Russell E. Trahan

Fractional Fourier transform for sonar signal processing

In this paper we present an approach for processing of sonar signals with the ultimate goal of ocean bottom sediment classification. Work reported is based on sonar data collected by the volume search sonar (VSS) in the Gulf of Mexico, as well as on VSS synthetic data. The volume search sonar is a beam formed multibeam sonar system with 27 fore and 27 aft beams, covering almost the entire water volume (from above horizontal, through vertical, back to above horizontal). Our investigation is focused on the bottom-return signals since we are interested in determination of the impulse response of the ocean bottom floor. The bottom-return signal is the convolution between the impulse response of the bottom floor and the transmitted sonar chirp signal. The method developed here is based on fractional Fourier transform, a fundamental tool for signal processing and optical information processing. Fractional Fourier transform is a generalization of the classical Fourier transform. The traditional Fourier transform decomposes signal by sinusoids whereas Fractional Fourier transform corresponds to expressing the signal in terms of an orthonormal basis formed by chirps. In recent years, interest in and use of time-frequency tools have increased and become more suitable for sonar applications. The fractional Fourier transform requires finding the optimum order of the transform that can be estimated based on the properties of the chirp signal. The bottom impulse response is given by the magnitude of the fractional Fourier transform applied to the bottom return signal. The technique used in this work has been tested both on synthetic data and real sonar data collected by the VSS. The synthetic sonar return signal has been generated by the convolution between the Green function, which has been utilized to simulate the impulse response of the seafloor and the transmitted VSS chirp. A study is carried out to compare the performance of our method to a conventional method based on deconvolution in the frequency domain (using standard Fourier transform). The amplitude and shape of an acoustic signal reflected from the sea floor is determined mainly by the seabottom roughness, the density difference between water and the sea floor, and reverberation within the substrate. Since the distribution of seafloor types is a very important tool in different applications, a sediment classification has been implemented based on a statistical analysis of the obtained impulse response. In order to perform a robust analysis of the signal, a joint time-frequency analysis is necessary. In this paper the analysis has been evaluated using the Wigner distribution, which can be thought of as a signal energy distribution in joint time-frequency domain. Singular value decomposition of the Wigner distribution has been used in order to perform the seafloor sediment classification. A comparative analysis of the experimental results for classical deconvolution and fractional Fourier method is presented. Results are shown and suggestions for future work are provided

Sonar signal enhancement using fractional Fourier transform

In this paper we present an approach for signal enhancement of sonar signals. Work reported is based on sonar data collected by the Volume Search Sonar (VSS), as well as VSS synthetic data. The Volume Search Sonar is a beamformed multibeam sonar system with 27 fore and 27 aft beams, covering almost the entire water volume (from above horizontal, through vertical, back to above horizontal). The processing of a data set of measurement in shallow water is performed using the Fractional Fourier Transform algorithm. The proposed technique will allow efficient determination of seafloor bottom characteristics and bottom type using the reverberation signal. A study is carried out to compare the performance of the presented method with conventional methods. Results are shown and future work and recommendations are presented.

Acoustic Seabed Classification using Fractional Fourier Transform and Time-Frequency Transform Techniques

In this paper we present an approach for processing sonar signals with the ultimate goal of ocean bottom sediment classification. Work reported is based on sonar data collected by the Volume Search Sonar (VSS), one of the five sonar systems in the AN/AQS-20. Our technique is based on the Fractional Fourier Transform (FrFT), a time-frequency analysis tool which has become attractive in signal processing. Because FrFT uses linear chirps as basis functions, this approach is better suited for sonar applications. The magnitude of the bottom impulse response is given by the magnitude of the Fractional Fourier transform for optimal order applied to the bottom return signal. Joint time-frequency representations of the signal offer the possibility to determine the time-frequency configuration of the signal as its characteristic features for classification purposes. The classification is based on singular value decomposition of the Choi William distribution applied to the impulse response obtained using Fractional Fourier transform. The set of the singular values represents the desired feature vectors that describe the properties of the signal. The singular value spectrum has a high data reduction potential. It encodes the following signal features: time-bandwidth product, frequency versus time dependence, number of signal components and their spacing. The spectrum is invariant to shifts of the signal in time and frequency and is well suited for pattern recognition and classification tasks. The most relevant features (singular values) have been mapped in a reduced dimension space where an unsupervised classification has been employed for acoustic seabed sediment classification. The theoretical method is addressed and then tested on field sonar data. In our classification we used the central beams. Good agreement between the experimental results and the ground truth is shown. A performance comparison of our method to a k-means based technique is also given. Results and recommendations for future work are presented

