Spectrum analyzer block in Simulink - Step-by-Step Execution

Concept Flow - Spectrum analyzer block

Input Signal

↓

Windowing Function

↓

FFT Computation

↓

Magnitude Calculation

↓

Display Spectrum

↓

User Interaction (Zoom, Scale)

The spectrum analyzer block takes an input signal, applies a window, computes the FFT, calculates magnitude, and displays the frequency spectrum with user controls.

Execution Sample

Simulink

input_signal = sin(2*pi*50*t) + 0.5*sin(2*pi*120*t);
windowed_signal = input_signal .* hamming(length(t));
fft_result = fft(windowed_signal);
magnitude = abs(fft_result);
spectrum = magnitude(1:length(t)/2);

This code simulates the steps inside a spectrum analyzer block: windowing, FFT, magnitude calculation, and extracting half spectrum.

Execution Table

Step	Action	Input/Variable	Output/Result
1	Receive input signal	sinusoids at 50Hz and 120Hz	Raw time-domain signal array
2	Apply window	Raw signal, Hamming window	Windowed signal array
3	Compute FFT	Windowed signal	Complex frequency-domain array
4	Calculate magnitude	FFT result	Magnitude spectrum array
5	Extract half spectrum	Magnitude spectrum	Positive frequency spectrum
6	Display spectrum	Positive frequency spectrum	Visual frequency plot
7	User interaction	Zoom/scale commands	Updated display view

💡 Process ends after displaying spectrum and user interaction updates.

Variable Tracker

Variable	Start	After Step 2	After Step 3	After Step 4	After Step 5	Final
input_signal	None	Raw signal array	Raw signal array	Raw signal array	Raw signal array	Raw signal array
windowed_signal	None	Windowed signal array	Windowed signal array	Windowed signal array	Windowed signal array	Windowed signal array
fft_result	None	None	Complex FFT array	Complex FFT array	Complex FFT array	Complex FFT array
magnitude	None	None	None	Magnitude array	Magnitude array	Magnitude array
spectrum	None	None	None	None	Half magnitude array	Half magnitude array

Key Moments - 3 Insights

Why do we apply a window function before FFT?

Applying a window reduces spectral leakage by tapering the signal edges, as shown in Step 2 of the execution_table.

Why do we only look at half of the FFT result for the spectrum?

Because the FFT of a real signal is symmetric, the positive frequencies are in the first half, as shown in Step 5.

What does the magnitude calculation represent in the spectrum?

It converts complex FFT values to real amplitudes showing signal strength at each frequency, as in Step 4.

Visual Quiz - 3 Questions

Look at the execution_table, what is the output after Step 3 (Compute FFT)?

A Complex frequency-domain array

B Windowed time-domain signal

C Magnitude spectrum array

D Raw input signal

💡 Hint

Refer to Step 3 output column in execution_table.

At which step do we reduce spectral leakage by modifying the signal?

A Step 1

B Step 4

C Step 2

D Step 5

💡 Hint

Check the action column for windowing in execution_table.

If the input signal was complex instead of real, how would the spectrum extraction step change?

A We would still extract only half the spectrum

B We would extract the full FFT spectrum

C We would skip FFT computation

D We would not apply a window

💡 Hint

Look at Step 5 and consider symmetry of FFT for real vs complex signals.

Concept Snapshot

Spectrum analyzer block:
- Input signal is windowed to reduce leakage
- FFT converts time signal to frequency domain
- Magnitude of FFT shows signal strength
- Only positive frequencies shown for real signals
- User can zoom and scale the spectrum display

Full Transcript

The spectrum analyzer block processes an input signal by first applying a window function to reduce spectral leakage. Then it computes the FFT to transform the signal into the frequency domain. The magnitude of the FFT is calculated to show the strength of each frequency component. For real-valued signals, only the positive half of the spectrum is displayed. Finally, the spectrum is shown visually, allowing user interaction such as zooming and scaling. This step-by-step process helps analyze the frequency content of signals clearly.

Practice

1. What is the main purpose of the Spectrum analyzer block in Simulink?

easy

A. To perform mathematical operations on signals

B. To generate random signals for testing

C. To visually display the frequency content of signals

D. To convert signals from analog to digital

Solution

1. Step 1: Understand the function of the Spectrum analyzer block

   The Spectrum analyzer block is designed to show the frequency content of signals visually.

2. Step 2: Compare options with the block's purpose

   Only To visually display the frequency content of signals correctly describes this visual frequency display function.

