In this paper, we proposed a method for radar modulation identification based on the measurement of inequality in the frequency domain. Gini's coefficient was used to exploit the inequality in the powers of spectral components. The maximum likelihood classifier was used to classify the detected radar signal into four types of modulations: unmodulated signal (UM), linear frequency modulation (LFM), non-linear frequency modulation (NLFM), and frequency shift keying (FSK). The simulation results demonstrated that the proposed method achieves an overall identification accuracy of 98.61% at a signal-to-noise ratio (SNR) of -6dB without a priori information such as carrier frequency, pulse arrival times or pulse width.

This paper investigates the autonomous decision-making process of the selection of alternative countermeasures against threats in electronic warfare settings. We introduce a threat model, which represents a specific threat pattern, and a methodology that decides the best countermeasure against real-time threats using the decision theory. To determine the optimal countermeasure, we model the probabilities of the effects of countermeasures, if executed, and combine the probabilities with their utilities. This methodology based upon the inductive threat model calculates the expected utilities of countermeasures which are applicable given a situation, and provide an intelligent command and control agent with the best countermeasure to threats. We present empirical results that demonstrate the agent's capabilities of choosing countermeasures to threats in simulated electronic warfare settings.

In electronic warfare support systems, the analysis of PRI (Pulse Repetition Interval) modulation characteristics for a radar pulse signal has attracted much interest because of the problem of the identification ambiguity in dense electronic warfare signal environments. A new method of recognizing the PRI modulation type of a radar pulse signal is proposed for electronic warfare support. The proposed method recognizes the PRI modulation types using classifiers based on the property of the autocorrelation of the PRI sequences for each PRI modulation type. In addition, the proposed method estimates the PRI modulation period for the PRI modulation type with the periodicity. Simulation results are presented to show the performance of the proposed method.