Objective This tutorial walks you through a compact, practical spectrum analyzer built from a Raspberry Pi and a low-cost RTL-SDR dongle. The goal is a passive RF monitoring tool that produces a waterfall style spectrum image you can use for situational awareness, antenna checks, and basic spectrum hygiene. I focus on tools that run reliably on Pi class hardware and on parameter choices that balance resolution against CPU, memory, and storage use.

What you will need

  • Raspberry Pi 3 or 4 (Pi 3A+ and Pi 4 are both good choices for low-cost deployments).
  • RTL-SDR compatible USB dongle (RTL2832U based dongles are common and supported by the rtl-sdr project).
  • A simple broadband antenna or discone for initial testing
  • Micro SD card and power supply
  • Optional: small case and USB extension cable to keep the dongle away from Pi RFI

High level approach

  1. Install rtl-sdr tools on the Pi so the dongle is accessible from Linux userspace.
  2. Use rtl_power to sweep frequency ranges and save power measurements to CSV.
  3. Convert the CSV to a waterfall image using a small Python tool such as heatmap.py or a faster renderer like sdr-heatmap.
  4. Iterate parameter choices to get the resolution, time-span, and file size behavior you want.

Step 1. Prepare the Pi and install software

  • Flash Raspberry Pi OS (Lite is fine for headless operation).
  • Update packages: sudo apt update && sudo apt upgrade -y
  • Install rtl-sdr and the utilities that include rtl_test and rtl_power. On Debian-based Pi OS the package is usually called rtl-sdr. Example: sudo apt install rtl-sdr -y. After install, verify the dongle is visible with rtl_test -t. For a graphical receiver you can install gqrx or other SDR front ends but they are optional.

Notes and troubleshooting

  • If a DVB-T driver claims the dongle, blacklist it so rtl-sdr can claim the device. Many online guides show adding a blacklist file in /etc/modprobe.d to prevent the kernel DVB drivers from binding.

Step 2. Use rtl_power to scan and record power vs frequency rtl_power is a command line sweeper that steps across a frequency band and writes averaged power per bin to CSV. It is well suited to long term unattended scans because it writes simple, compact CSV lines. The basic invocation looks like this pattern:

rtl_power -f :: -i -g -e output.csv

What each option controls

  • -f start:stop:step sets the frequency range and the FFT bin size. The smaller the step, the wider the horizontal resolution of the resulting waterfall and the larger the CSV.
  • -i interval is the integration period in seconds for each time row. Shorter values increase time resolution but make larger files.
  • -g gain controls receiver gain. Start with automatic or a mid value and adjust to avoid saturating strong local signals.
  • -e duration sets how long to run. Use smaller test runs when tuning parameters.

Example: scan a VHF window for five minutes at coarse resolution sudo rtl_power -f 144M:148M:10k -i 1 -g 40 -e 5m vhf_scan.csv

Guidance on choosing parameters

  • Horizontal resolution is (stop - start) / step. If you want a detailed waterfall across a large chunk of spectrum reduce the step but be mindful of file size and memory. Users commonly reduce step to 100 kHz or 10 kHz depending on the sweep width. A very wide sweep at fine step produces huge CSVs.
  • Integration interval controls vertical resolution. For monitoring intermittent signals you want a shorter interval. For slow environmental scans longer intervals reduce noise and file churn.

Step 3. Convert the CSV into a waterfall / spectrum image There are several small tools that turn rtl_power CSV output into images. The original heatmap.py is simple and widely used. There are newer tools such as sdr-heatmap which are faster and intended for constrained devices. Choose the one that fits your Pi model and desired performance.

Simple workflow using heatmap.py (Python)

  • Download heatmap.py and make it executable. Usage is typically: python heatmap.py input.csv output.png
  • If your Pi has limited RAM and the CSV is large, pipe gzipped files or trim the CSV before converting. Many users run rtl_power with moderate step sizes for long recordings and then create focused heatmaps of smaller sections.

Faster option: sdr-heatmap

  • sdr-heatmap is a Rust tool built to convert rtl_power files faster and with lower memory use on Pi class hardware. Installable from cargo or prebuilt binaries when available. It produces the same style of images but runs orders of magnitude faster on big CSVs. Consider it if you plan to run long continuous scans.

Example end-to-end script (minimal)

  1. Start a background rtl_power run for a fixed duration and save to CSV.
  2. When rtl_power exits, run heatmap.py or sdr-heatmap on the CSV to produce PNG.

A minimal shell example for testing (adjust paths and options):

#!/bin/bash OUT=/home/pi/scan.csv IMG=/home/pi/scan.png

10 minutes across 24 to 1766 MHz is a popular wide scan example but tailor start/stop to your dongle and needs

rtl_power -f 24M:1700M:200k -i 2 -g 40 -e 10m $OUT python3 /home/pi/heatmap.py $OUT $IMG

Replace heatmap.py with sdr-heatmap for better performance on large CSV files.

Operational tips and tuning

  • Avoid unnecessarily fine steps across enormous ranges. If you want fine resolution in one band, sweep that band with a small step and scan other bands coarsely. This hybrid approach keeps file sizes and CPU manageable.
  • Watch CPU and heat on small Pi models when running heavy conversions. Offload conversion to a more powerful machine if you plan long multi-day sweeps and lots of plotting. Balena documented a Raspberry Pi 3A+ example for rtl_power use cases if you want a containerized approach.
  • For long unattended runs compress data files or rotate them with logrotate style naming to avoid filling the SD card.

Interpretation basics

  • The waterfall image shows time vertically and frequency horizontally. Bright or warm colors indicate higher received power in a bin at that time. Continuous carriers create vertical stripes, intermittent carriers create short horizontal streaks, and broadband energy shows as wide bands. Use this for occupancy checks, antenna troubleshooting, and identifying transient emissions.

Legal and safety note This setup is passive only. Do not use it to decode or disclose private or protected communications. Respect local laws and spectrum privacy rules. The tools in this guide are for monitoring and measurement only.

Where to learn more and reference material

  • The rtl-sdr project and community tutorials provide step by step installation and usage examples for Pi hardware and SDR front ends.
  • The rtl_power command and examples are documented in community pages and show how parameters map to CSV output.
  • Python heatmap scripts and other visualization tools are available on GitHub. For larger datasets consider sdr-heatmap which is optimized for speed on Pi devices.

Closing A Raspberry Pi plus an RTL-SDR dongle is a low-cost way to build a useful spectrum analyzer that can run unattended. Start with conservative parameter choices, test short runs, and then tune step and interval to match the time and frequency detail you need. Keep an eye on CPU, memory, and storage, and pick a visualization tool matched to your Pi model.