RF Spectrum Analysis Dashboard

An interactive RF Spectrum Analysis Laboratory built with Streamlit.
This project simulates and visualizes radio frequency (RF) signals with configurable parameters, Doppler effects, filtering, and peak detection.
It is designed as both an educational tool and a lightweight sandbox for exploring real-world signal processing concepts.

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Features

• Generate synthetic RF signals with adjustable carriers, noise, and Doppler effects.

• Visualize the signal in multiple ways:

  • Time domain (raw waveform)
  • Frequency spectrum (FFT)
  • Spectrogram (time–frequency heatmap)
  • Cumulative spectral power
  • Detected peaks

• Apply filtering (low-pass, high-pass, or none) to clean signals.

• Configure sampling rate, duration, and peak detection thresholds.

• Interactive plots: zoom in/out, pan, and rescale views to examine details.

• Signal Generation

  • Multiple carriers with customizable frequencies & amplitudes
  • Adjustable sampling rate, duration, and noise level

• Doppler Simulation

  • Manual radial velocity (m/s)
  • Orbital speed (km/s) and direction factor (+1 toward, -1 away)

• Visualizations

  • Time domain (waveform view)
  • Frequency spectrum (FFT)
  • Spectrogram (time–frequency heatmap)
  • Cumulative spectral power
  • Peak detection overlays

• Filtering

  • None, low-pass, high-pass, and band-pass options

• Peak Detection

  • Adjustable magnitude threshold
  • Minimum separation between detected peaks

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How Synthetic RF Signals Are Generated

The simulator generates synthetic radio frequency (RF) signals by combining basic waveforms and noise sources:

• Baseband Signal Construction
  A clean sinusoidal carrier is created using NumPy (sin(2πft)), with user-defined frequency, amplitude, and duration.

• Noise Injection
  Gaussian (white) noise is added using numpy.random.normal to approximate real-world thermal noise.

• Doppler Shift Simulation
  Frequency shifts are applied by dynamically modifying the carrier frequency over time.

  • Manual mode: constant shift specified by the user.
  • Satellite/LEO preset: time-varying shift approximating orbital motion.

• Filtering
  Frequency-domain filters (low-pass, high-pass, band-pass) are applied using SciPy’s signal processing functions (butter, lfilter) to mimic receiver chain effects.

• Composite Signals
  Multiple sinusoids and noise sources can be summed to simulate realistic interference, jamming, or multipath.

This provides a sandbox that mimics real-world RF conditions while remaining fully synthetic, reproducible, and lightweight.

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Usage & Explanation

The dashboard allows you to configure RF signals and visualize how they behave. Below are the key components explained:

Visualizations

• Time Domain: The raw waveform of the signal as it changes over time. Shows how noise, carrier signals, and Doppler effects appear directly in the time series.
• Frequency Spectrum: Fast Fourier Transform (FFT) that breaks down the signal into its frequency components. Peaks correspond to carrier frequencies or dominant tones.
• Spectrogram (Time–Frequency): A sliding FFT plotted over time, producing a heatmap. This shows how frequencies evolve or shift (useful for Doppler).
• Cumulative Spectral Power: A cumulative distribution of signal power across frequencies. Helps visualize where most energy is concentrated.
• Detected Peaks: Automatically highlights frequency peaks above a threshold, useful for carrier detection or identifying interfering signals.

Signal Parameters

• Sampling Rate (Hz): Number of samples per second taken from the signal. Higher rates capture higher frequencies but require more processing.
• Duration (s): Length of the signal in seconds. Affects time resolution and FFT detail.
• Noise Std (linear): Standard deviation of Gaussian noise added to the signal. Higher values simulate more interference.
• Carriers: The base sinusoidal signals making up the RF input.
  • Frequencies (Hz): Frequencies of each carrier.
  • Amplitudes: Strength (magnitude) of each carrier.

Doppler

• Manual Radial Velocity (m/s): Simulates Doppler shift caused by motion toward/away from the observer.
• Orbital Speed (km/s): Converts orbital motion into Doppler frequency shifts.
• Radial Factor (toward=+1, away=-1): Direction of motion relative to observer.

Filtering

• Filter Type: Selects which frequencies are passed or removed.
  • None: Raw signal
  • Low-Pass: Keeps only frequencies below cutoff
  • High-Pass: Keeps only frequencies above cutoff
  • Band-Pass: Keeps frequencies within a specified range
• Low Cutoff (Hz): Lower boundary for filtering.
• High Cutoff (Hz): Upper boundary for filtering.

Peak Detection

• Magnitude Threshold: Minimum signal strength required for a frequency to be considered a peak.
• Min Separation (Hz): Minimum spacing between detected peaks to avoid labeling noise as multiple signals.

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Use Cases

• Learning and teaching signal processing fundamentals
• Exploring RF noise, filtering, and Doppler effects
• Demonstrating peak detection and spectral analysis
• Quick sandbox for communications-related experiments

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Requirements

• Python 3.8+
• Streamlit
• NumPy
• SciPy
• Matplotlib

(All included in requirements.txt)

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