Micro-Doppler Signature Based Helicopter Identification and Classification Through Machine Learning

Abstract

This paper focuses on identification of helicopter by exploiting the concept of micro-Doppler effect which is prominent in targets containing rotating, oscillating or vibrating parts in it. Radar received signal is analyzed by Short Time Fourier Transform (STFT) to extract the micro Doppler (mD) signature. From the mD signature, the helicopter parameters are estimated. In a multiple helicopters scenario, estimated parameters will be a mixure, pertaining to the multiple helicopters. These parameters are classified further using a machine learning algorithm, namely k-means clustering to classify the helicopters. Simulated results for the synthesized received signal shows the betted estimates of the helicopter parameter through mD signature. Dataset containing basic parameters like number of blades, blade length and rotational rates of the UN-1N helicopter (rotor with 2 blades), the SH-3H helicopter (rotor with 5 blades) and the CH-54B helicopter (rotor with 6 blades) are considered for the classification. Results show a good classification. When analysed with different SNR level in dataset, at lower SNR, observed some ovelapping in the classification.

A Self-Learning Mechanism-Based Approach to Helicopter Entry and Departure Recognition

“…As shown in Figure 3 , the FATO is the final approach and take-off area, with the helicopter’s entry and departure position operating in the FATO. With the exception of hospital heliports, heliport identification signs should be set in the center of the aiming point markings, i.e., the helicopter’s entry and departure process is indicated and guided by the heliport identification sign “H” on the helipad [ 30 ]. The “H”, which serves as the identification mark of the heliport, allows the pilot to better observe the helicopter in the air and to correct the landing point, and the helicopter parks in the area clearly marked with an H.…”