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This low-cost GNSS/IMU integrated navigation algorithm adopts an adaptive extended Kalman filter (EKF) algorithm based on machine learning, which adjusts the noise variance through the probability of hidden states.
The combination of GNSS and IMU can provide more accurate positioning and navigation information. GNSS is usually used to provide absolute position information, while IMU can provide information about device motion, including angular velocity and linear velocity. This information can help algorithms more accurately estimate the orientation and position of devices.
In this algorithm, by using machine learning techniques, the model can be trained to adaptively adjust the noise variance. This means that the model can dynamically adjust noise parameters based on changes in input data (such as vibration status, road condition status, satellite interference status, etc.), thereby improving the navigation accuracy of the algorithm.
This method requires first training the model to identify various states and establishing a noise variance database for each state. In the actual navigation process, the algorithm will adaptively adjust the noise variance based on the current state and data in the database.
Overall, this low-cost GNSS/IMU integrated navigation algorithm can provide more accurate and stable navigation information in different navigation environments by using machine learning technology. This is very useful for autonomous vehicles, drones, and other applications that require precise navigation.
This low-cost GNSS/IMU integrated navigation algorithm adopts an adaptive extended Kalman filter (EKF) algorithm based on machine learning, which adjusts the noise variance through the probability of hidden states.
The combination of GNSS and IMU can provide more accurate positioning and navigation information. GNSS is usually used to provide absolute position information, while IMU can provide information about device motion, including angular velocity and linear velocity. This information can help algorithms more accurately estimate the orientation and position of devices.
In this algorithm, by using machine learning techniques, the model can be trained to adaptively adjust the noise variance. This means that the model can dynamically adjust noise parameters based on changes in input data (such as vibration status, road condition status, satellite interference status, etc.), thereby improving the navigation accuracy of the algorithm.
This method requires first training the model to identify various states and establishing a noise variance database for each state. In the actual navigation process, the algorithm will adaptively adjust the noise variance based on the current state and data in the database.
Overall, this low-cost GNSS/IMU integrated navigation algorithm can provide more accurate and stable navigation information in different navigation environments by using machine learning technology. This is very useful for autonomous vehicles, drones, and other applications that require precise navigation.
