Friday, March 15, 2019

How to use AI (Artificial Intelligence) to identify Radio signals using a RTL SDR dongle and Linux (Ubuntu)

How to use AI (Artificial Intelligence) to identify Radio signals using a RTL SDR dongle and Linux (Ubuntu)
Identifying Radio stations

I was wondering if there is not a good framework to identify RF signals as I wanted to add some capabilities to my SDR's to identify RF signal.

I was thinking of a way to recognize Satellite signals and the automatically apply the necessary Demodulator's and decoders for the specific satellite.

I was looking at AI Deep Learning library to be able to identify RF Radio signals. There are countless deep learning frameworks available today.

By using Python3 and rtl-sdr dongle it would be possible to scan a frequency range trying to identify a satellite.

Here is a graph with all the most used Deep learning frameworks available.
Deep Learning Frameworks.

I found this opensource project called cnn-rtlsdr and it is available from github here https://github.com/randaller/cnn-rtlsdr

This framework is using Keras and TensorFlow to learn and recognize the RF signals.

So how dose it work?

You first need take an clean RF signal and digitize it and then let the framework learn its signature. The more you letting the AI framework learn a specific signal the more accurate it will able to recognize the RF Signal.



Here is my instillation procedure to get it working on my Ubuntu 18.10 Laptop

Installation Procedure.

Lets check if you have version 2 or 3 of python.
You need version 3
python -V
apt-get install git
git clone https://github.com/randaller/cnn-rtlsdr.git
cd cnn-rtlsdr



sudo apt-get update

sudo apt-get install python3-pip
sudo apt-get install rtl-sdr

sudo apt-get install build-essential libssl-dev libffi-dev python-dev

sudo pip3 install --upgrade pip

sudo pip3 install tensorflow
sudo pip3 install pyrtlsdr

sudo pip3 install scipy

[remove dongle]
rmmod dvb_usb_rtl28xxu rtl2832
[insert dongle]


Installing rtl-sdr and calibrating the frequency offset.

Using the Kal utility to calibrate your dongle offset using the GSM network.
Installing Kal
sudo apt-get install automake
sudo apt-get install libtool
sudo apt-get install libfftw3–dev
sudo apt-get install librtlsdr-dev
sudo apt-get install libusb1.0.0-dev

git clone https://github.com/steve-m/kalibrate-rtl.git
cd kalibrate-rtl/
./bootstrap
 ./configure
 make
 sudo make install


In south Africa we can use the GSM900 frequency
Lets run Kal
kal -s GSM900
Found 1 device(s):
  0:  Generic RTL2832U OEM

Using device 0: Generic RTL2832U OEM
Found Rafael Micro R820T tuner
Exact sample rate is: 270833.002142 Hz
[R82XX] PLL not locked!
kal: Scanning for GSM-900 base stations.
GSM-900:
    chan: 40 (943.0MHz - 736Hz)    power: 25909.17
    chan: 47 (944.4MHz - 817Hz)    power: 28430.99
    chan: 63 (947.6MHz - 128Hz)    power: 29010.57
    chan: 69 (948.8MHz - 597Hz)    power: 32479.73

We now select the strongest Station to measure the average frequency offset
kal -c 69
Found 1 device(s):
  0:  Generic RTL2832U OEM

Using device 0: Generic RTL2832U OEM
Found Rafael Micro R820T tuner
Exact sample rate is: 270833.002142 Hz
[R82XX] PLL not locked!
kal: Calculating clock frequency offset.
Using GSM-900 channel 69 (948.8MHz)
average        [min, max]    (range, stddev)
- 413Hz        [-460, -354]    (106, 30.402500)
overruns: 0
not found: 0
average absolute error: 0.435 ppm



We now need to test to see if we can identify any signals using the default test learn data.

Final test

The Default script will scan the normal FM broadcast band 88 to 108Mhz.
Although it detects the radio stations as TV is ok as the test data id was tv.

sudo python3 predict_scan.py
Found Rafael Micro R820T tuner
[R82XX] PLL not locked!
88.400 MHz - tv 99.98%
89.600 MHz - tv 99.91%
91.500 MHz - tv 99.99%
92.700 MHz - tv 99.93%
94.700 MHz - tv 99.13%
95.900 MHz - tv 98.04%
98.000 MHz - tv 100.00%
99.200 MHz - tv 99.95%
99.600 MHz - tv 81.13%
101.500 MHz - tv 99.91%
102.700 MHz - tv 100.00%
105.100 MHz - tv 100.00%
106.300 MHz - tv 99.56%



We now need to learn the different Rf signals so we can identify it.
Best way to do this is with an rtl dongle and your signal of interest.

Learning from existing RF signal Database.

1) "wfm" Wide band FM
2) "tv" TV signal
3) "gsm" GSM signal
4) "tetra" Tetra DMR
5) "dmr" DMR
5) "other"

Link to database https://drive.google.com/file/d/1PuhzXkk6AVwXPPKjtFUCpQVsqOOlszu8/view
Some RF signals have been learned by other users so you don't need to learn the common RF signals but just import the learn database.

Unzip the file in the cnn-rtlsdr directory
Then run the following command to learn the RF signal 
It takes about 80secons to learn a sample. So go and have a coffee or a bear :-)
Make sure you have your rtl_sdr dongle connected as the code will do a test at the end of the learning procedure.
python3 train_keras.py
You will need a lot of memory for your application tu run so close all necessary applications otherwise you will get an out of memory error..
Learning RF samples for the following RF signals.
When the learning is complete the script will do a test with the RTL-sdr dongle.
Testing signals with the new database. 


Lets learn our own signal not yet in database.
I want to learn a Satellite Telemetry signal from Satellite.

Learning my own unique signal.



python3 train_keras.py
Using TensorFlow backend.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1062: calling reduce_prod (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.
Instructions for updating:
keep_dims is deprecated, use keepdims instead
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:2550: calling reduce_sum (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.
Instructions for updating:
keep_dims is deprecated, use keepdims instead
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1123: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.
Instructions for updating:
keep_dims is deprecated, use keepdims instead
Train on 64972 samples, validate on 27844 samples
Epoch 1/50
64972/64972 [==============================] - 70s - loss: 0.3469 - acc: 0.8527 - val_loss: 0.0716 - val_acc: 0.9836
Epoch 2/50
64972/64972 [==============================] - 72s - loss: 0.0575 - acc: 0.9839 - val_loss: 0.0731 - val_acc: 0.9791


...
64972/64972 [==============================] - 79s - loss: 0.0016 - acc: 0.9995 - val_loss: 0.0069 - val_acc: 0.9984
Epoch 49/50
64972/64972 [==============================] - 80s - loss: 7.5126e-04 - acc: 0.9998 - val_loss: 0.0093 - val_acc: 0.9981
Epoch 50/50
64972/64972 [==============================] - 78s - loss: 0.0065 - acc: 0.9983 - val_loss: 0.0357 - val_acc: 0.9923

Found Rafael Micro R820T tuner
[R82XX] PLL not locked!
92.9 wfm 99.9636411667
49.25 other 99.8086333275
95.0 other 99.9997735023
104.0 other 99.9999880791
422.6 other 99.9927401543
100.5 other 99.9997496605
120.0 other 100.0
106.3 wfm 100.0
942.2 other 99.999666214
107.8 other 100.0
Validation: 30.0





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