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• W4384524875 abstract "Abstract Target tracking provides important location-based services in many applications. The main challenge of target tracking is to combat the severe degradation problem in Non-Line-of-Sight (NLOS) scenario. Most Deep Learning algorithms available in literature to address this issue belong to batch learning with high complexity. This paper proposes a novel online sequential learning algorithm, Improved Recurrent Extreme Learning Machine (IRELM), to solve the NLOS target tracking problem as a position series prediction task. IRELM is able to train Recurrent Neural Network (RNN) inputs one-by-one and adaptively update the input weight, hidden weight, feedback weight and output weight. Extensive simulations and experiments prove the superior tracking performance and feasible complexity of IRELM over the state-of-the-art Deep Learning methods." @default.
• W4384524875 created "2023-07-18" @default.
• W4384524875 creator A5021246820 @default.
• W4384524875 date "2023-07-17" @default.
• W4384524875 modified "2023-10-16" @default.
• W4384524875 title "Non-line-of-sight target tracking with improved recurrent extreme learning machine" @default.
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• W4384524875 doi "https://doi.org/10.1007/s40747-023-01156-7" @default.
• W4384524875 hasPublicationYear "2023" @default.
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