Sonar Processing I
Wednesday 13th May 2015, 1400hrs–1530hrs
Chaired by Bernard Myers
Recursive Bayesian Synthetic Aperture Geoacoustic Inversion
Dr Bien Aik Tan, DSO National Laboratories, Singapore
Geoacoustic inversion optimizes the waveguide geoacoustic model parameters by minimizing the difference between the measured and the modeled acoustic fields. In doing this, seafloor properties are estimated without resorting to costly direct measurements such as coring. Knowing the seafloor acoustic properties is important for various applications such as sonar performance prediction and operation, source localization and detection and classification of underwater man-made objects. However, a typical geoacoustic inversion procedure involves powerful source transmissions received on a large- aperture receiver array. A more practical approach is to use a single moving source and/or receiver in a low signal to noise ratio (SNR) setting. This paper uses single-receiver, broadband, frequency coherent matched-field inversion and exploits coherently repeated transmissions to improve estimation of the geoacoustic parameters. The long observation time improves the SNR and creates a synthetic aperture due to relative source-receiver motion. To model source/receiver horizontal motion, waveguide Doppler theory for normal modes is necessary. Here a recursive Bayesian estimation approach is developed that coherently processes the data pulse by pulse and incrementally updates estimates of parameter uncertainty. It also approximates source/receiver acceleration by assuming piecewise constant but linearly changing source/receiver velocities. When the source/receiver acceleration exists, it is shown that modeling acceleration can reduce further the parameter estimation biases and uncertainties. The method is demonstrated in simulation and in the analysis of low SNR, 100–900Hz linear frequency modulated (LFM) pulses from the Shallow Water 2006 experiment.
Signal Processing for Underwater Intrusion Detection based on Passive Sonar
Mr Yohei Kawaguchi, Hitachi, Ltd., Japan
It is important to protect coastal critical infrastructures such as airports, harbors, electric plants, etc. against terrorist intrusion from underwater. The authors are developing a technology for detecting terrorist intrusion. Using active sonar, divers can be detected even if they are more than a thousand meters away from arrays. Passive sonar has advantages over active sonar, such as cost effectiveness, environmentally friendliness, etc. However, signal-to-noise-ratio (SNR) of passive sonar is lower than that of active sonar. Aiming to improve SNR for passive sonar-based intrusion detection, we propose a new method for noise reduction. There are two kinds of the conventional approaches for noise reduction: One is the beamforming-based linear filter for reducing the directional noise. The other is the nonlinear filter based on spectral subtraction for reducing the diffuse noise. However, the noise reduction performance is low in the conventional cascade combination of these filters because they cannot be optimized simultaneously. To solve this problem, the proposed method is based on a time-variant multichannel Wiener filter, in which both the linear filter and nonlinear one can be optimized simultaneously. Results of experimental evaluation demonstrate that the noise reduction performance of the proposed method is higher than the conventional approach. Furthermore, we show a prototype of the intrusion detection system consisting of noise reduction, direction-of-arrival estimation, localization, and multi-target tracking.
Object Recognition System without Machine Learning Schemes
Mr Yuma Matsuda, NEC Corporation, Japan
This paper proposes a promising new system of recognizing objects without using machine learning schemes. In the maritime environment, the shape patterns of objects, which are usually occluded or covered with noise, unpredictably and dynamically change. In order to recognize objects in such an environment, unpredictably and dynamically changing patterns are required to be predicted or learned preliminary. In other words, a huge number of sample patterns are required to be learned preliminary. However, it is not easy to preliminary observe a huge variety of patterns especially in the maritime environment, which is not easy to approach. As a result, naval detection systems with reliable performance have not yet been established. The human visual system, on the other hand, performs well under unpredictably and dynamically changing environment. Using the human visual system as a reference, we have already proposed a promising approach of recognizing objects. Our approach consists of two steps. One is an image segmentation step, which extracts object areas in images autonomously. The other is, then, a shape representation and matching step, which represents the object areas and decides an area of them and another is similar or not. Our approach has already been shown to achieve great performance using examples of side-scan sonar images and satellite images even without any machine learning scheme. In this paper, we introduce our object recognition system with great performance even without using any machine learning scheme. We, finally, introduce the mechanism to achieve such a great performance using abundant examples.
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