Simultaneous Mapping and Localisation for Small Military Unmanned Underwater Vehicle

Paper proposes a simultaneous localisation and mapping (SLAM) scheme which is applicable to small military unmanned underwater vehicles (UUVs). The SLAM is a process which enables concurrent estimation of the position of UUV and landmarks in the environment through which the vehicle is passing. An unscented Kalman filter (UKF) is utilised to develop a SLAM suitable to nonlinear motion of UUV. A range sonar is used as a sensor to collect the relative position information of the landmark in the environment in which the UUV is navigating. The proposed SLAM scheme was validated through towing tank experiments about two degrees of freedom motion with UUV motion simulator and real range sonar system for small UUV. The results of these experiments showed that proposed SLAM scheme is capable of estimating the position of the UUV and the surrounding objects under real underwater environment.Defence Science Journal, 2012, 62(4), pp.223-227, DOI:http://dx.doi.org/10.14429/dsj.62.100

Optimal Operating Depth Search for Active Towed Array Sonar using Simulated Annealing

In an active towed array sonar, it is important to find the optimal operation depth. Generally, the optimal depth can be chosen via numerical simulations for all sonar depths and this imposes great burdens of time and cost.In this paper, an efficient approach is proposed to find the optimal depth using the optimisation technique. First, the sonar performance function is newly defined as a measure of how well the active sonar might perform. This function depends on the properties of the ocean environment and the positions of sonar and underwater target. Then, the simulated annealing to find an optimal solution for maximising sonar performance is used. The optimised depth agrees well with the depth obtained from direct searching for all depths of source and receiver combinations, but its computational time is largely reduced

Three-Dimensional Passive Source Localisation using the Flank Array of an Autonomous Underwater Vehicle in Shallow Water

Researchers have become interested in autonomous underwater vehicles equipped with various kinds of sonar systems that can perform many of underwater tasks, which is encouraged by the potential benefits of cost reduction and flexible deployment. This paper proposes an approach to three-dimensional passive source localisation with the flank array of an autonomous underwater vehicle in shallow water. The approach is developed based on matched-field processing for the likelihood of passive source localisation in the shallow water environment. Inter-position processing is also used for the improved localisation performance and the enhanced stability of the estimation process against the lack of spatial gain due to the small physical size of the flank array. The proposed approach is presented and validated through simulation and experimental data. The results illustrate the localisation performance at different signal-to-noise ratios and demonstrate the build up over time of the positional parameters of the estimated source as the autonomous underwater vehicle cruises at a low speed along a straight line at a constant depth.Defence Science Journal, 2013, 63(3), pp.323-330, DOI:http://dx.doi.org/10.14429/dsj.63.301

Triple Band Internal Antenna Using Matching Circuits

In this paper, a triple-band internal antenna for mobile handsets is present. Dual band operation is generated by using a band reject filter as a matching circuit and without any transformations on its radiator. Moreover, the antenna gives a wide impedance bandwidth in high band by using an additional chip capacitor so that the antenna can operate in triple-band with only one radiator. Details of the proposed antenna are presented

Hybrid Galerkin boundary element-wavenumber integration method for acoustic propagation in laterally inhomogeneous media

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Ocean Engineering, 1991.Includes bibliographical references (leaves 139-143).by Woojae Seong.Ph.D

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

Ray-based blind deconvolution with maximum kurtosis phase correction

© 2022 Acoustical Society of America.Ray-based blind deconvolution (RBD) is a method that estimates the source waveform and channel impulse response (CIR) using the ray arrival in an underwater environment. The RBD estimates the phase of the source waveform by using beamforming. However, low sampling, array shape deformation, and other factors can cause phase errors in the beamforming results. In this paper, phase correction is applied to the beamforming estimated source phase to improve RBD performance. The impulsiveness of the CIR was used as additional information to correct the initially estimated source phase. Kurtosis was used to measure impulsiveness, and the phase correction that maximized the kurtosis of the CIRs was calculated through optimization. The proposed approach is called ray-based blind deconvolution with maximum kurtosis phase correction (RBD-MKPC) and is based on a single-input multiple-output system. The RBD-MKPC was tested with several CIRs and source waveform combinations in the shallow-water acoustic variability experiment 2015 using broadband high-frequency pulses (11-31 kHz) as the source and a sparse vertical 16-element line array as receivers. The results indicate that the RBD-MKPC improves the estimation performance. In addition, from an optimization point of view and compared with other initialization methods, the proposed method showed superior convergence speed and estimation performance.N

Source Type Classification and Localization of Inter-Floor Noise with a Single Sensor and Knowledge Transfer between Reinforced Concrete Buildings

A convolutional neural network (CNN)-based inter-floor noise source type classifier and locator with input from a single microphone was proposed in [Appl. Sci. 9, 3735 (2019)] and validated in a campus building experiment. In this study, the following extensions are presented: (1) data collections of nearly 4700 inter-floor noise events that contain the same noise types as those in the previous work at source positions on the floors above/below in two actual apartment buildings with spatial diversity, (2) the CNN-based method for source type classification and localization of inter-floor noise samples in apartment buildings, (3) the limitations of the method as verified through several tasks considering actual application scenarios, and (4) source type and localization knowledge transfer between the two apartment buildings. These results reveal the generalizability of the CNN-based method to inter-floor noise classification and the feasibility of classification knowledge transfer between residential buildings. The use of a short and early part of event signal is shown as an important factor for localization knowledge transfer

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