Tagged: TDOA

June 21, 2018

Building an RF Direction Finding Robot with an RTL-SDR

Over on Hackaday.io, project logger Humpelstilzchen has been writing about his attempts to create an autonomous RF direction finding robot RC car with an RTL-SDR. The goal is to set up an ISM band transmitter as a beacon, and use the RTL-SDR on the robot as the receiver. It will then use direction finding techniques to drive towards the beacon. The robot is a 4WD RC toy car with some autonomous navigational features like GPS, ultrasonic, IMU and vision sensors.

In his latest project log Humpelstilzchen describes his first semi-successful attempt at getting RF direction finding working. In the experiment he uses a 433 MHz module to send out an FSK beacon. On the robot two antennas are used for the time difference of arrival/pseudo-doppler direction finding technique, and PIN diodes are used to rapidly switch between the antennas. A GNU Radio script running on a HummingBoard single board computer computes the TDOA/pseudo-doppler algorithm.

Psuedo-doppler direction finding works by rapidly switching between several antennas. The difference in the time that the signal arrives at each antenna can be used to calculate the transmitter's direction.

With the current set up he's been able to get the robot to distinguish if the beacon is closer to the left, or closer to the right, or equidistant. However, he notes that there are still problems with reflections of the beacon signal which can cause the robot to drive in the wrong direction.

This is still a work in progress and we look forward to his future results.

Humpelstilzchen's RF direction finding robot

April 6, 2018

Information on Time Correlating Signals with RTL-SDRs

In a previous post back in September 2017 Stefan Scholl (DC9ST) treated us to a very interesting write up about how to localize transmitters to within a few meters using time difference of arrival (TDOA) techniques with multiple RTL-SDR dongles spread out over an area.

Stefan has recently added to his post now with some additional information on how to properly correlate signals received between multiple RTL-SDR dongles, which is one of the key parts to TDOA. He writes that he covers the following questions:

- What signal parameters influence the quality of the correlation?
- Which type of correlation calculations are available (four)
- Which are suitable with RTL-SDRs, considering noise and phase and frequency offset?

Stefan writes that his findings could be interesting to people interested in the following techniques:

- TDOA localization
- Synchronizing several RTL-SDRs
- Passive Radar

Comparing various bandwidth sizes on correlation quality

September 28, 2017

Localizing Transmitters to within a few meters with TDOA and RTL-SDR Dongles

Back in August we posted a number of videos from the Software Defined Radio Academy talks held this year in Friedrichshafen, Germany. One of those talks was by Stefan Scholl, DC9ST and titled Introduction and Experiments on Transmitter Localization with TDOA. This was a very interesting talk that showed how Stefan has been using three RTL-SDR + Raspberry Pi setups to locate the almost exact position of various transmitters with time difference of arrival (TDOA) techniques. TDOA works by setting up at least three receivers spread apart by some distance. Due to the speed of radio propagation, the transmitted signal will arrive at each receiver at a different time allowing the physical origin point of the signal to be calculated.

Now over on his blog Stefan has created a very nice writeup of his work with RTL-SDRs and TDOA that is definitely worth a good read. He first explains the basics of how TDOA actually works, and then goes on to explain how his RTL-SDR based system works. He discusses the important challenges such as transferring the raw data, synchronizing the receivers in time and the signal processing required.

He tested the system on various transmitters including a DMR signal at 439 MHz, a mobile phone signal at 922 MHz, an FM signal at 96.9 MHz and an unknown signal at 391 MHz. The results were all extremely accurate, locating transmitters with an accuracy of up to a few meters.

Stefan has also uploaded all his MATLAB code onto GitHub.

Example localization of a DMR transmitter

Localizing the position of a mobile phone base station (Stars indicate known base stations)

August 31, 2017

Tracking Wildlife with TDOA Direction Finding and RTL-SDR Dongles

At the North-West University in South Africa Masters student SW Krüger submitted his dissertation titled “An inexpensive hyperbolic positioning system for tracking wildlife using off-the-shelf hardware” back in May of this year. Recently it was found online and can be viewed here (large pdf warning).

In his thesis Krüger explains his experiments with using RTL-SDR dongles to set up a very low cost wildlife monitoring system using TDOA (Time Difference of Arrival) techniques, and very low power beacons on the animal tags. TDOA is a difrection finding technique which involves using multiple receivers spread out over a region and calculating the difference in time from when the signal arrives at each receiver. With this information the position of the transmitter can be determined. Typically to do this the system clock in the computing hardware and OS needs to be synchronized as perfectly as possible between receivers, otherwise timing difference will cause huge errors in the position. Krüger uses synchronization bursts from a beacon, but notes that a real-time clock or GPS module could also be used for accurate time keeping.

In his experiment he set up two RTL-SDR receivers spaced 9 km apart and was able to obtain an accuracy of about 3.5m, which he writes is similar to other wildlife positioning systems that use tags with much higher power consumption. The computing hardware used at the RX station is a Raspberry Pi 3 powered by a 20W solar panel and batteries. There is also a wireless 3G modem for communications. The DSP software produced for the project is all open source and available on GitHub.

The RX System with RTL-SDR, Raspberry Pi, Mobile Broadband Modem, Power Supply and Solar Panel.