Detection and Classification of Drones Through Acoustic Features Using a Spike-Based Reservoir Computer for Low Power Applications
Alex Henderson, Chris Yakopcic, Steven D. Harbour, Tarek M. Taha · 2022 IEEE/AIAA 41st Digital Avionics Systems Conference (DASC) · 2022
Over the past few decades, the use of drones for applications such as defense, reconnaissance, agriculture, law enforcement, and others has dramatically increased. Although drones are useful for these applications, they can also be utilized to perform malicious activities, thus compromising the safety and integrity of physical infrastructures. To address this issue, various techniques have been developed to detect and identify drones, including radar, visual analysis, and radio-frequency signal processing. Furthermore, deep learning algorithms based on the recognition of acoustic drone features have been proposed to automate the detection process and overcome the current limitations in modern drone detection systems. This paper presents an auditory drone detection and identification system based on the sparse, event-driven communication nature of Spiking Neural Networks (SNNs). We investigate the use of a spiking reservoir computing model, known as a Liquid State Machine (LSM), that offers a computationally light alternative to the deep learning approaches of previous works. The LSM based auditory drone detection and identification system is demonstrated on a publicly available acoustic drone dataset, achieving an accuracy of 97.13% and 93.25% on the detection and identification tasks, respectively. To the best of our knowledge, this work presents the first spike-based implementation of an auditory drone recognition system. Moreover, this paper highlights the potential for low size, weight, and power neuromorphic hardware deployment for drone applications that may be limited to energy-constrained environments.