Abstract
An autonomous method for recognizing radar pulse modulations based on modulation components analysis is introduced in this paper. Unlike the conventional automatic modulation classification methods which extract modulation features based on a list of known patterns, this proposed method classifies modulations by the existence of basic modulation components including continuous frequency modulations, discrete frequency codes and discrete phase codes in an autonomous way. A feasible way to realize this method is using the features of abrupt changes in the instantaneous frequency rate curve which derived by the short-term general representation of phase derivative. This method is suitable not only for the basic radar modulations but also for complicated and hybrid modulations. The theoretical result and two experiments demonstrate the effectiveness of the proposed method.
Highlights
Pulse modulation classification plays an essential role in modern intercept receivers for electronic warfare (EW) applications such as threat recognition and analysis, construction of effective jamming responses and radar emitter identification [1]
2.1 Categories of possible radar pulse modulations In order to analyze the various kinds of intentional modulation on pulse (IMOP) conveniently, we categorize them based on three kinds of basic IMOP components including the continuous frequency modulation (CFM), the discrete frequency coded (DFC) modulation and the discrete phase coded (DPC) modulation
3.1 Modulation families classification based on modulation components analysis (MCA) method From the analyses above we know that the estimated instantaneous frequency rate (IFR) curve for an IMOP in the CFM family has no abrupt changes
Summary
Pulse modulation classification plays an essential role in modern intercept receivers for electronic warfare (EW) applications such as threat recognition and analysis, construction of effective jamming responses and radar emitter identification [1]. Wang et al EURASIP Journal on Advances in Signal Processing (2016) 2016:98 as the ambiguity function (AF) [15], Zhao Altas and Marks (ZAM) representations [16], the Rihacek distribution and the Hough transform [17], were applied to extract modulation features for five kinds of radar pulses These existing algorithms work effectively when the radar IMOPs are in the list of their databases, but fail when they are faced with some complicated or unknown modulated pulses. A two-stage classification procedure which contains a “modulation family classification” and an “accurate classification” is proposed The former part works autonomously based on a modulation components analysis (MCA) method and can extract partial information even when the IMOP of the radar pulse is not in the knowledge database. Two experiments to demonstrate the effectiveness of the proposed method are designed in Section 4, and Section 5 gives the main conclusions
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