fiber optic sensing applications in the electric power industry

Abstract Fiber optic telecommunications is a well-established discipline which finds applications in all industrial areas for signaling and control. However, fiber optics for intrinsic sensing of fluid levels, gas presence, temperature, pressure, or rotation, is rapidly being applied to the medical, petrochemical, and marine professions. Not to be overlooked are several new fiber optic sensing techniques which are now capable of accurately measuring electric current, voltage levels, and even breaker status. By monitoring such parameters, the power industry can take full advantage of the benefits of fiber optics, especially in high electromagnetic field environments. This paper surveys those techniques and experiences with fiber optic sensing applicable to todays power industry.

Dynamic analysis of substation busbar structures

Abstract The usual method used to design substation busbar structures is to analyze the short circuit force loading with an equivalent static loading. However, it is shown here that the relatively slow response modes of the structure require that a dynamic analysis be performed for the higher frequency short circuit forces. Full scale test data is compared to simulation results obtained from a finite element model.

Time-frequency transform techniques for seabed and buried target classification

An approach for processing sonar signals with the ultimate goal of ocean bottom sediment classification and underwater buried target classification is presented in this paper. Work reported for sediment classification is based on sonar data collected by one of the AN/AQS-20s sonars. Synthetic data, simulating data acquired by parametric sonar, is employed for target classification. The technique is based on the Fractional Fourier Transform (FrFT), which is better suited for sonar applications because FrFT uses linear chirps as basis functions. In the first stage of the algorithm, FrFT requires finding the optimum order of the transform that can be estimated based on the properties of the transmitted signal. Then, the magnitude of the Fractional Fourier transform for optimal order applied to the backscattered signal is computed in order to approximate the magnitude of the bottom impulse response. Joint time-frequency representations of the signal offer the possibility to determine the timefrequency configuration of the signal as its characteristic features for classification purposes. The classification is based on singular value decomposition of the time-frequency distributions applied to the impulse response. A set of the largest singular values provides the discriminant features in a reduced dimensional space. Various discriminant functions are employed and the performance of the classifiers is evaluated. Of particular interest for underwater under-sediment classification applications are long targets such as cables of various diameters, which need to be identified as different from other strong reflectors or point targets. Synthetic test data are used to exemplify and evaluate the proposed technique for target classification. The synthetic data simulates the impulse response of cylindrical targets buried in the seafloor sediments. Results are presented that illustrate the processing procedure. An important characteristic of this method is that good classification accuracy of an unknown target is achieved having only the response of a known target in the free field. The algorithm shows an accurate way to classify buried objects under various scenarios, with high probability of correct classification.

Acoustic seabed classification using fractional Fourier transform

In this paper we present a time-frequency approach for acoustic seabed classification. Work reported is based on sonar data collected by the Volume Search Sonar (VSS), one of the five sonar systems in the AN/AQS-20. The Volume Search Sonar is a beamformed multibeam sonar system with 27 fore and 27 aft beams, covering almost the entire water volume (from above horizontal, through vertical, back to above horizontal). The processing of a data set of measurement in shallow water is performed using the Fractional Fourier Transform algorithm in order to determine the impulse response of the sediment. The Fractional Fourier transform requires finding the optimum order of the transform that can be estimated based on the properties of the transmitted signal. Singular Value Decomposition and statistical properties of the Wigner and Choi-Williams distributions of the bottom impulse response are employed as features which are, in turn, used for classification. The Wigner distribution can be thought of as a signal energy distribution in joint time-frequency domain. Results of our study show that the proposed technique allows for accurate sediment classification of seafloor bottom data. Experimental results are shown and suggestions for future work are provided.

The spatial amplitude mapping method for estimation of time delay using adaptive filtering

Uniform and multiple delays/advances were estimated under heavy noisy conditions [signal‐to‐noise‐ratio (SNR=signal energy/noise energy) below 0 dB]. The technique introduced, called the spatial amplitude mapping (SAM) method, isolates a data segment from each of the channels in a multichannel system by using a suitable window. When two matched segments are plotted on an x–y plane, the distribution of every pair of windowed coordinates will remain near a straight 45° line that passes through the origin. The distribution of two nonmatched segments or two matched segments containing noise will scatter around this line; hence, a pair of matching windowed segments can be found by searching for the distribution closest to the 45° line. This information is then used to estimate the segment delays. Under very noisy conditions, however, multiple delays can be detected. A recursive least‐squares (RLS) filter is then used to adaptively estimate the correct delay. The technique was implemented in the delay estimatio...