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On May 26 at 15:00 p.m. (Moscow time)

Unmanned aerial vehicle (UAV), is a promising technology that is considered as one of the main key-enablers of sixth-generation (6G) cellular systems. UAVs have many applications, in varying fields, such real-time surveillance, assisting network coverage, and remote sensing applications. Direction of Arrival (DOA) estimation is a challenging problem with UAVs as well as many other signal processing applications such as radar, wireless communication, and acoustic signal processing. It involves determining the direction from which a signal is arriving at a sensor or antenna array.

DOA estimation has numerous applications, including radar systems, wireless communication networks, and sonar systems. Accurate DOA estimation is essential for improving the performance of these systems, such as increasing system capacity, improving signal-to-noise ratio, and reducing interference. DOA estimation can be performed using various techniques, including beamforming, subspace-based methods, and maximum likelihood estimation.

The choice of technique depends on various factors, such as the number of sensors, the signal-to-noise ratio, and the computational complexity of the algorithm. To this end, this study considers developing novel methods for DOA estimation in UAV systems using sparse sensor arrays and deep neural networks.Moreover, the proposed schemes will be implement using software-defined radio (SDR). The proposed methods will be evaluated for heterogeneous scenarios.