Hybrid Quantum Neural Network Advantage for Radar-Based Drone Detection and Classification in Low Signal-to-Noise Ratio

TL;DR

This paper takes a fairly complex radar time-series model derived from electromagnetic theory, namely the Martin-Mulgrew model, that is used to simulate radar returns of objects with rotating blades, such as drones and finds that when that signal-to-noise ratio (SNR) is high, CNN outperforms the HQNN for detection and classification.

Abstract

In this paper, we investigate the performance of a Hybrid Quantum Neural Network (HQNN) and a comparable classical Convolution Neural Network (CNN) for detection and classification problem using a radar. Specifically, we take a fairly complex radar time-series model derived from electromagnetic theory, namely the Martin-Mulgrew model, that is used to simulate radar returns of objects with rotating blades, such as drones. We find that when that signal-to-noise ratio (SNR) is high, CNN outperforms the HQNN for detection and classification. However, in the low SNR regime (which is of greatest interest in practice) the performance of HQNN is found to be superior to that of the CNN of a similar architecture.

Figures (18)

• Figure 1: Real and imaginary valued portions of the time series for drone Parrot Disco, produced using the MM model. The signal contains no noise.
• Figure 2: Real and imaginary valued portions of the STFT of drone Parrot Disco, produced using the MM model. The signal contains no noise.
• Figure 3: Real and imaginary valued portions of the STFT for drone Parrot Disco, produced using the MM model, with SNR -5.
• Figure 4: Real and imaginary valued portion of the STFT for drone DJI Matrice 300 RTK, produced using the MM model, with SNR -5.
• Figure 5: Time series of gaussian white noise.