Geo-Pointing of On-Board Sensors for Static/Low/High Dynamic Platforms

There are many new applications requiring high-accuracy platform attitude (1 milliradian / 0.05 degrees()) for geo-pointing or stabilization of sensors and devices mounted on static, low or high-dynamic manned and unmanned platforms. Typical onboard devices requiring geo-pointing or stabilization include antennas, cameras, LiDAR sensors, and various weapons, which are mounted on platforms such as aircraft, aerostats, boats, ground vehicles, tanks, and robots. Traditional approaches for providing this geo-pointing information are based on either inertial technology or multi-antenna GPS configurations. Considering platform dynamics and physical size, each approach has its limitations. For inertial solutions, there are requirements for platform motion and filter convergence time. Lower end Inertial Measurement Units (IMU) require motion to operate while high-end IMU's require long tuning periods when stationary. For multi-antenna GPS based systems, there is an antenna baseline separation requirement such that the approach cannot be used if the physical size of the platform is not large enough to support the antenna separation. Thus, a geo-pointing system is tradeoff between the following three factors: (1) platform size and the related antenna separation, (2) platform dynamics, and (3) IMU quality. In this paper, we investigate several real-world applications in which a straight forward implementation of either the standard inertial, or multi-antenna GPS approaches to solving the problem is not possible due to constraints imposed by one or more of the factors listed. We develop several algorithms designed to address these issues and present results of tests conducted using a car as the test platform. The tests utilize several inertial systems, each integrated with an IMU of differing performance characteristics ( Ring Laser Gyro, Fiber Optic Gyro and Quarts MEMS) as well as a dual-GPS antenna system. We show the performance of the modified coarse alignment, modified fine alignment algorithms as well as results for the hybrid solution.