EC4690 Joint Network-Enabled Electronic Warfare II

The course is intended for international students and contains the same material as EC4680. The course continues the discussion of counter electronic support and begins with an introduction to low-probability-of-intercept (LPI) emitter signaling techniques and technologies. The origin and importance of the LPI emitter are emphasized. Case studies are shown to demonstrate the capability of the LPI emitter as an anti-ship capable missile seeker. Network enabled receiver techniques are presented highlighting the benefits of the sensor-shooter-information grid and swarm intelligence. The new challenges facing the intercept receiver design and the trends in receiver technology are addressed. To increase the processing gain of the receiver, time-frequency signal processing methods are presented and include the pseudo Wigner-Ville distribution, quadrature mirror filter bank trees for wavelet decomposition and the Choi-Williams distribution. Bi-frequency techniques are also emphasized and include cyclostationary processing for estimating the spectral correlation density of the intercepted signal. Calculations using each signal processing method are shown to demonstrate the output information and its correlation with the input signal parameters. New detection results are then derived by the student for various LPI signaling schemes to illustrate the parameter extraction methods developed. Autonomous emitter classification architectures are also presented. Laboratory simulation exercises are conducted to demonstrate the concepts.

Prerequisite

EC3700

Lecture Hours

3

Lab Hours

2

Course Learning Outcomes

·       Review of special modulations used by stealth emitters.

·       Learn how electronic warfare receivers are designed.

·       Understand the tradeoffs in the various architectures.

·       Examine the different technologies that can be used in the receiver.

·       Understand the kernel function correlation between the Cohen class of distributions.

·       Investigate the Choi-Williams time frequency distribution for interception of stealth waveforms.

·       Use the pseudo-Wigner-Ville time frequency distribution of the detection of stealth modulations.

·       Use quadrature mirror filters to perform a wavelet decomposition of the input signal.

·       Investigate the cyclic spectral density function for the identification of modulation parameters.

·       Learn how to develop feature vectors using the time-frequency, bi-frequency detection planes.

·       Use a non-linear network coupled with a principal component analysis to correctly classify the detected modulation.