Unmanned Aerial Vehicle Acoustic Localization Using Multilayer Perceptron

Abstract

Unmanned Aerial Vehicles (UAVs), in recent years, are developing rapidly. However, when fly into private residences or public areas without authorizations, UAVs pose latent threats to personal privacy and public security. UAVs localization is a significant part of an anti-UAV system. In this paper, a remolded acoustic energy decay model preserved relative in acoustic energy attenuation inverse of distance square is used to generate training data. Multilayer perceptron(MLP) is the model to train these data and predicts accurate relative 3D space coordinates. Four different UAV flight trajectories are simulated. We also test robustness against noise with different levels. Simulation experiment results show that the deviation is less than 1.48 m in specific distances and noise levels, even with higher noise levels the deviation can still be accepted. The problem of limited detection range is overcome by the use of wireless sensor networks (WSNs) with more sensors. Long and short-term memory (LSTM) is investigated, but it doesn’t outperform MLP in accuracy and processing time.