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Positional estimation of invisible drone using acoustic array with A-shaped neural network

Image-based object detection is a commonly used algorithm for anti-drone surveillance system. However, there is a disadvantage that it cannot be detected if the target is not visible within the image. In this paper, we propose drone position estimation algorithm using acoustic array to detect objects complementing the difficulty of estimating sudden directional shifts in hiding, occurrence situations and quickly out of the vision of the camera. Sound data is converted into an image via mel-spectrogram to facilitate image sensor and sound sensor fusion and the drone position is estimated via the Convolution Neural Network. The proposed neural network is the A-shape neural network, which consists of up-sampling and down-sampling. Through these methods, we achieve RMSE of 13.045 pixels and show that the location of the drone can be estimated efficiently.