A Fast Sequential MLE Algorithm for Jet Engine Classification With Radar

Abstract

Radar signals scattered by an operating jet engine contain a unique signature that can be used to classify the type of engine, and therefore the aircraft. We present a new algorithm based on the maximum likelihood estimation (MLE) principle that can identify the number of blades of the rotors in the jet engine from the returned radar signal. Unlike the conventional algorithms, the proposed algorithm avoids the estimation/inspection of the autocorrelation of radar signal and utilizes the property of jet engine modulation (JEM) harmonics together with the successive interference cancellation (SIC) technique in the multi-dimensional search, which saves about 66.7% computing time compared to the autocorrelation based algorithm (specifically, the empirical mode decomposition (EMD) based algorithm). The proposed algorithm is assessed with synthetic data as well as real radar data using a mock-up jet engine. Specifically, by evaluating the mean-squared-errors and Cramer-Rao lower bound, we verify that the proposed SIC algorithm performs better than previously known jet engine-classification algorithms.