiver (range) based on dispersion curves. It is shown that the larger the range is, the more separable the modes are. To make the modes more distinguishable, a non-linear signal processing technique, called warping, is utilized. Propagation model of low-frequency signals, such as dolphin sound, is well-studied in shallow water environment (depth D\u3c200 m), and it was demonstrated that at large ranges (range r\u3e1 km), modal dispersion is utterly visible at time frequency (TF) domain. We used Peker is model for the aforementioned situation to localize both synthetic and real underwater acoustic signals. The accuracy of the localization system is examined with various sounds, including impulsive signal, sounds with known Fourier transform, and signals with estimated source phase. Experimental results show that the warping technique can considerably lessen the localization error, especially when prior knowledge about the source signal and waveguide are available

Digital Signal Processing Research Program

Contains table of contents for Section 2, an introduction, reports on twenty research projects and a list of publications.Lockheed Sanders, Inc. Contract BZ4962U.S. Army Research Laboratory Grant QK-8819U.S. Navy - Office of Naval Research Grant N00014-93-1-0686National Science Foundation Grant MIP 95-02885U.S. Navy - Office of Naval Research Grant N00014-95-1-0834U.S. Navy - Office of Naval Research Grant N00014-96-1-0930U.S. Navy - Office of Naval Research Grant N00014-95-1-0362National Defense Science and Engineering FellowshipU.S. Air Force - Office of Scientific Research Grant F49620-96-1-0072National Science Foundation Graduate Research Fellowship Grant MIP 95-02885Lockheed Sanders, Inc. Grant N00014-93-1-0686National Science Foundation Graduate FellowshipU.S. Army Research Laboratory/ARL Advanced Sensors Federated Lab Program Contract DAAL01-96-2-000

Amplitude and phase sonar calibration and the use of target phase for enhanced acoustic target characterisation

This thesis investigates the incorporation of target phase into sonar signal processing, for enhanced information in the context of acoustical oceanography. A sonar system phase calibration method, which includes both the amplitude and phase response is proposed. The technique is an extension of the widespread standard-target sonar calibration method, based on the use of metallic spheres as standard targets. Frequency domain data processing is used, with target phase measured as a phase angle difference between two frequency components. This approach minimizes the impact of range uncertainties in the calibration process. Calibration accuracy is examined by comparison to theoretical full-wave modal solutions. The system complex response is obtained for an operating frequency of 50 to 150 kHz, and sources of ambiguity are examined. The calibrated broadband sonar system is then used to study the complex scattering of objects important for the modelling of marine organism echoes, such as elastic spheres, fluid-filled shells, cylinders and prolate spheroids. Underlying echo formation mechanisms and their interaction are explored. Phase-sensitive sonar systems could be important for the acquisition of increased levels of information, crucial for the development of automated species identification. Studies of sonar system phase calibration and complex scattering from fundamental shapes are necessary in order to incorporate this type of fully-coherent processing into scientific acoustic instruments

Particle filtering for frequency estimation from acoustic time-series in dispersive media

Acoustic signals propagating in the ocean carry information about geometry and environmental parameters within the propagation medium. Accurately retrieving this information leads us to effectively estimate parameters that are of utmost importance in environmental studies, climate monitoring, and defense. This dissertation focuses on the development of sequential Bayesian filtering methods to obtain accurate estimates of instantaneous frequencies using Short Term Fourier Transforms within the acoustic field measured at an array of hydrophones, which can be used in a subsequent step for the estimation of propagation related parameters. We develop a particle filter to estimate these frequencies along with modal amplitudes, variance, model order. In the first part of our work, we consider a Gaussian model for the error in the data measurements, which has been the standard approach in instantaneous frequency estimation to date. We here design a filter that identifies the true structure of the data errors and implement a χ2 model to capture this structure appropriately. We demonstrate both with synthetic and real data that our approach is superior to the conventional method, especially for low Signal-to-Noise-Ratios

Blind deconvolution in multipath environments and extensions to remote source localization

In the ocean, the acoustic signal from a remote source recorded by an underwater hydrophone array is commonly distorted by multipath propagation. Blind deconvolution is the task of determining the source signal and the impulse response from array-recorded sounds when the source signal and the environment’s impulse response are both unknown. Synthetic time reversal (STR) is a passive blind deconvolution technique that accomplishes two remote sensing tasks. 1) It can be used to estimate the original source signal and the source-to-array impulse responses, and 2) it can be used to localize the remote source when some information is available about the acoustic environment. The performance of STR for both tasks is considered in this thesis. For the first task, simulations and underwater experiments (CAPEx09) have shown STR to be successful for 1.5-4 kHz broadcast signal. Here STR is successful when the signal-to-noise ratio is high enough, and the receiving array has sufficient aperture and element density so that conventional delay-and-sum beamforming can be used to distinguish ray-path-arrival directions. Also, an unconventional beamforming technique (frequency-difference beamforming) that manufactures frequency differences from the recorded signals has been developed. It allows STR to be successful with sparse array measurements where conventional beamforming fails. Broadband simulations and experimental data from the focused acoustic field experiment (FAF06) have been used to determine the performance of STR when combined with frequency-difference beamforming. For the source localization task, the STR-estimated impulse responses may be combined with ray-based back-propagation simulations and the environmental characteristics at the array into a computationally efficient scheme that localizes the remote sound source. These localization results from STR are less ambiguous than those obtained from conventional matched field processing in the same bandwidth. However, when the frequency of the recorded signals is sufficiently low and close to modal cutoff frequencies, STR-based source localization may fail because of dispersion in the environment. For such cases, an extension of mode-based STR has been developed for sound source ranging with a vertical array in a dispersive underwater sound channel using bowhead whale calls recorded with a 12-element vertical array (Arctic 2010).PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/102443/1/shimah_1.pd