Fractional Fourier Transform for Sonar Signal Processing

Barbu, Madalina; Kaminsky, Edit J.; Trahan, Russell

doi:10.1109/oceans.2005.1639989

Cited by 22 publications

(23 citation statements)

References 2 publications

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“…Two types of sediments are present in the area surveyed: sand and mud. The deconvolution method and the fractional Fourier transform method presented here have been applied to the same beams and pings before 10,11 . We used the same window of 256 samples for both methods.…”

Section: Resultsmentioning

confidence: 99%

“…2 presents the normalized amplitude impulse response of sand corresponding to the nadir beam using the two methods. further performed and the singular values for each beam and each sediment class are computed as shown in our previous work 11 . The representation of the largest S.V.…”

Section: Resultsmentioning

confidence: 99%

“…We have previously tested the techniques employed in this work for impulse response determination both on synthetic data and actual sonar data collected by the VSS 11,12 . The synthetic sonar return signal is generated by the convolution between the transmitted VSS chirp, with the specific parameters characterizing the system in question -such as a bandwidth of 10400 Hz, chirp duration of 4.32 ms 2,11 -and the Green function that has been utilized to simulate the synthetic impulse response of the seafloor.…”

Section: Approachmentioning

confidence: 99%

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Acoustic seabed classification using fractional Fourier transform

2006

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Approachmentioning

confidence: 99%

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Acoustic seabed classification using fractional Fourier transform

2006

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“…1, assuming the sonar transmits chirped signals, for which the Fractional Fourier Transform could be employed to approximate the impulse response, as detailed in [14], [16]. For continuous waves signals, the standard Fourier transform -instead of the Fractional Fourier Transform-is used.…”

Section: Singular Value Decompositionmentioning

confidence: 99%

Classification of Cylindrical Targets Buried in Seafloor Sediments

Kaminsky

Barbu

2007

2007 IEEE Region 5 Technical Conference

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Abstract-This paper presents the development and evaluation of a time-frequency processing technique for detection and classification of buried cylindrical targets from chirpbased parametric sonar data. The software is designed to discriminate between cylindrical targets -such as cablesof different diameters, which need to be identified as different from other strong reflectors or point targets. The method is evaluated on synthetic data generated with an acoustic scattering model for elastic cylinders for seven different diameters. The model generates characteristic responses of targets acquired by a parametric sonar system. The signal at the sonar receiver hydrophones is first windowed to reduce the data to the region of interest (buried target return). This return is then transformed using joint timefrequency transforms (we use the Wigner and Choi-Williams distributions) to produce a 2D image of the return. Dimensionality reduction and feature extraction are performed by singular value decomposition of this time-frequency image. Linear, quadratic, and Mahalanobis discriminant functions are then applied to the most significant singular values to produce the final classification. The study is carried out for various scenarios of free field response of targets as well as for responses from targets buried in sediment.

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“…FrFT is a power signal analysis tool. Consequently, it has been applied to different applications such as high-resolution SAR imaging, sonar signal processing, blind source separation, and beamforming in medical imaging [12][13][14][15]. Short term FrFT, component-optimized FrFT, and locally optimized FrFT have also been proposed for signal decomposition [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Fractional Fourier Transform for Ultrasonic Chirplet Signal Decomposition

Kasaeifard

Oruklu

et al. 2012

Advances in Acoustics and Vibration

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A fractional fourier transform (FrFT) based chirplet signal decomposition (FrFT-CSD) algorithm is proposed to analyze ultrasonic signals for NDE applications. Particularly, this method is utilized to isolate dominant chirplet echoes for successive steps in signal decomposition and parameter estimation. FrFT rotates the signal with an optimal transform order. The search of optimal transform order is conducted by determining the highest kurtosis value of the signal in the transformed domain. A simulation study reveals the relationship among the kurtosis, the transform order of FrFT, and the chirp rate parameter in the simulated ultrasonic echoes. Benchmark and ultrasonic experimental data are used to evaluate the FrFT-CSD algorithm. Signal processing results show that FrFT-CSD not only reconstructs signal successfully, but also characterizes echoes and estimates echo parameters accurately. This study has a broad range of applications of importance in signal detection, estimation, and pattern recognition.

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Fractional Fourier Transform for Sonar Signal Processing

Cited by 22 publications

References 2 publications

Acoustic seabed classification using fractional Fourier transform

Acoustic seabed classification using fractional Fourier transform

Classification of Cylindrical Targets Buried in Seafloor Sediments

Fractional Fourier Transform for Ultrasonic Chirplet Signal Decomposition

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