This paper presents a radar-based algorithm for autonomous estimation of drone intention. The algorithm is based on radar's kinematic measurements, providing fast and robust intention estimation for multiple targets. The core idea of the proposed algorithm is to build intention-specific features for each intention in advance and use them in actual drone surveillance situations. To effectively depict the trajectory characteristics of various intentions, the trajectory frequency is computed on multiple phase planes through Monte Carlo Simulations. Finally, a naive Bayes classifier is applied to integrate the trajectory likelihood in different phases and ultimately compute the intention likelihood of all intentions concerned. Numerical simulations for the three candidate intentions of image acquisition, smuggling, and kamikaze attack demonstrated the performance of the presented method. The simulation results show that one can estimate the true intention of a specific drone by comparing the value of each intention likelihood.

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This paper presents parametric investigation results on a staring FMCW radar system which targets drone swarms. The parametric investigation has been carried out by using the RAPID-SIM which facilitates system-level analysis of drone swarms' radar signatures. This paper explains concepts of the simulator's each module and also covers two parametric investigation results which deal with quantitative performance criteria for the design of the anti-drone swarms radar system.@en