Localization Technology Basing Two-Dimensional Vector Sensor

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.

Most underwater target tracking algorithms are based on noisy bearing-only measurements. The bearing measurements are actually bearing estimates obtained from acoustic field measurements made by an array of sensors placed on a manoeuvrable platform. In this paper, we investigate the problem of tracking a manoeuvring target in the ocean directly from the acoustic field measurements. The use of acoustic field measurements eliminates the need for the use of a manoeuvrable platform, and also eliminates the intermediate step of bearing estimation. We consider the vector acoustic field measurements by an array of acoustic vector sensors (AVS), which are more informative than the scalar acoustic field measurements by an array of acoustic pressure sensors (APS). We model the target dynamics by a piecewise-constant turn rate model. The resultant nonlinear estimation problem is solved using the Gaussian approximation filtering approach. Within this framework, we consider the quasi-Monte Carlo Kalman filtering (QMC-KF) algorithm that employs an advanced numerical integration procedure to solve the multi-dimensional integrals appearing in the filtering process. We also derive the posterior Cramer-Rao lower bound (PCRLB) on the root mean square error (RMSE) of the sequential Bayesian estimator, and show that the PCRLB for acoustic field measurements is lower than that for bearing-only measurements. Simulation results indicate that acoustic field measurements yield a significantly better performance than bearing-only measurements, the QMCKF performs better than other deterministic filters, and an AVS array provides a much better performance than an APS array.

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.

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

Open lake experiment for direction of arrival estimation using acoustic vector sensor array

In support of monitoring technologies to evaluate the potential environmental effects of offshore energy devices, this project attempts to ultimately improve upon traditional acoustic sensing techniques by developing a cost-effective compact array of acoustic vector sensors that characterizes, classifies, and provides accurate location information for anthropogenic and natural sounds. The NoiseSpotter consists of three acoustic vector sensors arranged in a three-dimensional array deployed as a seafloor mooring. Each vector sensor measures acoustic pressure and the three-dimensional particle velocity vector associated with the propagation of an acoustic wave, thereby inherently providing bearing information to an underwater source of sound. Here, performance characteristics of the NoiseSpotter are presented, along with an analysis of its abilities as an operational passive acoustic monitoring system to characterize sources of sound. Data are presented from controlled acoustic transmissions in a quiet environment and an energetic tidal channel in the presence of non-acoustic flow noise. NoiseSpotter performance is demonstrated through its modular design, ease of deployment/recovery, ability to remove signal contamination by flow noise and the ability to geolocate acoustic sources of interest.

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.

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

This paper presents an L-shaped sparsely-distributed vector sensor (SD-VS) array with four different antenna compositions. With the proposed SD-VS array, a novel two-dimensional (2-D) direction of arrival (DOA) and polarization estimation method is proposed to handle the scenario where uncorrelated and coherent sources coexist. The uncorrelated and coherent sources are separated based on the moduli of the eigenvalues. For the uncorrelated sources, coarse estimates are acquired by extracting the DOA information embedded in the steering vectors from estimated array response matrix of the uncorrelated sources, and they serve as coarse references to disambiguate fine estimates with cyclical ambiguity obtained from the spatial phase factors. For the coherent sources, four Hankel matrices are constructed, with which the coherent sources are resolved in a similar way as for the uncorrelated sources. The proposed SD-VS array requires only two collocated antennas for each vector sensor, thus the mutual coupling effects across the collocated antennas are reduced greatly. Moreover, the inter-sensor spacings are allowed beyond a half-wavelength, which results in an extended array aperture. Simulation results demonstrate the effectiveness and favorable performance of the proposed method.

In this article a numerical model is developed to investigate the thermoacoustic conversion of heat into sound in a Rijke tube. This study is carried out in an attempt to better understand the internal coupling among heat addition, pressure, and velocity oscillations inside a Rijke-type pulse combustor. In fact, similar coupling is believed to exist in other combustion devices including rocket motors at the verge of instability. In light of the recent progress in computational fluid dynamics (CFD), we are now able to incorporate the compressibility and acoustic wave effects bridging the gap between thermal and pressure oscillations. When acoustic velocity and pressure have favorable time phases in the lower tube section, their synchronously alternating motions in the upward or downward directions give rise to acoustic excitation. As a result, an optimal conversion occurs whereby thermal energy is converted into mechanical energy. The latter is manifested in the form of acoustic intensity, a by-product of acoustic velocity and pressure. Below a threshold value in power input to the internal heat source, no self-sustained acoustic oscillations are observed. Conversely, when a critical power input to the heater is exceeded, resonance is triggered in the form of pronounced acoustic amplification. Self-sustained thermal oscillations near the heat source are found to be responsible for driving the acoustic pressure excitation. The acoustic pressure and velocity mode shapes along the centerline concur with one-dimensional acoustic theory except near the heater source where a local increase in the velocity amplitude is noted. Our two-dimensional CFD results agree with experimental observations reported in other studies. During limit-cycle oscillations, the acoustic pressure is found to lead thermal fluctuations by a 45-degree angle. This result may be used to specify the phase angle in Carvalho's analytical formulation, which predicted a value smaller than 90 degrees. Overall, numerical results indicate a strong pressure dependence on heat fluctuations. In fact, the modulus of thermal oscillations is found to be directly proportional to the modular product of acoustic velocity and pressure. In relation to solid and hybrid rocket motors, our findings can be extrapolated to predict a strong thermoacoustic, noise generating coupling in the forward half of the motor.

