SONAR Systems and Underwater Signal Processing: Classic and Modern Approaches

Because of the difficulties in accurately characterizing the statistical behavior of underwater acoustic channels, tolerance to uncertain statistical modeling is an important property for underwater acoustic signal processing procedures to possess. This paper provides an overview of a number of techniques developed in recent years that can be applied to provide uncertainty tolerance in underwater signal processing applications. In addition to a discussion of general concepts of uncertainty tolerance, specific methods for attaining tolerance to uncertainty in temporal/spatial statistics for procedures such as Wiener and matched filtering, time-delay estimation, sonar system design, and signal prediction are reviewed. Tolerance to uncertainty in amplitude statistics is also a key issue in underwater channels, and techniques for achieving this goal are discussed in the contexts of signal estimation, identification, and detection procedures.

In the past, both theoretical work and practical implementation of particle filtering (PF) method have been extensively studied. However, its application in underwater signal processing has received much less attention. This paper intends to introduce PF approach for underwater acoustic signal processing. Particularly, we are interested in direction of arrival (DOA) estimation using PF. A detailed introduction along with this perspective is presented in this paper. Since the noise usually spreads the mainlobe of likelihood function and causes problem in subsequent particle resampling step, an exponential weighted likelihood model is developed to emphasize particles at more relevant area. Hence, the the effect due to background noise can be reduced. Real underwater acoustic data collected in SWELLEx-96 experiment are employed to demonstrate the performance of the proposed PF approaches for underwater DOA tracking.

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The detection of tonal signals with unknown frequencies is an important area of study in underwater signal processing. A common approach to address this issue is to use the Discrete Fourier Transform (DFT) for observations. When a tone does not lie precisely at the discrete DFT frequency point, its energy will leak to adjacent frequency point. This phenomenon is known as scalloping loss or Picket Fence Effect (PFE). PFE leads to the degradation of detection performance based on DFT. This paper studies the problem of robust detection in the case of PFE. A coherently-averaged power processor utilizing the information of adjacent frequency bins is designed. The results of simulations and experiments show that the proposed method is robust against PFE, and is highly suitable for tone detection in practical circumstances.

Present day SAW devices can be used as extremely fast analogue processors with speeds of several tens of Mega-samples per second. Such speeds are required in low frequency multichannel or two-dimensional systems like sonar systems when real time operation is wanted. SAW processors can then be employed provided adequate I/O and interface circuits perform the necessary multiplexing and/or time scaling of the input signals. Compatible digital circuits now exist and full processors can be built which couple both technologies. This presentation will review the basics concerning the SAW and digital technologies. It will next show how they can be applied to particular sonar systems.

Recent breakthroughs in computational capabilities and optimization algorithms have enabled a new class of signal processing approaches based on deep neural networks (DNNs). These algorithms have been extremely successful in the classification of natural images, audio, and text data. In particular, a special type of DNNs, called convolutional neural networks (CNNs) have recently shown superior performance for object recognition in image processing applications. This paper discusses modern training approaches adopted from the image processing literature and shows how those approaches enable significantly improved performance for synthetic aperture radar (SAR) automatic target recognition (ATR). In particular, we show how a set of novel enhancements to the learning algorithm, based on new stochastic gradient descent approaches, generate significant classification improvement over previously published results on a standard dataset called MSTAR.

Several review articles on sonar system technology have provided a detailed description of the mainstream sonar signal processing functions along with the associated implementation considerations. This chapter attempts to extend the scope of these articles by introducing an implementation effort of nonmainstream processing schemes in real-time sonar systems. It provides a historical overview of sonar systems and introduces the concept of the signal processor unit and its general capabilities. The chapter discusses the sonar problem and introduces the development of a realizable generic processing scheme that allows the implementation and testing of nonlinear processing techniques in a wide spectrum of real-time active and passive sonar systems. It presents a concept demonstration of both the sonar technology and advanced signal processing concepts that are proving invaluable in the reduction risk and in ensuing significant innovations occur during the formal development process.

