V. N. Hari

Design and performance analysis of a signal detector based on suprathreshold stochastic resonance

This paper presents the design and performance analysis of a detector based on suprathreshold stochastic resonance (SSR) for the detection of deterministic signals in heavy-tailed non-Gaussian noise. The detector consists of a matched filter preceded by an SSR system which acts as a preprocessor. The SSR system is composed of an array of 2-level quantizers with independent and identically distributed (i.i.d) noise added to the input of each quantizer. The standard deviation @s of quantizer noise is chosen to maximize the detection probability for a given false alarm probability. In the case of a weak signal, the optimum @s also minimizes the mean-square difference between the output of the quantizer array and the output of the nonlinear transformation of the locally optimum detector. The optimum @s depends only on the probability density functions (pdfs) of input noise and quantizer noise for weak signals, and also on the signal amplitude and the false alarm probability for non-weak signals. Improvement in detector performance stems primarily from quantization and to a lesser extent from the optimization of quantizer noise. For most input noise pdfs, the performance of the SSR detector is very close to that of the optimum detector.

Narrowband signal detection techniques in shallow ocean by acoustic vector sensor array

This paper presents the formulation and performance analysis of four techniques for detection of a narrowband acoustic source in a shallow range-independent ocean using an acoustic vector sensor (AVS) array. The array signal vector is not known due to the unknown location of the source. Hence all detectors are based on a generalized likelihood ratio test (GLRT) which involves estimation of the array signal vector. One non-parametric and three parametric (model-based) signal estimators are presented. It is shown that there is a strong correlation between the detector performance and the mean-square signal estimation error. Theoretical expressions for probability of false alarm and probability of detection are derived for all the detectors, and the theoretical predictions are compared with simulation results. It is shown that the detection performance of an AVS array with a certain number of sensors is equal to or slightly better than that of a conventional acoustic pressure sensor array with thrice as many sensors.

A Decoupled Approach for Near-Field Source Localization Using a Single Acoustic Vector Sensor

This paper considers the problem of three-dimensional (3-D, azimuth, elevation, and range) localization of a single source in the near-field using a single acoustic vector sensor (AVS). The existing multiple signal classification (MUSIC) or maximum likelihood estimation (MLE) methods, which require a 3-D search over the location parameter space, are computationally very expensive. A computationally simple method previously developed by Wu and Wong (IEEE Trans. Aerosp. Electron. Syst. 48(1):159–169, 2012), which we refer to as Eigen-value decomposition and Received Signal strength Indicator-based method (Eigen-RSSI), was able to estimate 3-D location parameters of a single source efficiently. However, it can only be applied to an extended AVS which consists of a pressure sensor separated from the velocity sensors by a certain distance. In this paper, we propose a uni-AVS MUSIC (U-MUSIC) approach for 3-D location parameter estimation based on a compact AVS structure. We decouple the 3-D localization problem into step-by-step estimation of azimuth, elevation, and range and derive closed-form solutions for these parameter estimates by which a complex 3-D search for the parameters can be avoided. We show that the proposed approach outperforms the existing Eigen-RSSI method when the sensor system is required to be mounted in a confined space.

Spatial Modeling of Deep-Sea Ferromanganese Nodules With Limited Data Using Neural Networks

Deep-sea ferromanganese nodules found in the Clarion–Clipperton zone (CCZ) in the Pacific ocean are a large potential source of metals such as nickel, cobalt, and manganese. Spatial modeling of these nodules is essential to obtain a better scientific understanding about their formation and distribution, and conduct feasibility studies on their exploitation. However, data on the quantitative and qualitative distribution of nodules in CCZ are sparse and often not divulged, and the accuracy of conventional spatial modeling techniques is limited by this scarcity of data. We present an approach based on artificial neural networks for modeling nodule parameters in the CCZ using the limited data available in the open domain. Our models predictions are comparable to benchmark predictions from the International Seabed Authority which used a more extensive data set. Moreover, our model can predict small as well as large-scale variations of nodules, which are essential in making evaluations for deep-sea harvesting. We discuss the contribution of each factor in the modeling, and show that small-scale nodule parameter variations can be effectively predicted by incorporating the local topography.

A vector sensing scheme for underwater acoustics based on particle velocity measurements

We present a particle velocity sensing scheme for underwater acoustic vector sensors, which utilizes a natureinspired bionic hair based transduction scheme. The hair based sensor offers an attractive option for particle velocity sensing with low sensor size, but it is limited by low sensitivity. This limitation can be overcome by using an acoustic horn for amplification, and using a sensitive measurement system such as a fibre optic system. We undertake simulation studies to evaluate the feasibility of the vector sensing scheme. For the small sensor sizes and low frequencies considered, the effects of viscosity become dominant. We discuss the effects of viscosity through parameteric studies and its effect on determining the operating parameters of the sensing scheme.

Particle filtering for channel parameter tracking in a noisy shallow ocean environment using a vertical array

Acoustic signals in a shallow ocean environment are severely distorted due to the time-varying and inhomogeneous nature of the propagation channel. In this paper, a state-space model is introduced to characterize the uncertainties of the shallow ocean and a Rao-Blackwellized particle filter (RBPF) is developed to estimate the model parameters. Since both modal functions and horizontal wave numbers of the channel are assumed unknown, the state-space model has a high nonlinearity and high dimensionality. As the modal functions are linear with the measurements conditioning on the horizontal wave numbers, a Kalman filtering (KF) is employed to marginalize out the modal functions. Hence only the horizontal wave numbers need to be estimated by using a PF. Simulation results show that the proposed RBPF algorithm significantly outperforms the existing approaches.

Near-field/far-field source localization in ocean with an acoustic vector sensor array using polynomial rooting

In this paper, we consider the problem of three-dimensional (3D - azimuth, elevation/depth and range) localization of a single acoustic source using an array of acoustic vector sensors (AVS). Localization algorithms such as multiple signal classification (MUSIC) require a 3D search over the location parameter space which is computationally expensive. Several methods have been proposed based on arrays of acoustic pressure sensors (APS) [1], [2] which simplified this search to polynomial rooting (PR) combined with a 2D search in the range-elevation/depth space. However, the computational complexity remains high for such methods since 2D search is involved. We present a method to localize a source with an array of AVS via a decoupled estimation of azimuth, and a 1D range search combined with PR to estimate the elevation/depth. This is computationally simpler than, and performs as well as PR with 2D searching and 3D MUSIC.

An optimal importance sampling based particle filtering for channel parameter estimation in shallow ocean

Estimating channel parameters in a shallow ocean environment is challenging due to low signal-to-noise ratio (SNR), multi-path effect and time-varying nature of ocean. In this paper, a Bayesian framework and its particle filtering (PF) implementation are introduced to cope with this problem. At each time step, the particles are sampled according to a random walk model, and then evaluated by the corresponding importance weights. An extended Kalman filter (EKF) is incorporated to achieve an optimal importance sampling, by which the states are coarsely estimated and the particles are relocated. As such the particles are more likely drawn at the relevant area and can be resampled more efficiently. Experiments show that the proposed EKF-PF tracking algorithm significantly outperforms the traditional tracking approaches in challenging environments.