Data-driven method of reverse modelling for multi-function radar
Jian Ping Ou, Feng Zhao, Xiaofeng Ai, Jianhua Yang, Yongguang Chen · 2017
The common researches for radar system modelling are mostly based on deductive methods, which forward simulate the radar working process according to the information of radar parameters and expert system. However, the radar prior information obtained is limited under battlefield background. So it is difficult to model the radar operating modes and schedule schemes accurately, especially for the multi-function radars (MFRs) which are able to employ multiple modes flexibly. A novel method of reverse modelling for MFR is proposed. The information of the waveform is translated into grammar according to the theory of formal language, and the corresponding Finite-state Automaton (FSA) is composed as initialization. Then, according to the thought of data-driven, the transition relations and probabilities between MFR modes are yielded by analysing the intercepted signals. Finally, the stochastic finite automaton (SFA) is composed, achieving the MFR model by reverse modelling. Simulation with hypothetical MFR signal data is presented, showing that the proposed method is able to compose its SFA effectively, which can be used in MFR state recognition to support the adaptive radar countermeasures.