RFGeolocation

Testbed for RF Geolocation by  Nathan Catt and  Andrew Belk



The goal of this project, Testbed Development for Geolocation of RF Emitters, was to improve upon the testbed used in conjunction with the Air Force Research Laboratory (AFRL) geolocation algorithm. The military has many applications where locating radio signals are important. Locating downed pilots or pinpointing communication signals of the enemy are just a couple of examples. Rose-Hulman Institute of Technology is currently working on compressive sensing algorithms to help locate the source of radio frequency (RF) signals. These algorithms will be implemented on a ground based mobile platform as part of this project development. The mobile platform will be controlled by a user. A system must first be designed that includes reconfigurable sensors with at least one transmitter and two receivers. The mobile testbeds will need to demonstrate the ability to receive signals and locate the transmitter. A final demonstration will be completed at the AFRL. This testbed could be made autonomous or implemented on an aerial platform in future development. The main goal of this project was to create a system that had individual sensors that can navigate through a predefined grid and use the received signal strength taken at different points and angles to locate the emitting signal. Our team used a mobile platform that has a programmable motor controller, a Software Defined Radio (SDR), a microprocessor, and various sensors. Our team was able to implement an Xbox 360 remote as a user control for movement and access to sensor data. The initial plan was to have an autonomously controlled system, but the accuracy of the off-the-shelf GPS was not sufficient. Our team suggests using a more accurate GPS sensor in order to implement a waypoint navigation algorithm.



Project Overview
For this project, our team has been working for the United States Air Force Research Lab (AFRL). The United States Air Force (USAF) has the desire to be able to locate emitters at different frequencies for a number of different reasons, including locating enemy communication signals. The goal of our team’s project was to create a testbed that would collect the required data to run a compressive sensing algorithm that is currently being developed by the Rose-Hulman Institute of Technology Department of Mathematics. Our project was intended to be a fundamental stepping block for much more complicated data collection, but more importantly, to collect data to test the effectiveness of the geolocation algorithms. The main deliverables for this project include the testing hardware developed by our team and a detailed testing plan. Our team was able to achieve creating reconfigurable sensors that are mobile and able to collect data when requested by the user.





Microprocessor
We chose to use the BeagleBone Black for our microprocessor. This enabled us to have plenty of room for additional sensor expansion. A BeagleBone Proto Cape was used to get rid of wire clutter.

We chose the Alfa Wi-Fi USB Adapter as a reliable/powerful Wifi adapter. The Wifi adapter must be plugged into the USB port upon start up in order for the connection to work.

Software
Our code can be cloned from GitHub.





Client
The Client code is ran on the BeagleBone. This code integrates all of the sensors and communicates information back to the host computer. The current BeagleBones are the Rev B version running Angstrom with the September 4th, 2013 image. The Client runs two primary programs. The first (Client.c) interfaces with the Central Processing Node, while the second (SDRdata.py) interfaces with the SDR. These two programs interface through a UNIX socket.

Wifi
Getting the BeagleBone (aka Client) to connect to the Wifi was a challenge. Direct instructions on how to set up the Wifi can be found here.

Full System Testing
For correct operation, the following boot sequence must be followed:


 * 1) Power on the Wi-Fi router
 * 2) Connect the central processing node to the router
 * 3) Power the Beaglebone; wait for it to connect to the network
 * 4) Power the motor controller and the SDR

Location of Measurements



Simulation of Position Points



MATLAB
Integrating MATLAB to view data in real-time is a potential addition to this project. This would enable users to understand and view solutions to the algorithm in real-time while controlling the rovers. In order to do this, a UNIX socket could be set up between the Server code and the MATLAB code so that once the Server code receives data, it is then relayed to MATLAB. Once MATLAB receives the code, it could add it to the table of previous data points and recalculate the position of the sensor using the algorithm.

Wild Thumper Controls
The controls to the Wild Thumper seem to be incorrect. Videos online seen here, show the Wild Thumper moving seamlessly through rough terrain. Our Wild Thumpers have a difficult time navigating even on pavement. This could possibly be due to its' heavy payload. Having two battery packs on board does not help.