ArticleslgStudy

science

Modified Allan variance

Modified Allan variance is a science 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 Allan variance rather than just read about it. In short: The modified Allan variance (MVAR), also known as mod σy2(τ), is a variable bandwidth modified variant of Allan variance, a measurement of frequency stability in clocks, oscillators and amplifiers. Its main advantage relative to Allan variance is its ability to separate white phase noise from flicker phase noise.

Modified Allan variance — main illustration
Modified Allan variance — illustration

Key takeaways

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

Reference excerpt

The modified Allan variance (MVAR), also known as mod σy2(τ), is a variable bandwidth modified variant of Allan variance, a measurement of frequency stability in clocks, oscillators and amplifiers. Its main advantage relative to Allan variance is its ability to separate white phase noise from flicker phase noise. The modified Allan deviation (MDEV), also known as mod σy(τ), is the deviation variant of the modified Allan variance.

Background The Allan variance has a drawback in that it is unable to separate the white phase modulation (WPM) from the flicker phase modulation (FPM). Looking at their response to Power-law noise it is clearly seen that WPM and FPM have almost the same response to tau, but WPM is linearly sensitive to the system bandwidth fH whereas FPM is only weakly dependent on it. Thus, by varying the system bandwidth the WPM and FPM noise forms may be separated. However, it is impractical to alter the hardware of the measurement system. By post-processing the sample-series and implementing a software bandwidth a modified Allan variance measure can be given capable of resolving the noise forms.

Definition The modified Allan variance is defined for using time error samples as

mod ⁡ σ y 2 ( n τ 0 ) = 1 2 τ 2 ⟨ [ 1 n ∑ i = 0 n − 1 x i + 2 n − 2 x i + n + x i ] 2 ⟩ {\displaystyle \operatorname {mod} \sigma _{y}^{2}(n\tau _{0})={\frac {1}{2\tau ^{2}}}\left\langle \left[{\frac {1}{n}}\sum _{i=0}^{n-1}x_{i+2n}-2x_{i+n}+x_{i}\right]^{2}\right\rangle }

or with average fractional frequency time series and τ = nτ0

mod ⁡ σ y 2 ( n τ 0 ) = 1 2 ⟨ [ 1 n ∑ i = 0 n − 1 y ¯ i + n − y ¯ i ] 2 ⟩ , {\displaystyle \operatorname {mod} \sigma _{y}^{2}(n\tau _{0})={\frac {1}{2}}\left\langle \left[{\frac {1}{n}}\sum _{i=0}^{n-1}{\bar {y}}_{i+n}-{\bar {y}}_{i}\right]^{2}\right\rangle ,}

where n is the integer number of samples averaged over.

Estimators The modified Allan variance estimator for time error time series is

… excerpt ends here. Continue reading the full article.

Illustrations

Modified Allan variance: Diagram of modified Allan deviation as a function of averaging time, showing the 6 typical regimes.[1] 
0. White phase-modulation noise (PM): At the highest frequency, white phase noise dominates. This corresponds to 
  
    
      
        σ
        (
        τ
        )
        ∝
        
          τ
          
            −
            3
            
              /
            
            2
          
        
        ,
        S
        [
        f
        ]
        =
        
          f
          
            3
          
        
      
    
    {\displaystyle \sigma (\tau )\propto \tau ^{-3/2},S[f]=f^{3}}
  
.
1. Flicker phase-modulation noise (PM): at a lower frequency, flicker phase noise dominates. This corresponds to 
  
    
      
        σ
        (
        τ
        )
        ∝
        
          τ
          
            −
            1
          
        
        ,
        S
        [
        f
        ]
        =
        
          f
          
            2
          
        
      
    
    {\displaystyle \sigma (\tau )\propto \tau ^{-1},S[f]=f^{2}}
  
.
2. White frequency-modulation noise (FM): at a lower frequency, white noise in frequency dominates. This corresponds to 
  
    
      
        σ
        (
        τ
        )
        ∝
        
          τ
          
            −
            1
            
              /
            
            2
          
        
        ,
        S
        [
        f
        ]
        =
        
          f
          
            0
          
        
      
    
    {\displaystyle \sigma (\tau )\propto \tau ^{-1/2},S[f]=f^{0}}
  
 
3. Flicker FM: 
  
    
      
        σ
        (
        τ
        )
        ∝
        
          τ
          
            0
          
        
        ,
        S
        [
        f
        ]
        ∝
        
          f
          
            −
            1
          
        
      
