Detection in an uncertain underwater waveguide

An important problem in sound localization is the determination of the polar and azimuthal angles of far-field acoustic sources. Two fundamental approaches to the solution can be identified: spatial filtering (beamforming) and the parameter estimation approach. Van Veen and Buckley (1988) and Krim and Viberg (1996) give comprehensive reviews of the first and second approaches, respectively. Spatial filtering was carried out by an array of pressure sensors. A serious drawback to the filtering approach is that its performance depends directly on the physical size of the array (aperture), regardless of the data gathering time and signal-to-noise ratio. This aperture dependence together with more demanding applications motivated a good number of researchers to develop parametric estimation techniques. These methods can be separated into two main categories, namely, spectralbased and parametric approaches. The most famous example of the first is MUSIC (MUltiple Signal Classification) algorithm developed by Schmidt (1981) and Bienvenu and Kopp (1980), and of the second is the Maximum Likelihood (ML) method developed by Kumaresan and Shaw (1985) and Bresler and Macovski (1986). In contrast to beamforming techniques, a MUSIC estimate of arbitrary accuracy can be achieved if the data gathering time is sufficiently long, the SNR high enough, and the signal model sufficiently accurate. However, a significant limitation is the inability to resolve closely spaced signals with small sample sizes and low SNR. Further deterioration occurs for highly correlated signals and complete breakdown for coherent signals. The interested reader is referred to Krim and Viberg (1996) for discussions on how these limitations have been addressed. All of the methods for localizing acoustic sources had one thing in common. They used arrays composed of pressure sensors. This continued until Nehorai and Paldi (1994) introduced a new type of sensor called the vector sensor. An acoustic vector sensor measures the acoustic pressure and all three components of the acoustic particle velocity at a single point in space. The extra information provided by the vector sensor opened the door to improved source localization accuracy without increase in array aperture. Vector-sensor models and fundamental processing techniques were developed by Nehorai and Paldi (1994) and Hawkes and Nehorai (2000) for the case of sensors located away from and in the presence of a reflecting boundary, respectively. Parametric techniques that had been designed for arrays of pressure sensors were adapted to vector sensors. For example, Wong

An vector sensor is a compact device which simultaneously measures the scalar pressure p(t, r/spl I.oarr//sub 0/) and the gradient of the pressure, /spl nabla/p(t, r/spl I.oarr//sub 0/), at some measurement point r/spl I.oarr//sub 0/. Thus, a three-dimensional (3D) vector sensor produces four time series outputs, which can be processed to provide some degree of spatial filtering and a direction of arrival (DoA) estimate to an target. Historically, two-dimensional (2D) vector sensors have been used in several Navy systems, like the DIFAR sonobuoy (AN/SQQ-53 series) and the AN/WLR-9 intercept receiver (which uses the multimode hydrophone). These devices have made important contributions to the Navy sonar community, where it is desirable to obtain an accurate azimuthal DoA estimate for bearing to a low-frequency target from a single point in space. Other researchers have used the outputs of vector sensor to estimate intensity vector (watts/m/sup 2/). In the literature, these intensity-related vector sensors have been referred to as intensity probes or acoustic watt-meters. In this paper, we consider 3D vector sensors that uses three orthogonal underwater accelerometers with a scalar pressure sensor of hydrophone packaged in a single housing. The sensors will be referred to as low-frequency vector sensors since they were optimized for the 50 Hz to 2 kHz band. We will show that these vector sensors can produce a frequency-independent spatial response over the aforementioned band. Specifically, a 3-dB beamwidth of 105 degrees across the stated frequency band and an array gain against isotropic noise of 6 dB can be achieved. We will also show that the four time series vector sensor outputs (x, y and z accelerations and 1 pressure) can be processed to produce multiple nulls in the spatial response or beam pattern, which can be used to null out interfering noise sources. Experimental results, recorded at the US Navy Seneca Lake Test Facility, Dresden, NY using two different designs of the aforementioned 3D vector sensor, show how vector sensors perform in the presence of multiple low-frequency sources and interferers.

