Mapping coastal circulations using moving vehicle acoustic tomography.

With the increased availability of highly maneuverable unmanned surface/underwater vehicles, abundant ocean data can now be collected. This study uses tomographic techniques to extend the survey area covered by moving vehicles. An acoustic reciprocal transmission experiment was conducted using three tomographic sensors installed on an autonomous underwater vehicle, a fishing ship, and a buoy. The distributed sensing method is applied for currents estimation. The estimated currents near the ship show consistent results with the direct measurements. In particular, an anticyclonic circulation was revealed. Further, a general least-squares method is employed to improve the estimate of this vortical structure.

Mapping of ocean currents in shallow water using moving ship acoustic tomography.

This study investigates techniques for the acoustic mapping of ocean currents in a shallow-water environment using moored and ship-towed stations. The currents are estimated using the differential travel times (DTTs) observed in the reciprocal acoustic transmissions between those stations. Due to the relative motion induced by the ship-towed station, the Doppler shift estimated from the measured channel impulse response is used to compensate for the arrival patterns and to correct the influence of the relative speed on the DTTs. Furthermore, to estimate the DTTs from the arrival patterns received in the reciprocal directions, a method is devised which involves the time-evolving cross-correlation function of the reciprocal arrival patterns. A feasibility test was carried out at Sizhiwan Marine Test Field, Kaohsiung, Taiwan in September of 2015. The currents were estimated using the data collected from one ship-towed and four moored stations. The estimated currents are consistent with the direct measurements from a shipboard acoustic Doppler current profiler and reveal the spatial distribution of the currents.

Estimating temperature and current using a pair of transceivers in a harbor environment.

Obtaining the horizontal variation of temperature and current fields of a water column usually requires travel-time measurements of acoustic signals traveling along different paths between several horizontally distributed transceivers. This study explores the possibility of using a pair of transceivers deployed in a highly-reverberant harbor environment to extract spatial information of the water. Multipath acoustic propagation of two main arrival groups, i.e., direct arrivals and arrivals reflecting off the harbor side, was observed in the pulse responses measured in the harbor environment during the flood tide. Compared with the direct point measurements of temperature and current, the path-averaged measurements show a similar temporal variation during the experiment, demonstrating the possibility of estimating the spatial variation of the currents and temperatures using the multipath acoustic propagation.

Multipath correlations in underwater acoustic communication channels.

Uncorrelated scattering (US), which assumes that multipath arrivals undergo uncorrelated scattering and are thus uncorrelated, has been the standard model for digital communications including underwater acoustic communications. This paper examines the cross-correlation of multipath arrivals based on at-sea data with different temporal coherence time, assuming quasi-stationary statistics. It is found that multipath arrivals are highly cross-correlated when the channel is temporally coherent, and are uncorrelated when the channel is temporally incoherent. A theoretical model based on the path phase rates and relative-phase fluctuations is used to explain experimentally observed phenomena, assuming the path amplitudes vary slowly compared with the phases. The implications of correlated scattering for underwater acoustic communication channel tracking are discussed.

Acoustic mapping of ocean currents using networked distributed sensors.

Distributed underwater sensors are expected to provide oceanographic monitoring over large areas. As fabrication technology advances, low cost sensors will be available for many uses. The sensors communicate to each other and are networked using acoustic communications. This paper first studies the performance of such systems for current measurements using tomographic inversion approaches to compare with that of a conventional system which distributes the sensors on the periphery of the area of interest. It then proposes two simple signal processing methods for ocean current mapping (using distributed networked sensors) aimed at real-time in-buoy processing. Tomographic inversion generally requires solving a challenging high dimensional inverse problem, involving substantial computations. Given distributed sensors, currents can be constructed locally based on data from neighboring sensors. It is shown using simulated data that similar results are obtained using distributed processing as using conventional tomographic approaches. The advantage for distributed systems is that by increasing the number of nodes, one gains a much more improved performance. Furthermore, distributed systems use much less energy than a conventional tomographic system for the same area coverage. Experimental data from an acoustic communication and networking experiment are used to demonstrate the feasibility of acoustic current mapping.

