AMSAT SA SPACE SYMPOSIUM 2019 Author :Anton Janovsky ZR6AIC How to use AI (Artificial Intelligence) to identify Radio signals using a RTL SDR dongle and Linux (Ubuntu) AMSAT SA SPACE SYMPOSIUM 2019 Author :Anton Janovsky ZR6AIC

AMSAT SA SPACE SYMPOSIUM 2019 Selecting the Best used AI framework Here is a graph with all the most used Deep learning frameworks available. AMSAT SA SPACE SYMPOSIUM 2019 HackRF RaspberryPi RTL Dongle

AMSAT SA SPACE SYMPOSIUM 2019 Background of AI How to use AI (Artificial Intelligence) to identify Radio signals using a RTL SDR dongle and Linux (Ubuntu) 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. AMSAT SA SPACE SYMPOSIUM 2019

AMSAT SA SPACE SYMPOSIUM 2019 Hardware. Google Cloud Tenserflow AMSAT SA SPACE SYMPOSIUM 2019 HackRF RaspberryPi RTL Dongle

Deciding how to go from here. I found this open source 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 does 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. AMSAT SA SPACE SYMPOSIUM 2019

AMSAT SA SPACE SYMPOSIUM 2019 Installing and setting up Let's 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] AMSAT SA SPACE SYMPOSIUM 2019

AMSAT SA SPACE SYMPOSIUM 2019 Installing and setting up 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 AMSAT SA SPACE SYMPOSIUM 2019

AMSAT SA SPACE SYMPOSIUM 2019 Let’s test to see if we can identify any signals 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% AMSAT SA SPACE SYMPOSIUM 2019

AMSAT SA SPACE SYMPOSIUM 2019 Let’s test to see if we can identify any signals. 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" AMSAT SA SPACE SYMPOSIUM 2019

AMSAT SA SPACE SYMPOSIUM 2019 Get existing DB that already lerd rf signals 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 to run so close all necessary applications otherwise you will get an out of memory error.. ( usef 500Mb in now time so I recommend using External hard drive) AMSAT SA SPACE SYMPOSIUM 2019

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. 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. 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 AMSAT SA SPACE SYMPOSIUM 2019

Identifying using my own Database 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 AMSAT SA SPACE SYMPOSIUM 2019

AMSAT SA SPACE SYMPOSIUM 2019 Questions? Complete details is available from my Blog http://zr6aic.blogspot.com AMSAT SA SPACE SYMPOSIUM 2019