Abstract

The growing prevalence of unmanned aerial vehicles (UAVs), also known as drones, has intensified the demand for robust detection technologies for security and counterterrorism applications. Acoustic sensors provide a promising solution for UAV detection, although their effectiveness is strongly influenced by environmental noise, making advanced signal processing techniques necessary. Most UAV propulsion emission is concentrated within a specific frequency band, making it possible to extract dominant frequencies for detection. The intensity of this band can be modeled as a stochastic process defined by the sum of normally distributed amplitudes across the selected frequencies. Detection performance depends on how well the UAV intensity distribution can be distinguished from the background noise distribution. The problem can therefore be formulated as a hypothesis test between noise-only and UAV-present conditions. Classification accuracy is improved by selecting appropriate frequency components and optimizing amplitude estimation methods to reduce overlap between the two distributions. Algorithm selection is crucial for classifier performance. This paper uses the Kaczmarz projection method as the primary algorithm for amplitude estimation within the selected frequency cluster. The method avoids matrix inversion and associated singularity problems, supports closely spaced frequencies and has linear computational complexity. However, the lack of tunable parameters can produce noisy estimates. The recursive least squares (RLS) algorithm with rank two updates offers adjustable window size and forgetting factor parameters but has quadratic computational complexity. Moreover, ill-conditioning of the information matrix limits parameter selection and reduces the separation between UAV and noise distributions compared with the Kaczmarz method. Despite this limitation, RLS achieves better noise cancellation because of its adaptive parameter control. The proposed classifier therefore combines RLS-based noise suppression with Kaczmarz-based dominant frequency extraction. The approach is validated using real acoustic measurements of UAVs and environmental background noise.

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