Measuring the Kuroshio Current with ocean acoustic tomography.

Ocean current profiling using ocean acoustic tomography (OAT) was conducted in the Kuroshio Current southeast of Taiwan from August 20 to September 15, 2009. Sound pulses were transmitted reciprocally between two acoustic stations placed near the underwater sound channel axis and separated by 48 km. Based on the result of ray simulation, the received signals are divided into multiple ray groups because it is difficult to resolve the ray arrivals for individual rays. The average differential travel times from these ray groups are used to reconstruct the vertical profiles of currents. The currents are estimated with respect to the deepest water layer via two methods: An explicit solution and an inversion with regularization. The strong currents were confined to the upper 200 m and rapidly weakened toward 500 m in depth. Both methods give similar results and are consistent with shipboard acoustic Doppler current profiler results in the upper 150 m. The observed temporal variation demonstrates a similar trend to the prediction from the Hybrid Coordinate Ocean Model.

Clutter reduction using Doppler sonar in a harbor environment.

A high frequency experiment was conducted in the Woods Hole Harbor in Massachusetts to evaluate the effectiveness of Doppler sonar for discriminating targets from reverberation. Using a pulsed linear frequency modulated signal, one finds that the matched filtered outputs are filled with high-level discrete backscattered returns, referred to as clutter, which are often confused with the target echo. The high level non-target returns have an amplitude distribution that is heavy-tailed. Using a Doppler-sensitive binary-phase-shift-keying signal coded with an m-sequence, the target echo and clutter can be separated by Doppler and delay, and tracked using the Doppler spectrogram (Dopplergram). The Doppler filtered time series show a background reverberation with a Rayleigh-like amplitude distribution, with an improved signal-to-(peak) reverberation ratio compared with that without Doppler filtering. The reduced reverberation level with Doppler processing decreases the probability of false alarm (Pfa) for a given threshold level. Conversely, for a given Pfa, the higher signal-to-(peak) reverberation ratio implies a higher probability of detection. Transmission loss measurement was conducted to estimate some of the system parameters, e.g., the source level and target strength relative to the noise level.

Localization of a leading robotic fish using a pressure sensor array on its following vehicle.

The tail-flapping propulsion of a robotic fish forms a hydrodynamic pressure field that depends primarily on the flapping frequency and amplitude. In a two-robot aligned group, the tail of the front robot generates an oscillating pressure that is detectable by its follower. This paper proposes a position estimator for the follower to locate the position of the leading robotic fish. The position estimator uses the hydrodynamic pressure measured on a sensor array installed on the forefront of the following vehicle body. We derive a potential flow model to describe the pressure field of the leader in the presence of the follower. Using this pressure field model, we further derive an observability measure which is used to determine the relative positions of the leader and follower for which the position estimator will produce a reliable estimate. The position estimator employs the Levenberg-Marquardt algorithm, due to the nonlinearity of the pressure model. Results from the observability analysis show that a satisfactory estimation of the leader position is achieved when the leader is located directly ahead, on the starboard-bow, or the port-bow of the follower, similar to the formation pattern generally found in a school of fish. The observability analysis also shows that poor estimation is obtained when the leader is abeam of the follower. Tank experiments confirm the observability analysis and also demonstrate the use of the position estimator for feedback control by the follower.

Statistical estimation of source location in presence of geoacoustic inversion uncertainty.

A statistical estimation of source location incorporating uncertainty in ocean environmental model parameters is derived using a Bayesian approach. From a previous geoacoustic inversion, a posterior probability distribution of the environmental parameters that reflects uncertainty in the ocean environment is obtained. This geoacoustic uncertainty then is mapped into uncertainty in the acoustic pressure field and is propagated through the Bartlett matched-field processor for source localization. Using data from the ASIAEX 2001 East China Sea experiment, the estimated source location and variability over time are compared with the known source positions.

