Remote Sensing · 2018 · 153 citations · 79 references
ComputingAutonomous SystemsMechatronics EngineeringAutonomous VehiclesGlobal Navigation Satellite SystemsSensor FusionGlobal Positioning System IntegrationGnss Reference FramesMulti-constellation IntegrationReal-time Kinematic PositioningSensor Fusion For LocalizationAutonomous Vehicle ControlGlobal Navigation Satellite Systems (Gnss)Inertial Measurement Unit IntegrationInertial Measurement UnitsInertial NavigationInertial Navigation SystemsMobile MappingPositioning Accuracy
The integration of Global Positioning System (GPS) real-time kinematics (RTK) and an inertial navigation system (INS) has been widely used in many applications, such as mobile mapping and autonomous vehicle control. Such applications require high-accuracy position information. However, continuous and reliable high-accuracy positioning is still challenging for GPS/INS integration in urban environments because of the limited satellite visibility, increasing multipath, and frequent signal blockages. Recently, with the rapid deployment of multi-constellation Global Navigation Satellite System (multi-GNSS) and the great advances in low-cost micro-electro-mechanical-system (MEMS) inertial measurement units (IMUs), it is expected that the positioning performance could be improved significantly. In this contribution, the tightly-coupled single-frequency multi-GNSS RTK/MEMS-IMU integration is developed to provide precise and continuous positioning solutions in urban environments. The innovation-based outlier-resistant ambiguity resolution (AR) and Kalman filtering strategy are proposed specifically for the integrated system to resist the measurement outliers or poor-quality observations. A field vehicular experiment was conducted in Wuhan City to evaluate the performance of the proposed algorithm. Results indicate that it is feasible for the proposed algorithm to obtain high-accuracy positioning solutions in the presence of measurement outliers. Moreover, the tightly-coupled single-frequency multi-GNSS RTK/MEMS-IMU integration even outperforms the dual-frequency multi-GNSS RTK in terms of AR and positioning performance for short baselines in urban environments.
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Q-Gene: processing quantitative real-time RT–PCR data
Perikles Simon · Bioinformatics · 2003 · 861 citations · Full text
Biology of tendon injury: healing, modeling and remodeling.
Pankaj Sharma, Nicola Maffulli · PubMed · 2006 · 825 citations