Kalman Filter Tracking Algorithm Simulation Based on Angle Expansion

In the position tracking of a uniform linear moving target affected by noise, when the extended Kalman filter adopts distance as the correction amount, and the performance is greatly affected by the noise. The angle variable is used as the second-dimensional variable to form a gain matrix, so that distance and the angle work together to track the target position. By introducing the current position angle observations that are away from the sensor into the observation matrix Z. and introducing the horizontal and vertical angle change ratio under the action of the prior value into Jacobian matrix H; the second dimension is expanded. Simulation results and analysis show that the proposed method has a smaller position tracking deviation and a better overall performance under certain conditions in comparison with Kalman filter method with only distance correction, the new method has a smaller position tracking deviation and a better overall performance under certain conditions.