Thanks to VE3NEA for letting us know about his new RTL-SDR compatible heatmap generator plugin for SDR#. To use the plugin you first need to generate some heatmap CSV data by using the rtl_power software. You can then open the CSV file in the plugin and it will generate a heatmap image. A frequency heatmap shows a wideband waterfall image of detected frequency activity.
RTL-SDR heatmap tools are nothing new, but the convenience of having it as a SDR# plugin is that you can click on the heatmap image to instantly tune to a frequency where activity was recorded during the initial rtl_power scan.
Over on YouTube The Thought Emporium channel has been working on creating a "WiFi Camera" over the past few weeks. The idea is to essentially create a small radio telescope that can "see" WiFi signals, by generating a heatmap of WiFi signal strength. This is done with a directional helical 2.4 GHz antenna and motorized rotator that incrementally steps the antenna through various angles. After each movement step a HackRF and Python script is used to measure WiFi signal strength for a brief moment, and then the rotator moves onto the next angle. The helical antenna and rotator that they created are made out of PVC pipe plastic and wood, and are designed to be built by anyone with basic workshop tools like a bandsaw.
The final results show that they've been able to successfully generate heatmaps that can be overlaid on top of a photo. The areas that show higher signal strength correlate with areas on the photo where WiFi routers are placed, so the results appear to be accurate. In the future they hope to expand this idea and create a skyward pointing radio telescope for generating images of the galactic hydrogen line, and of satellites.
The videos are split into three parts. The first two videos show the build process of the antennas and rotator, whilst the third video shows the final results.
DIY Radio Telescope Version 2: Wifi vision - Part 1
DIY Radio Telescope V2: Wifi Vision - Part 2
Building a Camera That Can See Wifi | Radio Telescope V2 - Part 3 SUCCESS!
It’s been known for a while now that it is possible to break into cars using simple wireless attacks that involve jamming of the car keyfob frequency. Sammy Kamkars “rolljam” is one such example that can be built with a cheap Arduino and RF transceiver chip. One way to secure yourself against wireless attacks like this is to run a jammer detector.
A jammer detector is quite simple in theory – just continuously measure the signal strength at the car keyfob frequency and notify the user if a strong continuous signal is detected. Over on his blog author mikeh69 has posted about his work in creating a wireless jammer detector out of a Raspberry Pi and RTL-SDR dongle. He uses a Python script and some C code that he developed to create a tool that displays the signal strength on an onscreen bar graph and also conveys signal strength information via audio tones. He writes that with a pair of earphones and battery pack you can use the system while walking around searching for the source of a jammer.
Mikeh69’s post goes into further detail about installing the software and required dependencies. He also writes that in the future he wants to experiment with creating large area surveys by logging signal strength data against GPS locations to generate a heatmap. If you are interested in that idea, then it is similar to Tim Haven’s driveby noise detector system which also used RTL-SDR dongles, or the heatmap feature in RTLSDR Scanner.
Dump1090 is one of the most popular ADS-B decoders that is used together with the RTL-SDR dongle. ADS-B stands for Automatic Dependant Surveillance Broadcast and is a system used by aircraft that broadcasts their GPS positions. It is a replacement for traditional reflection based radar systems. We have a tutorial on using the RTL-SDR to decode ADS-B here.
There is now a forked version of dump1090 by tedsluis that incorporates heatmap generation and range/altitude view. A heatmap will allow you to visualize where the most active aircraft paths in your area are and the range/altitude view allows you to see at what altitudes aircraft typically fly at in different locations. The software logs aircraft data in a CSV file, and then after collecting enough data a second program can be used to generate the heatmap. The full explanation of the software and instructions for installing and using it on a Raspberry Pi Linux system together with PiAware are posted on the flightaware.com forums.
dump1090-mutability with Heatmap ADS-B and range altitude view
Rtl_power is a tool that allows you to create wide band signal strength heat maps over a long length of time. It works by very quickly hopping across the spectrum, capturing the RTL-SDR bandwidth of about 2 MHz at a time, and then displaying it on a heat map. This is useful for seeing what frequencies are active and at what times.
Over on GitHub a new heatmap plotter for rtl_power has been released. The software is called rtl_heatmap and is software that can be used to create a heatmap from the csv data produced by rtl_power. The software creates the heatmap and also adds frequency marker information to the plot. Rtl_heatmap is written in JS and HTML5 and is a web browser based app.
Rtl_power is a tool that can scan a large chunk of bandwidth with an RTL-SDR dongle and record signal power levels over time.
Over on Reddit, user tautology2 has linked to his project which is software that can create a heatmap of signal strengths. His software uses the data that is output from RTLSDR Scanner which is a program that will collect signal strength data over any desired bandwidth and at the same time also record GPS coordinates using an external GPS receiver. RTLSDR Scanner can also create a heatmap by itself, but tautology2’s heatmap is much clearer and has good web controls for choosing the heatmap signal frequency.
Back in June we posted about DE8MSH’s rtl_power based heatmap viewer which was automatically generated every day from a Raspberry Pi. The browser based heatmap display provides a way to view the frequency and time of where the mouse pointer is allowing you to easily identify signals.