Compressive Sound Speed Profile Inversion Using Beamforming Results

Sound speed profile (SSP) significantly affects acoustic propagation in the ocean. In this work, the SSP is inverted using compressive sensing (CS) combined with beamforming to indicate the direction of arrivals (DOAs). The travel times and the positions of the arrivals can be approximately linearized using their Taylor expansion with the shape function coefficients that parameterize the SSP. The linear relation between the travel times/positions and the shape function coefficients enables CS to reconstruct the SSP. The conventional objective function in CS is modified to simultaneously exploit the information from the travel times and positions. The SSP is estimated using CS with beamforming of ray arrivals in the SWellEx-96 experimental environment, and the performance is evaluated using the correlation coefficient and mean squared error (MSE) between the true and recovered SSPs, respectively. Five hundred synthetic SSPs were generated by randomly choosing the SSP dictionary components, and more than 80 percent of all the cases have correlation coefficients over 0.7 and MSE along depth is insignificant except near the sea surface, which shows the validity of the proposed method

Detection of Direction-Of-Arrival in Time Domain Using Compressive Time Delay Estimation with Single and Multiple Measurements

The compressive time delay estimation (TDE) is combined with delay-and-sum beamforming to obtain direction-of-arrival (DOA) estimates in the time domain. Generally, the matched filter that detects the arrivals at the hydrophone is used with beamforming. However, when the ocean noise smears the arrivals, ambiguities appear in the beamforming results, degrading the DOA estimation. In this work, compressive sensing (CS) is applied to accurately evaluate the arrivals by suppressing the noise, which enables the correct detection of arrivals. For this purpose, CS is used in two steps. First, the candidate time delays for the actual arrivals are calculated in the continuous time domain using a grid-free CS. Then, the dominant arrivals constituting the received signal are selected by a conventional CS using the time delays in the discrete time domain. Basically, the compressive TDE is used with a single measurement. To further reduce the noise, common arrivals over multiple measurements, which are obtained using the extended compressive TDE, are exploited. The delay-and-sum beamforming technique using refined arrival estimates provides more pronounced DOAs. The proposed scheme is applied to shallow-water acoustic variability experiment 15 (SAVEX15) measurement data to demonstrate its validity

Underwater Acoustic Signal Detection Using Calibrated Hidden Markov Model with Multiple Measurements

It is important to find signals of interest (SOIs) when operating sonar systems. A threshold-based method is generally used for SOI detection. However, it induces a high false alarm rate at a low signal-to-noise ratio. On the other side, machine-learning-based detection is performed to obtain more reliable detection results using abundant training data, costing intensive time and labor. We propose a method with favorable detection performance by using a hidden Markov model (HMM) for sequential acoustic data, which requires no separate training data. Since the detection results from HMM are significantly affected by the random initial parameters of HMM, the genetic algorithm (GA) is adopted to reduce the sensitivity of the initial parameters. The tuned initial parameters from GA are used as a start point for the subsequent Baum–Welch algorithm updating the HMM parameters. Furthermore, multiple measurements from arrays are exploited both in determining the proper initial parameters with GA and updating the parameters with the Baum–Welch algorithm. In contrast to the standard random selection of the initial point with single measurement, a stable initial point setting by the GA ensures improved SOI detections with the Baum–Welch algorithm using the multiple measurements, which are demonstrated in passive and active acoustic data. Particularly, the proposed method shows the most confidential detection in finding weak elastic surface waves from target, compared to existing methods such as conventional HMM

Passive Sonar Target Identification Using Multiple-Measurement Sparse Bayesian Learning

Accurate estimation of the frequency component is an important issue to identify and track marine objects (e.g., surface ship, submarine, etc.). In general, a passive sonar system consists of a sensor array, and each sensor receives data that have common information of the target signal. In this paper, we consider multiple-measurement sparse Bayesian learning (MM-SBL), which reconstructs sparse solutions in a linear system using Bayesian frameworks, to detect the common frequency components received by each sensor. In addition, the direction of arrival estimation was performed on each detected common frequency component using the MM-SBL based on beamforming. The azimuth for each common frequency component was confirmed in the frequency-azimuth plot, through which we identified the target. In addition, we perform target tracking using the target detection results along time, which are derived from the sum of the signal spectrum at the azimuth angle. The performance of the MM-SBL and the conventional target detection method based on energy detection were compared using in-situ data measured near the Korean peninsula, where MM-SBL displays superior detection performance and high-resolution results

Engineering Characteristics of Chemically Treated Water-Repellent Kaolin

Water-repellent soils have a potential as alternative construction materials that will improve conventional geotechnical structures. In this study, the potential of chemically treated water-repellent kaolin clay as a landfill cover material is explored by examining its characteristics including hydraulic and mechanical properties. In order to provide water repellency to the kaolin clay, the surface of clay particle is modified with organosilanes in concentrations (CO) ranging from 0.5% to 10% by weight. As the CO increases, the specific gravity of treated clay tends to decrease, whereas the total organic carbon content of the treated clay tends to increase. The soil-water contact angle increases with an increase in CO until CO = 2.5%, and then maintains an almost constant value (≈134.0°). Resistance to water infiltration is improved by organosilane treatment under low hydrostatic pressure. However, water infiltration resistance under high hydrostatic pressure is reduced or exacerbated to the level of untreated clay. The maximum compacted dry weight density decreases with increasing CO. As the CO increases, the small strain shear modulus increases, whereas the effect of organosilane treatment on the constrained modulus is minimal. The results indicate that water-repellent kaolin clay possesses excellent engineering characteristics for a landfill cover material