Abstract
Positioning accuracy and power consumption are essential performance indicators of integrated navigation and positioning chips. This paper proposes a single-frequency GNSS/MEMS-IMU/odometer real-time high-precision integrated navigation algorithm with dynamic power adaptive adjustment capability in complex environments. It is implemented in a multi-sensor fusion navigation SiP (system in package) chip. The simplified INS algorithm and the simplified Kalman filter algorithm are adopted to reduce the computation load, and the strategy of adaptively adjusting the data rate and selecting the observation information for measurement update in different scenes and motion modes is combined to realize high-precision positioning and low power consumption in complex scenes. The performance of the algorithm is verified by real-time vehicle experiments in a variety of complex urban environments. The results show that the RMS statistical value of the overall positioning error in the entire road section is 0.312 m, and the overall average power consumption is 141 mW, which meets the requirements of real-time integrated navigation for high-precision positioning and low power consumption. It supports single-frequency GNSS/MEMS-IMU/odometer integrated navigation SiP chip in real-time, high-precision, low-power, and small-volume applications.
Highlights
The global navigation satellite system has the advantages of globalization, all-weather, and high precision, which are widely used in land vehicle navigation systems [1]
This paper proposes a single-frequency GNSS/mechanical systems (MEMS)-inertial measurement unit (IMU)/odometer real-time highprecision simplified integrated navigation algorithm with dynamic power adaptive adjustment capability in complex environments
It is implemented in a multi-sensor fusion navigation SiP chip
Summary
The global navigation satellite system has the advantages of globalization, all-weather, and high precision, which are widely used in land vehicle navigation systems [1]. Environmental conditions may cause GNSS signal loss or attenuation and degrade the navigation accuracy [2]. The inertial navigation system does not depend on the external environment and can autonomously provide the position, speed, and attitude information of moving objects. It has good dynamic performance and high navigation accuracy in a short time. Due to errors such as bias, drift, and noise of the inertial measurement unit (IMU), the navigation errors will accumulate over time [3,4,5]. GNSS and INS have strong complementarity with each other in many aspects such as error characteristics
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