<p>Polarization-based integrated navigation system (PINS) that combines the polarization sensor (PS) and the inertial navigation system (INS) has been widely recognized as an effective solution for acquiring attitude information of unmanned aerial vehicles (UAVs). However, based on the PINS hardware configuration, the accurate acquisition of UAV position information remains a challenge. In this article, we propose an improved PS/INS integrated navigation scheme by incorporating an embedded UAV dynamic model (UDM). Compared with existing PS/INS fusion methods, the presented PINS enables the optimal estimation of the UDM thrust coefficient error along with other system state elements, thus significantly improving the UDM accuracy. On this basis, the UDM and PS are used to fuse with the INS, which improves the estimation accuracy of both the UAV attitude and position. Furthermore, we employ an adaptive fusion strategy to detect the reliability of PS data. Therefore, once the UDM is corrected using reliable PS data, it can further fuse with the INS, thereby improving the environmental adaptability of the PINS. The simulation and flight experiment results verified the effectiveness of the proposed PS/INS/UDM integrated navigation scheme.</p>

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An enhanced polarization sensor/INS integrated navigation method with embedded UAV dynamics

  • Yu Bai,
  • Wenshuo Li,
  • Xin Liu,
  • Xuqing Fan,
  • Xiang Yu,
  • Lei Guo

摘要

Polarization-based integrated navigation system (PINS) that combines the polarization sensor (PS) and the inertial navigation system (INS) has been widely recognized as an effective solution for acquiring attitude information of unmanned aerial vehicles (UAVs). However, based on the PINS hardware configuration, the accurate acquisition of UAV position information remains a challenge. In this article, we propose an improved PS/INS integrated navigation scheme by incorporating an embedded UAV dynamic model (UDM). Compared with existing PS/INS fusion methods, the presented PINS enables the optimal estimation of the UDM thrust coefficient error along with other system state elements, thus significantly improving the UDM accuracy. On this basis, the UDM and PS are used to fuse with the INS, which improves the estimation accuracy of both the UAV attitude and position. Furthermore, we employ an adaptive fusion strategy to detect the reliability of PS data. Therefore, once the UDM is corrected using reliable PS data, it can further fuse with the INS, thereby improving the environmental adaptability of the PINS. The simulation and flight experiment results verified the effectiveness of the proposed PS/INS/UDM integrated navigation scheme.