Design, development, fabrication and evaluation of the dynamics of a graphene based underwater acoustic vector sensor: A simulation and experimental study

In the free field, making full use of scalar and vector information of acoustic field can bring many advantages for acoustic measurement, but under the conditions of the existence of sound baffle, the sound field should be distorted due to the superposition of diffraction wave. Taking the soft spherical baffle as background, the sphere scattering model is established, the diffraction pressure field and particle velocity field under the condition of different frequency, distance and incident angle is analyzed. The effect of diffraction wave on sound pressure and particle velocity is determined by the amplitude of diffraction wave and the phase difference between the diffraction wave and the incident wave, which is related with the frequency, distance and incident angle. The effect of diffraction wave on sound pressure and the particle velocity components in y axis is strong only on near field and high frequency, but for particle velocity component in x axis, on near field, the effect is always strong both at high and low frequency. The effect of diffraction wave can be ignored only in the far field for particle velocity component in x axis. The description of reference for influences on the directivity of vector sensor by soft ball boundary is not consistent with us.

A vector sensor measures the scalar and vector components of the acoustic field. In a multipath channel, the sensor collects the information via multiple paths. Depending on the angle-of-arrival distribution and other channel characteristics, different types of correlation appear in a vector sensor array, which affect the array performance. In this paper, a novel statistical framework is developed which provides closed-form parametric expressions for signal correlations in vector sensor arrays. Such correlation expressions serve as useful tools for system design and performance analysis of vector sensor signal processing algorithms. They can also be used to estimate some important physical parameters of the channel such as angle spreads, mean angle of arrivals, etc.

This paper introduces a sequential filtering technique to obtain the ocean bottom acoustic properties with the noise field of a ship of opportunity. It filters the ratio between pressure p and vertical particle velocity V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">z</sub> (or pressure gradient p <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">g</sub> ), both measured by a vector sensor, to estimate the environmental properties. This technique models the inverse problem as a random walk for the environmental time/spatial evolution, by formulating the environmental parameters in a recursive state-space form. It assumes the environmental parameters as the summation of previous stage parameters with a noise variance term for each state, and estimates their values along the source ship track by sequentially filtering recorded ship noise data. Numerical tests were carried out for the South East Elba environment. The preliminary results demonstrates this technique can effectively resolve most geoacoustic parameters.

Acoustic emissions from underwater sources are used to detect, locate, and track targets by passive surveillance sensors. The “vector” sensors measure the scalar pressure as well as the orthogonal components of particle velocity and offer distinct advantages over the traditional solution of array of hydrophones due to their smaller size and frequency independent performance over the frequency range specified for constituent elements. This paper presents studies on an in-house implemented prototype vector sensor employing tri-axial accelerometer as constituent element. The tests undertaken include Direction of Arrival (DoA) estimation and application of modified Fast Orthogonal Search technique on a single Vector Sensor for improvement in spectral resolution. We also propose a configuration involving just two Vector Sensors (composed solely of accelerometers) that offers ease of design and obviates the requirement of a pressure sensor in setup for obtaining DoA estimate while also providing range to the source.

Predicted values of transmission loss (TL) in ocean environments are sensitive to environmental uncertainties. The resulting predicted-TL uncertainty can be quantified via the probability density function (PDF) of TL. Monte Carlo methods can determine the PDF of TL but typically require thousands of field calculations, making them inappropriate for real-time applications. Thus, a variety of alternative techniques based on polynomial chaos, field shifting, modal propagation in ocean waveguides, and spatial variations of TL near the point(s) of interest have been proposed. Recently, an approach to estimating the PDF of TL based on nominal TL, ocean environmental parameters, and machine learning was found to have a success rate of 95% with constant source depth (91 m) and frequency (100 Hz) when tested on 657,775 receiver locations within 100 randomly selected ocean environments. This presentation describes an extension of this approach and its success predicting the PDF of TL for different source depths and frequencies for ranges up to 100 km. This increase in the size of the parameter space furthers the need for a sophisticated method of choosing training examples. Such a method is proposed, and its performance is compared to that of prior techniques. [Work supported by ONR.]

The vector sensor is a new type of sonar in recent years, and the vector signal processing technology has also been developed. The vector sensor can simultaneously pick up the acoustic pressure and particle velocity information in the acoustic field at a common point. The vector sensor can suppress the isotropic noise field. Only a single vector sensor can complete the target azimuth estimation. However, single vector sensor cannot complete target positioning based on acoustic pressure and particle velocity information alone, and other environmental information needs to be combined. The time reversal mirror (TRM) technology in underwater acoustics comes from the phase conjugation method in optics, which is a positioning technology. The TRM is used in the field of active sonar firstly. With the gradual maturity of the modeling of multi-path acoustic propagation in the underwater waveguide environment, passive positioning technology based on virtual TRM can be realized. With the development of vector signal processing technology, the performance of TRM can be effectively improved. In recent years, research in the field of passive TRM has mainly focused on the underwater communication. Research on the passive positioning technology of TRM is lacking. Under the above background, this paper studies the passive positioning technology of TRM based on single vector sensor.