    
    {\displaystyle \sigma (\tau )\propto \tau ^{0},S[f]\propto f^{-1}}
  
. This is also called "pink noise".
4. Random Walk FM: 
  
    
      
        σ
        (
        τ
        )
        ∝
        
          τ
          
            +
            1
            
              /
            
            2
          
        
        ,
        S
        [
        f
        ]
        ∝
        
          f
          
            −
            2
          
        
      
    
    {\displaystyle \sigma (\tau )\propto \tau ^{+1/2},S[f]\propto f^{-2}}
  
. This is also called "brown noise" or "brownian noise". In this regime, the frequency of the system executes a random walk. In other words, 
  
    
      
        d
        f
        
          /
        
        d
        t
      
    
    {\displaystyle df/dt}
  
 becomes a white noise.
5. Frequency drift: 
  
    
      
        σ
        (
        τ
        )
        ∝
        
          τ
          
            +
            1
          
        
        ,
        S
        [
        f
        ]
        ∝
        
          f
          
            −
            3
          
        
      
    
    {\displaystyle \sigma (\tau )\propto \tau ^{+1},S[f]\propto f^{-3}}
  
. In this regime, the frequency of the system executes a pink noise walk. In other words, 
  
    
      
        d
        f
        
          /
        
        d
        t
      
    
    {\displaystyle df/dt}
  
 becomes a pink noise.
Diagram of modified Allan deviation as a function of averaging time, showing the 6 typical regimes.[1] 0. White phase-modulation noise (PM): At the highest frequency, white phase noise dominates. This corresponds to σ ( τ ) ∝ τ − 3 / 2 , S [ f ] = f 3 {\displaystyle \sigma (\tau )\propto \tau ^{-3/2},S[f]=f^{3}} . 1. Flicker phase-modulation noise (PM): at a lower frequency, flicker phase noise dominates. This corresponds to σ ( τ ) ∝ τ − 1 , S [ f ] = f 2 {\displaystyle \sigma (\tau )\propto \tau ^{-1},S[f]=f^{2}} . 2. White frequency-modulation noise (FM): at a lower frequency, white noise in frequency dominates. This corresponds to σ ( τ ) ∝ τ − 1 / 2 , S [ f ] = f 0 {\displaystyle \sigma (\tau )\propto \tau ^{-1/2},S[f]=f^{0}} 3. Flicker FM: σ ( τ ) ∝ τ 0 , S [ f ] ∝ f − 1 {\displaystyle \sigma (\tau )\propto \tau ^{0},S[f]\propto f^{-1}} . This is also called "pink noise". 4. Random Walk FM: σ ( τ ) ∝ τ + 1 / 2 , S [ f ] ∝ f − 2 {\displaystyle \sigma (\tau )\propto \tau ^{+1/2},S[f]\propto f^{-2}} . This is also called "brown noise" or "brownian noise". In this regime, the frequency of the system executes a random walk. In other words, d f / d t {\displaystyle df/dt} becomes a white noise. 5. Frequency drift: σ ( τ ) ∝ τ + 1 , S [ f ] ∝ f − 3 {\displaystyle \sigma (\tau )\propto \tau ^{+1},S[f]\propto f^{-3}} . In this regime, the frequency of the system executes a pink noise walk. In other words, d f / d t {\displaystyle df/dt} becomes a pink noise.

Worked examples

Example 1 — a first encounter with Modified Allan variance

Start with the simplest possible case. Write down what Modified Allan variance claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, 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 Allan variance 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 Allan variance 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 Allan variance

In research
Modified Allan variance appears in science 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 Allan variance 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 Allan variance is common in secondary-school and first-year university syllabi. It links to neighbouring topics Clocks, Signal processing metrics, so understanding it makes those chapters shorter.
In everyday life
Look for Modified Allan variance 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.
Ask Teacher Smith questions about this articleOpens your AI tutor with a question about “Modified Allan variance” →

Affiliate

Preply — study more efficiently by working with a personal tutor. 50% off.

How to study Modified Allan variance in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Modified Allan variance 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 Allan variance out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Modified Allan variance in simple terms?

The modified Allan variance (MVAR), also known as mod σy2(τ), is a variable bandwidth modified variant of Allan variance, a measurement of frequency stability in clocks, oscillators and amplifiers. Its main advantage relative to Allan variance is its ability to separate white phase noise from flick…

Why does Modified Allan variance matter?

Because it connects several science 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 Allan variance?

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 Allan variance.

Tags

  • Clocks
  • Signal processing metrics

Keep exploring