The shallow water environment can be considered as a time-dispersive system whose time-varying impulse response can be expressed as a superposition of time-frequency components with dispersive characteristics. In this paper, we propose a frequency-domain characterization of the shallow water system based on the normal-mode model that treats the system as an acoustic medium. After studying the dispersive characteristics of this system, a blind time-frequency processing technique is employed to separate the normal-mode components without knowledge of the relevant environment parameters. This technique is based on first approximating the time-frequency structure of the received signal and then designing time-frequency separation filters based on warping techniques. Following this method, two types of receivers are developed to exploit the diversity of the shallow water channel and to improve underwater communications performance. Simulation results demonstrate the dispersive characterization of this system and the improved processing performance of the receiver schemes.
The detection and localization of transient signals is nowadays a typical point of interest when we consider the multitude of existing transient sources, such as electrical and mechanical systems, underwater environments, audio domain, seismic data, and so forth. In such fields, transients carry out a lot of information. They can correspond to a large amount of phenomena issued from the studied problem and important to analyze (anomalies and perturbations, natural sources, environmental singularities, . . .). They usually occur randomly as brief and sudden signals, such as partial discharges in electrical cables and transformers tanks. Therefore, motivated by advanced and accurate analysis, efficient tools of transients detection and localization are of great utility. Higher order statistics, wavelets and spectrogram distributions are well known methods which proved their efficiency to detect and localize transients independently to one another. However, in the case of a signal composed by several transients physically related and with important energy gap between them, the tools previously mentioned could not detect efficiently all the transients of the whole signal. Recently, the generalized complex time distribution concept has been introduced. This distribution offers access to highly concentrated representation of any phase derivative order of a signal. In this paper, we use this improved phase analysis tool to define a new concept to detect and localize dependant transients taking regard to the phase break they cause and not their amplitude. ROC curves are calculated to analyze and compare the performances of the proposed methods.
This paper presents some general considerations on the transient phenomena in power networks which are a key point in the security of any power supply system. In this context, some detection techniques employed in their analysis will be addressed. Among these techniques, a special attention will be given to the analysis of the transient signals associated with the partial discharges (PDs), as follows : spectrogram, high order statistics (HOS) with wavelets and complex time distribution. Results on realistic data will point out on the potential of each type of method.
When the ocean seabed is considered to be rigid, the ideal waveguide model can be used to model the shallow water environment. However, a more realistic ocean waveguide model treats the ocean floor as a boundary between two different fluid media. In this paper, a frequency-domain characterization of shallow water environments is proposed based on this realistic waveguide model with a fluid boundary. First, the time-frequency characteristics of this model are studied as well as the impact of the environment parameters on the dispersive phenomena. Then a frequency-domain matched filter receiver is designed to obtain time-dispersion diversity once we separate the modes using warping techniques in the time-frequency plane. Simulations demonstrate that the new receiver design improves the bit error rate performance.
The characterization of a natural environment (underwater, for example) and the identification of radar/communication signals in SIGINT (signal intelligence) are just two typical examples of applications requiring signal analysis in a passive configuration. In the first case, even if the characterization is based on the analysis of received signals in an active configuration, the unknown deformations of the transmitted signal transform the signal processing problem in a passive context one. Concerning the second case, the passive behavior of the signal intelligence field is a well-known problem in the electronic warfare problem.In this paper we propose a general signal analysis framework in passive context. We show that, in spite of the differences between some possible passive applications (underwater channel characterization and SIGINT) a unified signal analysis framework can defined. This definition starts from the general observation that real life signals received in a passive configuration are non-stationary. Their analysis in the time-frequency domain is well adapted so that it offers appropriated structures which are good candidates for the information post-processing. In a passive context, the definition of an appropriate time-frequency representation space is a complex problem, mainly related to the lack of a priori information about the processed signal. One general solution is proposed in this paper and it is based on the timefrequency-phase coherence. Conceptually, while the received signals are unknown (a model is difficult to be assumed), a general remark is the coherent shapes of their time-frequency structures. This coherence could be materialized by fundamental physical parameter of every signal -amplitude, time, frequency and initial phase.Indeed, the signal analysis framework is defined through three blocks : detection of regions of interest, segmentation and separation, analytical characterization. This architecture is mainly based on joint use of time, frequency and local phase analysis. More precisely, the phase information will be locally analysed, using generalized instantaneous moments, on the time-frequency regions previously selected thanks to the time-frequency grouping algorithm.This architecture constitutes an efficient scheme to solve the constraints brought by this type of signals with a complex time-frequency behavior and by the human operator to reduce his tasks in the decision process. Examples from underwater behavior (underwater mammals vocalizations) and electronic warfare will prove the efficiency of the proposed approach.
The shallow water environment can be characterized as a time-dispersive system whose time-varying impulse response can be expressed as a superposition of time-frequency components with dispersive structures. In this paper, a blind timefrequency processing technique is employed to separate these components without knowledge of environmental parameters. This technique is based on first approximating the time-frequency structures of the received signal, and then designing separation filters based on time-frequency warping techniques. Based on this method, a receiver is developed to exploit the diversity of the channel and to improve communications performance.
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