Efficient Framework for UAV-Based Distributed Sensing

This paper proposes an unmanned aerial vehicle (UAV)-based distributed sensing framework that uses orthogonal frequency-division multiplexing (OFDM) waveforms to detect the position of a ground target. The area of interest, where the target is located, is sectioned into a grid of cells, where the radar cross-section (RCS) of every cell is jointly estimated by the UAVs, and a central node acts as a fusion center by receiving all the estimations and performing information-level fusion. A periodogram is employed for local estimation at each UAV, and a digital receive beamformer is assumed. The fused RCS estimates of the grid are used to estimate the cell containing the target. To evaluate the accuracy of the proposed framework, Monte Carlo simulations are carried out to obtain the detection probability, and our results show that the proposed framework attains a notable improved accuracy over a single mono-static UAV benchmark, due to the fusion from multiple sensing UAVs.