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Found 6 papers, 0 papers with code
High-Accuracy Absolute-Position-Aided Code Phase Tracking Based on RTK/INS Deep Integration in Challenging Static Scenarios
First, an absolute code phase is predicted from base station information, and integrated solution of the INS DR and real-time kinematic (RTK) results through an extended Kalman filter (EKF).
Supporting GNSS Baseband Using Smartphone IMU and Ultra-Tight Integration
A Doppler value is predicted based on an integrated extended Kalman filter (EKF) navigator where the pseudorange-state-based measurements of GNSS and INS are fused.
Multi-Signal Approaches for Repeated Sampling Schemes in Inertial Sensor Calibration
While different techniques are available to model and remove the deterministic errors, there has been considerable research over the past years with respect to modelling the stochastic errors which have complex structures.
Inertial Sensing Meets Artificial Intelligence: Opportunity or Challenge?
Recently, the emergence of chip-level inertial sensors has expanded the relevant applications from positioning, navigation, and mobile mapping to location-based services, unmanned systems, and transportation big data.
Deep Reinforcement Learning (DRL): Another Perspective for Unsupervised Wireless Localization
However, it is challenging to use low-cost IoT devices for robust unsupervised localization (i. e., localization without training data that have known location labels).
Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation
Compared to the related surveys, this paper has a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges.
Networking and Internet Architecture Signal Processing
