Acoustic Estimation of the Direction of Arrival of an Unmanned Aerial Vehicle Based on Frequency Tracking in the Time-Frequency Plane

Abstract

The development of unmanned aerial vehicles (UAVs) opens up a lot of opportunities but also brings some threats. Dealing with these threats is not easy and requires some good techniques. Knowing the location of the threat is essential to deal with an UAV that is displaying disturbing behavior. Many methods exist but can be very limited due to the size of UAVs or due to technological improvements over the years. However, the noise produced by the UAVs is still predominant, so it gives a good opening for the development of acoustic methods. The method presented here takes advantage of a microphone array with a processing based on time domain Delay and Sum Beamforming. In order to obtain a better signal to noise ratio, the UAV’s acoustic signature is taken into account in the processing by using a time-frequency representation of the beamformer’s output. Then, only the content related to this signature is considered to calculate the energy in one direction. This method enables to have a good robustness to noise and to localize an UAV with a poor spectral content or to separate two UAVs with different spectral contents. Simulation results and those of a real flight experiment are reported.

Vowel classification with combining pitch detection and one‐dimensional convolutional neural network based classifier for gender identification

“…A higher correlation indicates that the NCCF is closer to value one. Hence, the SHC is obtained at time t and frequency f and is defined to track the approximate F 0 candidate as follows: [13, 50] $$$\mathrm{S}\mathrm{H}\mathrm{C}(t,f)=\sum\limits _{{f}^{\prime }=-W/2}^{W/2}\prod\limits _{r=1}^{N}S\left(t,rf+{f}^{\prime }\right)$$$ where S ( t , f ) is the magnitude spectrum at frequency f for the t th frame, W is the spectral window length in frequency (Hz), N is the number of harmonics ( N = 3 is suggested) [15, 25], and f ′ is a discrete variable with an interval dependent on the FFT length (FFT length = 8192 samples for 12‐bit analog‐to‐digital converter on a microcontroller) and sampling rate (16 bits, sampling rate of 16 kHz, bandwidth = 8 kHz, bin width = 8 kHz/8192 = 0.9765 Hz). Hence, voice signal analysis can be performed with the FFT method.…”

“…A higher correlation indicates that the NCCF is closer to value one. Hence, the SHC is obtained at time t and frequency f and is defined to track the approximate F 0 candidate as follows: [13,50] SHCðt…”