Booz Allen Signals Engineering and Analytics Bootcamp

What Is Signals Engineering and Analytics?

Signals engineering and analytics is a field that combines expertise in digital signal processing (DSP), signals propagation theory, collection hardware knowledge, and electromagnetics to derive intelligence from signals data collected by radio frequency (RF) receivers or other sensor platforms.

Course Offering

In Booz Allen’s comprehensive signal processing training, you’ll delve into the world of SIGINT and ELINT analysis guided by our seasoned experts. You’ll explore the intricacies of RF signals properties, master signal processing techniques and algorithms, and acquire practical skills through hands-on experience in coding DSP algorithms and configuring and operating SDR.

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Course Curriculum

TechSig 101 – Introduction to Technical Signals Analysis

This course provides a survey-level introduction to wireless communications, RF signals, RF signals collection, and technical analysis of RF signals. We will observe the basic principles of RF signals through experimentation with software-defined radios (SDR).​

TechSig 201 – Math, Theory, RF Datasets

This course provides the foundation in math and theory of signals analysis, including analog-to-digital conversion and sampling/aliasing, Fourier analysis and transforms, filter design, noise properties and reduction, probability and statistics, and linear algebra. Participants should have a good theoretical foundation in RF signals analysis, generating visualizations, simulating basic transmitter/receiver models, and evaluating signal/noise metrics.

TechSig 301 – Detection Theory

This course introduces detection theory for RF signals. After this course, participants given an RF recording will be able to apply different detection techniques to detect signals present.

TechSig 401 – Machine Learning for RF Signals

This course provides a comprehensive understanding of machine learning (ML) for RF signals detection, matched filter and pulsed signals, probability theory for signals detection, cyclostationary processing for signals detection, and overall detection improvement methods.

TechSig Capstone Project

  • Create linear frequency modulated (LFM), binary phase-shift keying (BPSK), and noise signals of varying signal-to-noise ratios (SNR) – create a labeled machine learning dataset of these signals 
  • Perform signal analysis on sample signal files for each class: time, frequency, constellation, spectrogram plots, calculate SNR
  • Create spectrogram images of each signals file
  • Leveraging TensorFlow/Keras, train and test a spectrogram-image-based signal classifier neural network
  • Format code for this project into a well-documented project in Github, with Python package requirements documented, and a diagram of the data flow for your ML pipeline  ​

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