Do postmodern approaches replace the modern approaches while the classical approach is replaced by modern approaches in education? It seems that the changing needs of the societies lead to the creation of new paradigms. Our paradigms which derived from preexisting knowledge, experiences, and implied conditioning, constitute our ‘way to perceive a situation’ and manage our perspective which directs our behaviors. Thus, globalization, accelerating the flow of information through the internet network, should change the way we observe a situation and our perspective on education and educators. As a continuation of this, our point of view should change our behavior. Success is a combination of many factors mentioned above, which are related to each other. Hence, the study intends to examine the utmost prominent factors in the education systems that may be taken as an example and after comparing these factors with Turkey’s status, the study intends to investigate which lessons could be taken from this knowledge.

This article discusses federalism and deferent theories related to it. Federalism as a theory, as a concept and as an organizational structure refers to the distribution of powers between the two levels of the government. It stands for unity, cooperation, and interdependence between the central government and the component units. Being dynamic in nature, it adopts changes according to the prevailing conditions of the country. An attempt has been made to elucidate federalism from two broad perspectives of the classical approach and Modern approach. The classical approach restricts federalism to cooperation and independence with quasi federalism, coupled with the element of encroachment from the federal government. This approach is merely based upon the doctrine of legal formalism or formalism fallacy. The modern approach focuses on two cardinal principles of cooperation and interdependence. The modern theory is a broad term and encompasses many forms of federalism within its ambit.

The conventional controllers used for DC machines are static and their parameters are fixed through proper design. The classical approach is to use a PID controller with constant parameters after analyzing the stability criterion. The modern approach is to use controllers based on fuzzy logic or other AI techniques. The authors have chosen a speed-tracking problem where a DC machine has to follow a time varying speed demand. The controller coefficients are fixed through an evolutionary algorithm. Representative values of steady state error, maximum overshoot and transient rise time are computed through feature extraction algorithms. Now, the fitness of each member is computed as a fuzzy value based on some predefined fuzzy functions involving the feature values. This fuzzy fitness value governs the selection of coefficients through a genetic algorithm until convergence is obtained. The performance has been studied with various fitness functions and the results are found to be satisfactory.

The authors have explained the idea and development of a WordPress content management system. The benefits and capabilities of using a custom template for a specific web solution have been highlighted. We have explained how to develop a custom template based on two approaches. The basic stages of developing a custom template with the classic approach and a modern approach have been displayed where the WordPress REST API and Vue.js framework are being used to develop the template. We have also mentioned how plugins can facilitate and enhance a website.

Multibeam sonar is a technique used in many applications in offshore activities. Sonar consists of an integrated system of components: acoustic transducers, signal transmitters and receivers, and signal processing circuits. Numerical prototyping by model-based design (MBD) is widely employed to develop various instruments, which could also be useful in designing signal processing algorithms and configurations of sonar systems. A sonar system is divided roughly into two subsystem blocks, block 1: electric circuits and signal processing subsystem, and block 2: acoustic subsystem. The design of block 2 requires numerical simulation of an ultrasound wave reflected by targets. One issue for MBD of multibeam sonar is to study a high speed and low cost computational method to simulate echo signals. Numerical computation methods, such as finite-element method, require significant computational resources to solve a large acoustic field. However, multibeam sonar is used for targets which are typically located away from transducers. Therefore, equations to simulate projected and received ultrasonic waves can be simplified by a far field approximation. This paper proposes a numerical simulation method of acoustic echo signals between transducers and targets for MBD. Finally, the resulting processed images are shown from the results calculated by the entire MBD system.

A number of at-sea measurements have shown that fluctuations in the amplitude of passive sonar signals are affected by source depth, range, and the propagation path from the source to the receiver. However, to date, received signal statistics have not been incorporated into passive sonar classification algorithms because the field results have not been accompanied by development of a statistics-based classifier architecture whose performance can be assessed using accepted signal processing metrics, e.g., a receiver operating characteristic curve. The performance of a statistics-based classifier depends critically upon statistical knowledge of the received signal, which in turn depends upon statistical knowledge of relevant environment parameters (sound speed in the water column and sediment, for example). Thus the classifier architecture must make use of the environmental information which in reality is only approximately known and must be described in statistical terms. Given a statistical description of the environment, an ocean acoustic propagation model and Monte Carlo simulation can be used to predict received signal statistics. Once received signal statistics are available for the different classes, a classifier can be designed. Here we examine a likelihood ratio binary source depth classifier for passive sonar. [Work sponsored by ONR Undersea Signal Processing.]

Cyclostationarity Based Sonar Signal Processing

Classical and Modern Approaches in the Theory of Mechanisms