3. Final Answer:

   To visually display the frequency content of signals -> Option C

4. Quick Check:

   Spectrum analyzer shows frequency content = To visually display frequency content [OK]

Hint: Remember: Spectrum analyzer = frequency visualization [OK]

Common Mistakes:

• Confusing it with signal generation
• Thinking it performs signal conversion
• Assuming it does mathematical signal processing

2. Which of the following is the correct way to add a Spectrum analyzer block in Simulink?

easy

A. Use the plot() function in Simulink

B. Write spectrumAnalyzer() in the MATLAB command window

C. Add a Scope block and rename it Spectrum analyzer

D. Drag the block from the DSP System Toolbox library

Solution

1. Step 1: Identify how to add Spectrum analyzer in Simulink

   The Spectrum analyzer block is available in the DSP System Toolbox library and can be dragged into the model.

2. Step 2: Evaluate other options

   Write spectrumAnalyzer() in the MATLAB command window is a MATLAB command, not a Simulink block addition. Use the plot() function in Simulink is a MATLAB plotting function, not a block. Add a Scope block and rename it Spectrum analyzer is incorrect because renaming a Scope block does not change its function.

3. Final Answer:

   Drag the block from the DSP System Toolbox library -> Option D

4. Quick Check:

   Adding Spectrum analyzer = Drag from DSP System Toolbox [OK]

Hint: Add blocks by dragging from the correct library [OK]

Common Mistakes:

• Trying to add it via MATLAB commands
• Confusing Scope with Spectrum analyzer
• Using plot() instead of block

3. Consider this Simulink model snippet where a sine wave is connected to a Spectrum analyzer block. What will the Spectrum analyzer display?

medium

A. A single peak at the sine wave frequency

B. Multiple peaks at random frequencies

C. A flat line with no peaks

D. A peak at zero frequency only

Solution

1. Step 1: Understand the input signal

   A sine wave has a single frequency component, so its frequency spectrum shows one peak at that frequency.

2. Step 2: Analyze Spectrum analyzer output

   The Spectrum analyzer displays frequency content, so it will show a single peak corresponding to the sine wave frequency.

3. Final Answer:

   A single peak at the sine wave frequency -> Option A

4. Quick Check:

   Sine wave frequency spectrum = single peak [OK]

Hint: Sine wave = one frequency peak in spectrum [OK]

Common Mistakes:

• Expecting multiple peaks for a pure sine wave
• Thinking the spectrum is flat
• Confusing zero frequency with sine frequency

4. You connected a noisy signal to a Spectrum analyzer block but see no frequency peaks. What is the most likely error?

medium

A. The Spectrum analyzer block is not connected to the signal

B. The signal is too strong and saturates the analyzer

C. The Spectrum analyzer block is set to time domain display

D. The signal is a pure DC with no frequency content

Solution

1. Step 1: Check connection of Spectrum analyzer block

   If the block is not connected to the signal, it cannot display frequency content, resulting in no peaks.

2. Step 2: Evaluate other options

   The signal is too strong and saturates the analyzer is unlikely because saturation usually shows distorted peaks. The Spectrum analyzer block is set to time domain display is invalid as Spectrum analyzer shows frequency, not time domain. The signal is a pure DC with no frequency content contradicts the noisy signal premise.

3. Final Answer:

   The Spectrum analyzer block is not connected to the signal -> Option A

4. Quick Check:

   No peaks = likely no connection [OK]

Hint: Check block connections first if output is empty [OK]

Common Mistakes:

• Assuming wrong display mode
• Ignoring block connection
• Misinterpreting signal type

5. You want to analyze a signal with both low and high frequency components using the Spectrum analyzer block. Which parameter adjustment will help you see both clearly?

hard

A. Disable windowing to get raw data

B. Increase FFT length and adjust frequency span

C. Set the Spectrum analyzer to time domain mode

D. Decrease FFT length and use default frequency span

Solution

1. Step 1: Understand FFT length effect

   Increasing FFT length improves frequency resolution, helping to distinguish low and high frequency components clearly.

2. Step 2: Adjust frequency span

   Setting frequency span to cover the full range of interest ensures both low and high frequencies are visible.

3. Final Answer:

   Increase FFT length and adjust frequency span -> Option B

4. Quick Check:

   Better resolution + correct span = clear frequency display [OK]

Hint: Longer FFT + correct span = clearer frequency details [OK]

Common Mistakes:

• Reducing FFT length reduces resolution
• Using time domain mode hides frequency info
• Disabling windowing causes spectral leakage