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.

We examine the improvement attained by using acoustic vector-sensors for direction-of-arrival (DOA) estimation, instead of traditional pressure sensors, via optimal performance bounds and particular estimators. By examining the Cramer-Rao bound in the case of a single source, we show that a vector-sensor array's smaller estimation error is a result of two distinct phenomena: (1) an effective increase in signal-to-noise ratio due to a greater number of measurements of phase delays between sensors and (2) direct measurement of the DOA information contained in the structure of the velocity field due to the vector sensors' directional sensitivity. Separate analysis of these two phenomena allows us to determine the array size, array shape, and SNR conditions under which the use of a vector-sensor array is most advantageous and to quantify that advantage. By extending the beamforming and Capon (1969) direction estimators to vector-sensors, we find that the vector-sensors' directional sensitivity removes all bearing ambiguities. In particular, even simple structures such as linear arrays can determine both azimuth and elevation, and spatially undersampled regularly spaced arrays may be employed to increase the aperture and, hence, the performance. Large sample approximations to the mean-square error matrices of the estimators are derived and their validity is assessed by Monte Carlo simulation.

Vector sensors have attracted much attention in recent years. However, relative few papers have been published on the geo-acoustic inversion with vector sensors. The data recorded with vector sensors in several shallow water acoustic experiments have been studied in this paper. The experimental data and theoretical analyses indicate that (1) the intensity of the vertical particle velocity decreases faster than that of the horizontal particle velocity; (2) the same normal modes of the horizontal particle velocity and the vertical particle velocity have similar transmission losses, but different amplitudes and phases; (3) the difference of the mode amplitudes between the horizontal particle velocity and the vertical particle velocity is dependent on the eigen-value and receiver depth. With those properties, two geo-acoustic inversion schemes employing vector sensors have been developed. The first inversion scheme uses a combination of matched field processing and the difference of transmission losses between pressure and particle velocity. The second estimates the bottom sound speed and attenuation from the amplitudes of normal modes of particle velocities. Experimental results show that the developed geo-acoustic inversion methods with vector sensors can decrease the uncertainty of inversion in comparison with that by hydrophones.

In the application of underwater target orientation using two-dimensional differential pressure vector sensors, both the elements of the vector sensors and the compensation algorithm will introduce target orientation errors. In this paper, simulation results of these target orientation errors reveal that channel response inconsistency errors contribute much more influence to the orthogonal array compared with the SNR, the target incident angle, and the unit location displacement. Based on this observation, a compensation algorithm for orientation estimation error is proposed and testing results demonstrate the efficiency of the proposed algorithm.

To mitigate the reduced accuracy of direction-of-arrival (DOA) estimation in scenarios with low signal-to-noise ratios (SNR) and multiple interfering sources, this paper proposes an Auxiliary Classifier Generative Adversarial Network (ACGAN) architecture that integrates a Squeeze-and-Excitation (SE) attention mechanism and a Multi-scale Dilated Feature Aggregation (MDFA) module. In this neural network, a vector hydrophone array is employed as the receiving unit, capable of simultaneously sensing particle velocity signals in three directions and acoustic pressure , thereby providing high directional sensitivity and maintaining robust classification performance under low-SNR conditions. The MDFA module extracts features from multiple receptive fields, effectively capturing cross-scale patterns and enhancing the representation of weak targets in beamforming maps. This helps mitigate estimation bias caused by mutual interference among multiple targets in low-SNR environments. Furthermore, an auxiliary classification branch is incorporated into the discriminator to jointly optimize generation and classification tasks, enabling the model to more effectively identify and separate multiple types of labeled sources. Experimental results indicate that the proposed network is effective and shows improved performance across diverse scenarios.