Effect of ocean sound speed uncertainty on matched-field geoacoustic inversion.

The effect of ocean sound speed uncertainty on matched-field geoacoustic inversion is investigated using data from the SW06 experiment along a nearly range-independent bathymetric track. Significant sound speed differences were observed at the source and receiving array and several environmental parameterizations were investigated for the inversion including representing the ocean sound speed at both source and receivers with empirical orthogonal function (EOF) coefficients. A genetic algorithm-based global optimization method was applied to the candidate environmental models. Then, a Bayesian inversion technique was used to quantify uncertainty in the environmental parameters for the best environmental model, which included an EOF description of the ocean sound speed.

Passive fathometer processing.

Ocean acoustic noise can be processed efficiently to extract Green's function information between two receivers. By using noise array-processing techniques, it has been demonstrated that a passive array can be used as a fathometer [Siderius, et al., J. Acoust. Soc. Am. 120, 1315-1323 (2006)]. Here, this approach is derived in both frequency and time domains and the output corresponds to the reflection sequence. From this reflection sequence, it is possible to extract seabed layering. In the ocean waveguide, most of the energy is horizontally propagating, whereas the bottom information is contained in the vertically propagating noise. Extracting the seabed information requires a dense array, since the resolution of the bottom layer is about half the array spacing. If velocity sensors are used instead of pressure sensors, the array spacing requirement can be relaxed and simulations show that just one vertical velocity sensor is sufficient.

Statistical estimation of transmission loss from geoacoustic inversion using a towed array.

Geoacoustic inversion estimates environmental parameters from measured acoustic fields (e.g., received on a towed array). The inversion results have some uncertainty due to noise in the data and modeling errors. Based on the posterior probability density of environmental parameters obtained from inversion, a statistical estimation of transmission loss (TL) can be performed and a credibility level envelope or uncertainty band for the TL generated. This uncertainty band accounts for the inherent variability of the environment not usually contained in sonar performance prediction model inputs. The approach follows [Gerstoft et al. IEEE J. Ocean. Eng. 31, 299-307 (2006)] and is demonstrated with data obtained from the MAPEX2000 experiment conducted by the NATO Undersea Research Center using a towed array and a moored source in the Mediterranean Sea in November 2000. Based on the geoacoustic inversion results, the TL and its variability are estimated and compared with the measured TL.

On the effect of error correlation on matched-field geoacoustic inversion.

The effect of correlated data errors on matched-field geoacoustic inversion for vertical array data is examined. The correlated errors stem from the inability to model the inhomogeneities in the environment resulting in an additional error term beyond ambient noise. Simulated data with these correlated errors are generated and then inverted with or without using the proper covariance matrix. Results show that the correlated error has a negative impact on geoacoustic parameter estimation if not accounted for properly.

Uncertainty analysis in matched-field geoacoustic inversions.

Quantifying uncertainty for parameter estimates obtained from matched-field geoacoustic inversions using a Bayesian approach requires estimation of the uncertainties in the data due to ambient noise as well as modeling errors. In this study, the variance parameter of the Gaussian error model, hereafter called error variance, is assumed to describe the data uncertainty. In practice, this parameter is not known a priori, and choosing a particular value is often difficult. Hence, to account for the uncertainty in error variance, several methods are introduced for implementing both the full and empirical Bayesian approaches. A full Bayesian approach that permits uncertainty of the error variance to propagate through the parameter estimation processes is a natural way of incorporating the uncertainty of error variance. Due to the large number of unknown parameters in the full Bayesian uncertainty analysis, an alternative, the empirical Bayesian approach, is developed, in which the posterior distributions of model parameters are conditioned on a point estimate of the error variance. Comparisons between the full and empirical Bayesian inferences of model parameters are presented using both synthetic and experimental data.