Polynomial Wigner–Ville distribution

 In signal processing, the polynomial Wigner–Ville distribution is a quasiprobability distribution that generalizes the Wigner distribution function. It was proposed by Boualem Boashash and Peter O'Shea in 1994.

IntroductionEdit

Many signals in nature and in engineering applications can be modeled as ${\displaystyle z(t)=e^{j2\pi \phi (t)}}$, where ${\displaystyle \phi (t)}$ is a polynomial phase and ${\displaystyle j={\sqrt {-1}}}$.

For example, it is important to detect signals of an arbitrary high-order polynomial phase. However, the conventional Wigner–Ville distribution have the limitation being based on the second-order statistics. Hence, the polynomial Wigner–Ville distribution was proposed as a generalized form of the conventional Wigner–Ville distribution, which is able to deal with signals with nonlinear phase.

DefinitionEdit

The polynomial Wigner–Ville distribution ${\displaystyle W_{z}^{g}(t,f)}$ is defined as

${\displaystyle W_{z}^{g}(t,f)={\mathcal {F}}_{\tau \to f}\left[K_{z}^{g}(t,\tau )\right]}$

where ${\displaystyle {\mathcal {F}}_{\tau \to f}}$ denotes the Fourier transform with respect to ${\displaystyle \tau }$, and ${\displaystyle K_{z}^{g}(t,\tau )}$ is the polynomial kernel given by

${\displaystyle K_{z}^{g}(t,\tau )=\prod _{k=-{\frac {q}{2}}}^{\frac {q}{2}}\left[z\left(t+c_{k}\tau \right)\right]^{b_{k}}}$

where ${\displaystyle z(t)}$ is the input signal and ${\displaystyle q}$ is an even number. The above expression for the kernel may be rewritten in symmetric form as

${\displaystyle K_{z}^{g}(t,\tau )=\prod _{k=0}^{\frac {q}{2}}\left[z\left(t+c_{k}\tau \right)\right]^{b_{k}}\left[z^{*}\left(t+c_{-k}\tau \right)\right]^{-b_{-k}}}$

The discrete-time version of the polynomial Wigner–Ville distribution is given by the discrete Fourier transform of

${\displaystyle K_{z}^{g}(n,m)=\prod _{k=0}^{\frac {q}{2}}\left[z\left(n+c_{k}m\right)\right]^{b_{k}}\left[z^{*}\left(n+c_{-k}m\right)\right]^{-b_{-k}}}$

where ${\displaystyle n=t{f}_{s},m={\tau }{f}_{s},}$ and ${\displaystyle f_{s}}$ is the sampling frequency. The conventional Wigner–Ville distribution is a special case of the polynomial Wigner–Ville distribution with ${\displaystyle q=2,b_{-1}=-1,b_{1}=1,b_{0}=0,c_{-1}=-{\frac {1}{2}},c_{0}=0,c_{1}={\frac {1}{2}}}$

ExampleEdit

One of the simplest generalizations of the usual Wigner–Ville distribution kernel can be achieved by taking ${\displaystyle q=4}$. The set of coefficients ${\displaystyle b_{k}}$ and ${\displaystyle c_{k}}$ must be found to completely specify the new kernel. For example, we set

${\displaystyle b_{1}=-b_{-1}=2,b_{2}=b_{-2}=1,b_{0}=0}$

${\displaystyle c_{1}=-c_{-1}=0.675,c_{2}=-c_{-2}=-0.85}$

The resulting discrete-time kernel is then given by

${\displaystyle K_{z}^{g}(n,m)=\left[z\left(n+0.675m\right)z^{*}\left(n-0.675m\right)\right]^{2}z^{*}\left(n+0.85m\right)z\left(n-0.85m\right)}$

Design of a Practical Polynomial KernelEdit

Given a signal ${\displaystyle z(t)=e^{j2\pi \phi (t)}}$, where ${\displaystyle \phi (t)=\sum _{i=0}^{p}a_{i}t^{i}}$is a polynomial function, its instantaneous frequency (IF) is ${\displaystyle \phi '(t)=\sum _{i=1}^{p}ia_{i}t^{i-1}}$.

