Generalized ambiguity functions

Cohen, Leon; Posch, T.E.

doi:10.1109/icassp.1985.1168133

Cited by 32 publications

(9 citation statements)

References 18 publications

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“…The generalised ambiguity function, or ( , )A X ( , ) [11] is a key concept in t}f which aids one in clearly seeing the e!ect of the kernel in determining C X (t, ; ).…”

Section: Ambiguity Functions Relationshipsmentioning

confidence: 99%

Helicopter Transmission Fault Detection via Time-Frequency, Scale and Spectral Methods

Williams¹,

Zalubas²

2000

Mechanical Systems and Signal Processing

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“…The generalised ambiguity function, or ( , )A X ( , ) [11] is a key concept in t}f which aids one in clearly seeing the e!ect of the kernel in determining C X (t, ; ).…”

Section: Ambiguity Functions Relationshipsmentioning

confidence: 99%

Helicopter Transmission Fault Detection via Time-Frequency, Scale and Spectral Methods

Williams¹,

Zalubas²

2000

Mechanical Systems and Signal Processing

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“…The choice of a time kernel appropriate to a specific application can be better explained through the generalized ambiguity function (GAF). Equation (1) can be rewritten in terms of the GAF [8], i.e., GAF(ξ, t : ϕ), as follows:…”

Section: Quadratic Tfrs For Nonstationary Bldc Motor Fault Diagnomentioning

confidence: 99%

Non-Stationary Motor Fault Detection Using Recent Quadratic Time-Frequency Representations

Rajagopalan

Habetler

Restrepo

et al. 2006

Conference Record of the 2006 IEEE Industry Applications Conference Forty-First IAS Annual Meeting

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Abstract-As the use of electric motors increases in the aerospace and transportation industries where operating conditions continuously change with time, fault detection in electric motors has been gaining importance. Motor diagnostics in a nonstationary environment is difficult and often needs sophisticated signal processing techniques. In recent times, a plethora of new time-frequency distributions has appeared, which are inherently suited to the analysis of nonstationary signals while offering superior frequency resolution characteristics. The Zhao-Atlas-Marks distribution is one such distribution. This paper proposes the use of these new time-frequency distributions to enhance nonstationary fault diagnostics in electric motors. One common myth has been that the quadratic time-frequency distributions are not suitable for commercial implementation. This paper also addresses this issue in detail. Optimal discrete-time implementations of some of these quadratic time-frequency distributions are explained. These time-frequency representations have been implemented on a digital signal processing platform to demonstrate that the proposed methods can be implemented commercially.

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“…DEFINITION 2.2. The (generalized) ambiguity function (AF) [2][3][4]11] of a deterministic signal x(t) is given by…”

Section: Theoretical Spreadmentioning

confidence: 99%

Numerical Spread: Quantifying Local Stationarity

Hedges

Suter

2002

Digital Signal Processing

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Generalized ambiguity functions

Cited by 32 publications

References 18 publications

Helicopter Transmission Fault Detection via Time-Frequency, Scale and Spectral Methods

Helicopter Transmission Fault Detection via Time-Frequency, Scale and Spectral Methods

Non-Stationary Motor Fault Detection Using Recent Quadratic Time-Frequency Representations

Numerical Spread: Quantifying Local Stationarity

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