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Modified Wigner distribution function

Modified Wigner distribution function is a mathematics topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Modified Wigner distribution function rather than just read about it. In short: Note: the Wigner distribution function is abbreviated here as WD rather than WDF as used at Wigner distribution function A Modified Wigner distribution function is a variation of the Wigner distribution function (WD) with reduced or removed cross-terms. The Wigner distribution (WD) was first proposed for corrections to classical statistical mechanics in 1932 by Eugene Wigner.

Key takeaways

  • Modified Wigner distribution function belongs to mathematics; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Modified Wigner distribution function to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Modified Wigner distribution function from memory before moving on to harder problems.

Reference excerpt

Note: the Wigner distribution function is abbreviated here as WD rather than WDF as used at Wigner distribution function A Modified Wigner distribution function is a variation of the Wigner distribution function (WD) with reduced or removed cross-terms. The Wigner distribution (WD) was first proposed for corrections to classical statistical mechanics in 1932 by Eugene Wigner. The Wigner distribution function, or Wigner–Ville distribution (WVD) for analytic signals, also has applications in time frequency analysis. The Wigner distribution gives better auto term localisation compared to the smeared out spectrogram (SP). However, when applied to a signal with multi frequency components, cross terms appear due to its quadratic nature. Several methods have been proposed to reduce the cross terms. For example, in 1994 Ljubiša Stanković proposed a novel technique, now mostly referred to as S-method, resulting in the reduction or removal of cross terms. The concept of the S-method is a combination between the spectrogram and the Pseudo Wigner Distribution (PWD), the windowed version of the WD. The original WD, the spectrogram, and the modified WDs all belong to the Cohen's class of bilinear time-frequency representations :

C x ( t , f ) = ∫ − ∞ ∞ ∫ − ∞ ∞ W x ( θ , ν ) Π ( t − θ , f − ν ) d θ d ν = [ W x ∗ Π ] ( t , f ) {\displaystyle C_{x}(t,f)=\int _{-\infty }^{\infty }\int _{-\infty }^{\infty }W_{x}(\theta ,\nu )\Pi (t-\theta ,f-\nu )\,d\theta \,d\nu \quad =[W_{x}\,\ast \,\Pi ](t,f)}

where Π ( t , f ) {\displaystyle \Pi \left(t,f\right)} is Cohen's kernel function, which is often a low-pass function, and normally serves to mask out the interference in the original Wigner representation.

Mathematical definition Wigner distribution

W x ( t , f ) = ∫ − ∞ ∞ x ( t + τ / 2 ) x ∗ ( t − τ / 2 ) e − j 2 π τ f d τ {\displaystyle W_{x}(t,f)=\int _{-\infty }^{\infty }x(t+\tau /2)x^{*}(t-\tau /2)e^{-j2\pi \tau f}\,d\tau }

Cohen's kernel function : Π ( t , f ) = δ ( 0 , 0 ) ( t , f ) {\displaystyle \Pi (t,f)=\delta _{(0,0)}(t,f)}

Spectrogram

S P x ( t , f ) = | S T x ( t , f ) | 2 = S T x ( t , f ) S T x ∗ ( t , f ) {\displaystyle SP_{x}(t,f)=|ST_{x}(t,f)|^{2}=ST_{x}(t,f)\,ST_{x}^{*}(t,f)}

where S T x {\displaystyle ST_{x}} is the short-time Fourier transform of x {\displaystyle x} .

S T x ( t , f ) = ∫ − ∞ ∞ x ( τ ) w ∗ ( t − τ ) e − j 2 π f τ d τ {\displaystyle ST_{x}(t,f)=\int _{-\infty }^{\infty }x(\tau )w^{*}(t-\tau )e^{-j2\pi f\tau }\,d\tau }

Cohen's kernel function : Π ( t , f ) = W h ( t , f ) {\displaystyle \Pi (t,f)=W_{h}(t,f)} which is the WD of the window function itself. This can be verified by applying the convolution property of the Wigner distribution function. The spectrogram cannot produce interference since it is a positive-valued quadratic distribution.

Modified form I

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Modified Wigner distribution function

Start with the simplest possible case. Write down what Modified Wigner distribution function claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In mathematics, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Modified Wigner distribution function before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Modified Wigner distribution function ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Modified Wigner distribution function

In research
Modified Wigner distribution function appears in mathematics research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Modified Wigner distribution function in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Modified Wigner distribution function is common in secondary-school and first-year university syllabi. It links to neighbouring topics Signal processing, Transforms, so understanding it makes those chapters shorter.
In everyday life
Look for Modified Wigner distribution function outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Modified Wigner distribution function in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Modified Wigner distribution function means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Modified Wigner distribution function out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Modified Wigner distribution function in simple terms?

Note: the Wigner distribution function is abbreviated here as WD rather than WDF as used at Wigner distribution function A Modified Wigner distribution function is a variation of the Wigner distribution function (WD) with reduced or removed cross-terms. The Wigner distribution (WD) was first propos…

Why does Modified Wigner distribution function matter?

Because it connects several mathematics ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Modified Wigner distribution function?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Modified Wigner distribution function.

Tags

  • Signal processing
  • Transforms

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