abstract
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One of the important objectives of a Radar Warning Receiver (RWR) aboard a tactical aircraft is to evaluate the level of threat posed by hostile radars in an extremely complex Electronic Warfare (EW) environment in reliable, robust and timely manner. For the RWR objective to be achieved, it passively collects electromagnetic signals emitted from potentially hostile radars. One class of such radar systems is the Multi-Function Radar (MFR) which presents a serious threat from the stand point of a RWR. MFRs perform multiple functions simultaneously employing complex hierarchical signal architecture. The purpose of this paper is to uncover the evolution of the operational mode (radar function) from the view point of a target carrying the RWR when provided with noisy observations and some prior knowledge about how the observed radar functions. The RWR estimates the radar's threat which is directly dependant on its current mode of operation. This paper presents a grid filter approach to estimate operational mode probabilities accurately with the aid of pre-trained Observable Operator Models (OOMs) and Hidden Markov Models (HMMs). Subsequently, the current mode of operation of a radar is estimated in the maximum a posteriori (MAP) sense. Practicality of this novel approach is tested for an EW scenario in this paper by means of a hypothetical MFR example. Finally, we conclude that the OOM-based grid filter tracks the mode of operation of a MFR more accurately than the corresponding HMM-based grid filter.