No abstract

Passive radar provides a means of spectrum sharing in a congested environment. In passive radar, the radar design problem is one of emitter selection, rather than waveform optimisation. This study combines relevant criteria (signal-to-noise ratio, ambiguity function integrated sidelobes, effective multistatic resolution area and contrast ratio) in a multiobjective optimisation to select subsets of available emitters for passive multistatic synthetic aperture radar (SAR). Extensions to wellknown monostatic and bistatic performance criteria are defined for the multistatic SAR case. Assumed limitations in the number of available receiver channels constrain the emitter selection. The proposed emitter selection framework is demonstrated for a simulated scenario of 24 analogue and digital emitters, with a constraint of four receiver channels. Results indicate that the proposed ranking successfully differentiates between emitter sets resulting in good images and those sets resulting in poor images, providing a first step towards formulating a general image quality equation for multistatic SAR.

In this article, a machine learning aided electronic warfare (EW) system is presented and the simulation results are discussed. The developed EW system uses an automatic decision tree generator to create engagement protocol and a fuzzy logic model to quantify threat levels. A long-short term memory (LSTM) neural network was also trained to predict the next signal set of multifunction radars. The simulation results demonstrate the effectiveness of the developed EW system's ability to engage multiple multifunction radars.