For a practical polynomial kernel ${\displaystyle K_{z}^{g}(t,\tau )}$, the set of coefficients ${\displaystyle q,b_{k}}$and ${\displaystyle c_{k}}$should be chosen properly such that

${\displaystyle {\begin{aligned}K_{z}^{g}(t,\tau )&=\prod _{k=0}^{\frac {q}{2}}\left[z\left(t+c_{k}\tau \right)\right]^{b_{k}}\left[z^{*}\left(t+c_{-k}\tau \right)\right]^{-b_{-k}}\\&=\exp(j2\pi \sum _{i=1}^{p}ia_{i}t^{i-1}\tau )\end{aligned}}}$

${\displaystyle {\begin{aligned}W_{z}^{g}(t,f)&=\int _{-\infty }^{\infty }\exp(-j2\pi (f-\sum _{i=1}^{p}ia_{i}t^{i-1})\tau )d\tau \\&\cong \delta (f-\sum _{i=1}^{p}ia_{i}t^{i-1})\end{aligned}}}$

• When ${\displaystyle q=2,b_{-1}=-1,b_{0}=0,b_{1}=1,p=2}$,

${\displaystyle z\left(t+c_{1}\tau \right)z^{*}\left(t+c_{-1}\tau \right)=\exp(j2\pi \sum _{i=1}^{2}ia_{i}t^{i-1}\tau )}$

${\displaystyle a_{2}(t+c_{1})^{2}+a_{1}(t+c_{1})-a_{2}(t+c_{-1})^{2}-a_{1}(t+c_{-1})=2a_{2}t\tau +a_{1}\tau }$

${\displaystyle \Rightarrow c_{1}-c_{-1}=1,c_{1}+c_{-1}=0}$

${\displaystyle \Rightarrow c_{1}={\frac {1}{2}},c_{-1}=-{\frac {1}{2}}}$

• When ${\displaystyle q=4,b_{-2}=b_{-1}=-1,b_{0}=0,b_{2}=b_{1}=1,p=3}$

${\displaystyle {\begin{aligned}&a_{3}(t+c_{1})^{3}+a_{2}(t+c_{1})^{2}+a_{1}(t+c_{1})\\&a_{3}(t+c_{2})^{3}+a_{2}(t+c_{2})^{2}+a_{1}(t+c_{2})\\&-a_{3}(t+c_{-1})^{3}-a_{2}(t+c_{-1})^{2}-a_{1}(t+c_{-1})\\&-a_{3}(t+c_{-2})^{3}-a_{2}(t+c_{-2})^{2}-a_{1}(t+c_{-2})\\&=3a_{3}t^{2}\tau +2a_{2}t\tau +a_{1}\tau \end{aligned}}}$

${\displaystyle \Rightarrow {\begin{cases}c_{1}+c_{2}-c_{-1}-c_{-2}=1\\c_{1}^{2}+c_{2}^{2}-c_{-1}^{2}-c_{-2}^{2}=0\\c_{1}^{3}+c_{2}^{3}-c_{-1}^{3}-c_{-2}^{3}=0\end{cases}}}$

ApplicationsEdit

Nonlinear FM signals are common both in nature and in engineering applications. For example, the sonar system of some bats use hyperbolic FM and quadratic FM signals for echo location. In radar, certain pulse-compression schemes employ linear FM and quadratic signals. The Wigner–Ville distribution has optimal concentration in the time-frequency plane for linear frequency modulated signals. However, for nonlinear frequency modulated signals, optimal concentration is not obtained, and smeared spectral representations result. The polynomial Wigner–Ville distribution can be designed to cope with such problem.

This article uses material from the Wikipedia article  Metasyntactic variable, which is released under the  Creative Commons Attribution-ShareAlike 3.0 Unported License.