How does a Kalman filter improve the accuracy and stability of autosteering systems?

A Kalman filter improves the accuracy and stability of autosteering systems by optimally combining noisy measurements from multiple sensors and a mathematical model of the vehicle's dynamics to estimate the vehicle's state (position, velocity, orientation) more accurately than any single sensor could provide. Autosteering systems rely on sensors such as GPS, inertial measurement units (IMUs), and wheel angle sensors to determine the vehicle's position and orientation. However, each of these sensors has its own sources of error and noise. GPS signals can be affected by atmospheric conditions and obstructions, IMUs can drift over time, and wheel angle sensors can be affected by tire slippage. A Kalman filter is an algorithm that uses a mathematical model of the vehicle's motion to predict the vehicle's state. It then compares this prediction to the measurements from the sensors. Based on the estimated uncertainty of the model and the sensors, it weighs the prediction and the measurements to produce an optimal estimate of the vehicle's state. This process is repeated continuously, providing a smooth and accurate estimate of the vehicle's position and orientation over time. By fusing data from multiple sensors, the Kalman filter can reduce the effects of individual sensor errors and noise. It can also estimate parameters that are not directly measured by sensors, such as vehicle sideslip angle. This improved accuracy and stability leads to better steering performance, reduced path deviations, and smoother operation of the autosteering system. For example, in a tractor autosteering system, a Kalman filter can combine GPS data with IMU data to provide a more accurate and stable estimate of the tractor's position, even when GPS signals are temporarily blocked or degraded. This allows the tractor to maintain a straight path with minimal deviations, improving planting accuracy and reducing overlap.