Fourier–Bessel series

In mathematics, Fourier–Bessel series is a particular kind of generalized Fourier series (an infinite series expansion on a finite interval) based on Bessel functions.

Fourier–Bessel series are used in the solution to partial differential equations, particularly in cylindrical coordinate systems.

Definition

The Fourier–Bessel series may be thought of as a Fourier expansion in the ρ coordinate of cylindrical coordinates. Just as the Fourier series is defined for a finite interval and has a counterpart, the continuous Fourier transform over an infinite interval, so the Fourier–Bessel series has a counterpart over an infinite interval, namely the Hankel transform.

Because Bessel functions are orthogonal with respect to a weight function $x$ on the interval $[0,b]$, they can be expanded in a Fourier–Bessel series defined by

$f(x)\sim \sum _{n=0}^{\infty }c_{n}J_{\alpha }(\lambda _{n}x/b)$,

where $\lambda _{n}$ is the nth zero of $J_{\alpha }(x)$. This series is associated with the boundary condition $f(b)=0$.

From the orthogonality relationship

$\int _{0}^{1}xJ_{\alpha }(x\lambda _{m})\,J_{\alpha }(x\lambda _{n})\,dx={\frac {\delta _{mn}}{2}}[J_{\alpha +1}(\lambda _{n})]^{2}$,

the coefficients are given by

$c_{n}={\frac {\int _{0}^{b}x\,J_{\alpha }(\lambda _{n}x/b)\,f(x)\,dx}{\int _{0}^{b}xJ_{\alpha }^{2}(\lambda _{n}x/b)dx}}={\frac {\langle f,J_{\alpha }(\lambda _{n}x/b)\rangle }{\|J_{\alpha }(\lambda _{n}x/b)\|^{2}}}.$

The lower integral may be evaluated, yielding

$c_{n}={\frac {\int _{0}^{b}x\,J_{\alpha }(\lambda _{n}x/b)\,f(x)\,dx}{b^{2}J_{\alpha \pm 1}^{2}(\lambda _{n})/2}}$,

where the plus or minus sign is equally valid.

The Fourier-Bessel series expansion employs aperiodic and decaying Bessel functions as the basis. The Fourier-Bessel series expansion has been successfully applied in diversified areas such as Gear fault diagnosis, discrimination of odorants in a turbulent ambient, postural stability analysis, detection of voice onset time, glottal closure instants (epoch) detection, separation of speech formants, EEG signal segmentation, speech enhancement, and speaker identification. The Fourier-Bessel series expansion has also been used to reduce cross terms in the Wigner–Ville distribution.

Dini series

A second Fourier–Bessel series, also known as Dini series, is associated with the Robin boundary condition

$bf'(b)+cf(b)=0$, where $c$ is an arbitrary constant.

The Dini series can be defined by

$f(x)\sim \sum _{n=0}^{\infty }b_{n}J_{\alpha }(\gamma _{n}x/b)$,

where $\gamma _{n}$ is the nth zero of $xJ'_{\alpha }(x)+cJ_{\alpha }(x)$.

The coefficients $b_{n}$ are given by

$b_{n}={\frac {2\gamma _{n}^{2}}{b^{2}(c^{2}+\gamma _{n}^{2}-\alpha ^{2})J_{\alpha }^{2}(\gamma _{n})}}\int _{0}^{b}J_{\alpha }(\gamma _{n}x/b)\,f(x)\,x\,dx$.

References

• Smythe, William R. (1968). Static and Dynamic Electricity (3rd ed.). New York: McGraw-Hill.

• Magnus, Wilhelm; Oberhettinger, Fritz; Soni, Raj Pal (1966). Formulas and Theorems for Special Functions of Mathematical Physics. Berlin: Springer.

• J. Schroeder, Signal processing via Fourier–Bessel series expansion, Digital Signal Process. 3 (1993), 112–124.

• G. D’Elia, S. Delvecchio and G. Dalpiaz, On the use of Fourier-Bessel series expansion for gear diagnostics, Proc. of the Second Int. Conf. Condition Monitoring of Machinery in Non-Stationnary Operations (2012), 267-275.

• A. Vergaraa, E. Martinelli, R. Huerta, A. D’Amico and C. Di Natale, Orthogonal decomposition of chemo-sensory signals: Discriminating odorants in a turbulent ambient, Procedia Engineering 25 (2011), 491–494.

• R.B. Pachori and D. Hewson, Assessment of the effects of sensory perturbations using Fourier–Bessel expansion method for postural stability analysis, J. Intell. Syst. 20 (2011), 167–186.

• R.B. Pachori and S. V. Gangashetty, Detection of voice onset time using FB expansion and AM-FM model, Proc. IEEE 10th Int. Conf. Inf. Sci. Signal Process. Appl. (2010), 149–152.

• P. Jain and R.B. Pachori, Time-order representation based method for epoch detection from speech signals, J. Intell. Syst. 21 (2012), 79-95.

• R.B. Pachori and P. Sircar, Analysis of multicomponent AM-FM signals using FB-DESA method, Digital Signal Process. 20 (2010), 42–62.

• R.B. Pachori and P. Sircar, EEG signal analysis using FB expansion and second order linear TVAR process, Signal Process. 88 (2008), 415–420.

• F.S. Gurgen and C. S. Chen, Speech enhancement by Fourier–Bessel coefficients of speech and noise, IEE Proc. Comm. Speech Vis. 137 (1990), 290–294.

• K. Gopalan, T. R. Anderson and E. J. Cupples, A comparison of speaker identification results using features based on cepstrum and Fourier–Bessel expansion, IEEE Trans. Speech Audio Process. 7 (1999), 289–294.

• R.B. Pachori and P. Sircar, A new technique to reduce cross terms in the Wigner distribution, Digital Signal Process. 17 (2007), 466–474.

External links

• Weisstein, Eric. W. "Fourier-Bessel Series". From MathWorld--A Wolfram Web Resource.
• Fourier–Bessel series applied to Acoustic Field analysis on Trinnov Audio's research page

See also

• Orthogonality
• Generalized Fourier series
• Hankel transform
• Neumann polynomial