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

Concept Flow - Spectrum analyzer block

Input Signal

↓

Windowing Function

↓

FFT Computation

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Magnitude Calculation

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Display Spectrum

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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.