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Deep Learning-based Localization and Outlier Removal Integration Model for Indoor Surveillance
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  • Van-Linh Nguyen ,
  • Lan-Huong Nguyen ,
  • Po-Ching Lin ,
  • Ren-Hung Hwang
Van-Linh Nguyen
National Chung Cheng University

Corresponding Author:[email protected]

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Lan-Huong Nguyen
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Po-Ching Lin
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Ren-Hung Hwang
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Abstract

Directional antenna technologies are crucial to enhance high-speed data transmission in emerging wireless communications such as mmWave. These technologies can enable high-accuracy radio positioning by exploiting spatial-temporal signal processing in wideband beamforming space. However, the radio positioning technique potentially poses surveillance risks to mobile users, particularly being tracked illegally. This work presents a novel scheme to track a user in a building based on passively received signals. The scheme includes a Deep Convolutional Neural Network (DCNN) localization module to train on the accumulated channel impulse responses (CIR) and corresponding Angle-Delay profiles. The user’s estimated locations from the DCNN localization are then refined with an Unscented Kalman filter (UKF) data fusion module to eliminate outlier data points. Simulations indicate that the proposed scheme can accurately regenerate the user trajectory, even without the attacker’s physical intrusion into the building. This poses a new concern of surveillance risks in directional wireless communications, given their expected popularity in 5G and beyond.