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This study designs a trigger to determine the number of layers of a multi-layer adaptive Kalman filter and applies it to optoelectronic infrared payload system vision. This feature reduces the number of mechanically stabilized motors, equipment weight, CPU resources, and power for an electro-optical infrared payload system. The goal is to reduce the traditional use of multiple gyroscopes to perform calibration measurements on different gimbal frames by this design. In this study, mathematical modeling was carried out for the three-axis, three-frame camera stabilizer system, and the system foundation without motor and gimbal frame was established to achieve aperture-type camera mode. The exposure of the drone’s payload structure outside the aircraft can be reduced. This study provides the adaptive Kalman filter with the offset parameters of the camera image Minimum Output Sum of Squared Error and the three-axis degrees of freedom vector and angle data on the gyroscope. By using the image processing unit, the offset was corrected at each frame per second. The experimental results show that under the same hardware, failure limit and camera field of view constraints. The processing time by this method was compared to the traditional frame correction and full image stabilization methods. The results show that the proposed method can shorten 6 microseconds under the traditional method and can be used to provide lower power consumption, lower image delay, and a larger viewing angle range.

Analysis and Design Multiple Layer Adaptive Kalman Filter Applied to Electro-Optical Infrared Payload Vision System