Because of the difficulties in accurately characterizing the statistical behavior of underwater acoustic channels, tolerance to uncertain statistical modeling is an important property for underwater acoustic signal processing procedures to possess. This paper provides an overview of a number of techniques developed in recent years that can be applied to provide uncertainty tolerance in underwater signal processing applications. In addition to a discussion of general concepts of uncertainty tolerance, specific methods for attaining tolerance to uncertainty in temporal/spatial statistics for procedures such as Wiener and matched filtering, time-delay estimation, sonar system design, and signal prediction are reviewed. Tolerance to uncertainty in amplitude statistics is also a key issue in underwater channels, and techniques for achieving this goal are discussed in the contexts of signal estimation, identification, and detection procedures.

The paper studies the patterns manifesting themselves in the formation and propagation of caustic and weakly diverging beams using the geometric acoustic approximation and mode theory applied to the simplest model of an underwater sound channel open to the bottom in the form of the bilinear dependence of the square of the refractive index on depth. It is established that when a vertical array emitting a tonal sound signal is located at a certain critical depth, the multimode caustic beam which forms near the reference ray horizontally departing from its center and which predominates in intensity is simultaneously the most weakly diverging beam. It is shown that the reference ray of such a weakly diverging caustic beam corresponds to the smoothest minimum for the dependence of the length of the ray cycle on their angle of departure from the center of the array and to the depth level of revolution at the critical depth characteristic of the given oceanic waveguide.

Acoustic noise in the ocean is spatially complex and dynamic. It results from a composite of moving discrete and distributed sources with narrow and broadband signatures from near and distant locations over complex propagation paths. Whether the goal of noise characterization is to improve understanding of the sources and environmental aspects of noise or to find and describe weak signals, it can be better achieved with vector acoustic data and vector-based analysis rather than with a pure scalar-pressure approach. We will show analytically and with realistic acoustic noise simulations that the vector characterization of noise parameters such as directional distribution, frequency content, and time variation is more accurate and can be done with simpler instrumentation than what is commonly done with pressure data. We employ adaptive particle filters to model and estimate the temporal aspects of the noise fields. The filter's state and observation vectors consist of signal directions and magnitudes, respectively. As a result, the noise fields are directly associated with the filter's process and observation noise.

A two-dimensional (2D) vector sensor measures one acoustic pressure and two acoustic particle velocity components of the acoustic field. In this paper, a new approach is presented for localization of acoustic sources using an array of 2D vector sensors. A simple algorithm for estimating the source location with this array is presented along with their statistical performance analysis. The simulation experiment is presented to verify the efficacy of the proposed localization method and simultaneously analyzing the factors which affects the accuracy of localization.

We propose time-frequency methods to filter dispersive guided wave signals. Guided waves occur in acoustical propagation (oceanic waveguides), geophysics (layered medium), or optics (dielectric optical waveguides). In waveguides, signals can be decomposed into normal modes which contain information on environmental parameters and source localization. As modes present nonlinear time-frequency evolution, modal filtering is not possible with conventional tools. To overcome the difficulty presented by these nonlinearties, we have developed matched tools: matched frequency and time-frequency representations and the modal filterings associated with these representations. The tools developed are based on unitary equivalence principle. Performance and robustness of different proposed modal filters are evaluated and compared. All of these tools can be used for both source localization and environmental inversion.

Noise and distortion in real signals have posed challenges in signal detection in ultrahigh-frequency (UHF) radio frequency identification (RFID) systems, with the exception of large frequency deviation. In this article, signal detection in a UHF RFID system was treated as a time-series classification problem. A support vector machine-based detector was proposed for real-time signal detection to optimize the system performance. The improved system was able to achieve frame synchronization detection and time-series data detection with a frequency deviation of ± 22%. With the analysis of the frame format, the frame synchronization was based on the sequence detection with the most likely selection, and the data detection was performed using a symbol-by-symbol method with two-step adjustments in order to reduce time and hardware costs. The algorithm was mainly trained offline using the experimental data, and then efficiently implemented in a UHF RFID system based on a software-defined radio platform. The system was validated on real-time signals of commercial tags. The experimental results showed that the learned detector had displayed improvements in detection accuracy and stability when compared to the existing method based on a correlation algorithm.