Department Publications

Unmanned aerial vehicles are increasingly deployed across diverse domains including security surveillance, precision agriculture, infrastructure inspection, and logistics. However, their rapid proliferation raises critical concerns regarding unauthorized intrusions, smuggling, and adversarial exploitation. Robust drone detection and classification systems are therefore essential. In this paper, we propose a multi-modal framework that fuses frequency-modulated continuous-wave radar and acoustic sensing using a transformer encoder-based architecture. Unlike single-modality approaches, our method exploits cross-modal complementarity to improve robustness against environmental noise, false alarms, and stealthy drone operations. Notably, our approach directly processes raw acoustic waveforms without domain transformations such as spectrograms, reducing preprocessing overhead and improving real-time …

Sensing and early detection of small unmanned aerial systems (sUAS) are critically important in modern-day defense. In dense urban and indoor environments, detection becomes extremely challenging due to dense multipath, fading, low-altitude flight, and non-line-of-sight (NLOS) radio-frequency propagation. This paper presents a continuous-wave multiple-input multiple-output radar and a deep learning model for sUAS detection using NLOS signals. The radar operates at 2.47 GHz, and spectral correlation densities derived from rotational micro-Doppler signatures from the rotor blades are used as inputs to the deep learning model. Experimental results demonstrate an overall detection accuracy of across a dataset of five drone types, confirming the feasibility of sUAS detection in dense urban environments without direct line-of-sight conditions.

This paper presents the design and implementation of a multi-band multi-mode radar for reliable detection and identification of micro unmanned aerial systems (UASs). A rotational micro-Doppler continuous-wave radar operating at 5.85 GHz and a frequency-modulated continuous-wave (FMCW) radar operating with 77–80 GHz bandwidth are used for multi-modal radio frequency (RF) sensing, localization and detection. Fusion of the two radars is achieved with a neurosymbolic artificial intelligent model. Cyclostationary feature extraction is used for the micro-Doppler signature extraction. Wavelet domain feature extraction is used on the FMCW radar outputs. A custom neural engine is used for multi-modal sensor fusion across the two RF bands and radar types.

Drone audio broadcasting enables rapid, flexible speech communication in scenarios such as search and rescue, disaster response, and public safety by projecting speech from onboard loudspeakers. However, speech played from drones is contaminated by its own intense, time-varying rotor noise, while increasing playback level is often undesirable as it raises concerns about privacy and noise pollution. Speech intelligibility improvement methods could be used to improve the intelligibility of speech under noise without increasing its sound level. To date, most methods have been evaluated using conventional maskers such as speech shaped noise or babble noise, rather than fluctuating drone noise. This study presents a subjective listening test with native New Zealand English listeners, that investigates the performance of four modern speech intelligibility improvement methods under varying input signal-to-noise ratio (SNRs) with two recorded drone noise conditions. The results show that the spectral shaping and dynamic range compression (SSDRC) and its variant consistently achieved better performance yielding up to a 72% intelligibility score improvement over unmodified speech at an input SNR of-16 dB, with diminishing gain at higher input SNR levels. Performance difference between drone types was are while the ranking of the evaluated methods was consistent across both conditions.