Super resolution DOA estimation based on deep neural network
Wanli Liu
Abstract
Recently, deep neural network (DNN) studies on direction-of-arrival (DOA) estimations have attracted more and more attention. This new method gives an alternative way to deal with DOA problem and has successfully shown its potential application. However, these works are often restricted to previously known signal number, same signal-to-noise ratio (SNR) or large intersignal angular distance, which will hinder their generalization in real application. In this paper, we present a novel DNN framework that realizes higher resolution and better generalization to random signal number and SNR. Simulation results outperform that of previous works and reach the state of the art.
Topics & Concepts
GeneralizationComputer scienceSIGNAL (programming language)AlgorithmArtificial neural networkDeep neural networksArtificial intelligenceNoise (video)Signal-to-noise ratio (imaging)Resolution (logic)Direction of arrivalPattern recognition (psychology)State (computer science)Speech recognitionTelecommunicationsMathematicsImage (mathematics)Antenna (radio)Programming languageMathematical analysisDirection-of-Arrival Estimation TechniquesSpeech and Audio ProcessingBlind Source Separation Techniques