IMU rotation of an inertial navigation system (INS) can bound the free propagation of the INS error introduced by the drifts of inertial sensors. The rotation scheme of the IMU will directly affect the accuracy, structure and costs of the system. A reasonable rotation scheme should remove most of the system errors arising from the drifts of the inertial sensors, and at the same time, should not introduce other additional errors. First, this paper discusses the design and analysis approach of the rotation scheme based on the error propagation equations of the INS. Then, a conventional 8-position rotation scheme is analyzed for the applications of the optical gyro INS, and its drawbacks are discussed in detail. Following these, an improved 8-position rotation scheme and a novel 16-position rotation scheme are proposed for the optical gyro INS, and their merits are also discussed. Simulation results have shown that the 16-position rotation scheme, which can compensate not only the drifts but also the scale factor errors and the misalignment errors of the inertial sensors, is the best rotation scheme and can be used as a practical solution to compensate the drifts of the inertial sensors in the rotational INS.

A dual-rate Kalman Filter (DRKF) has been developed to integrate the time-differenced GPS carrier phases and the GPS pseudoranges with INS measurements. The time-differenced GPS carrier phases, which have low noise and millimeter measurement precision, are integrated with INS measurements using a Kalman Filter with high update rates to improve the performance of the integrated system. Since the time-differenced GPS carrier phases are only relative measurements, when integrated with INS, the position error of the integrated system will accumulate over time. Therefore, the GPS pseudoranges are also incorporated into the integrated system using a Kalman Filter with a low update rate to control the accumulation of system errors. Experimental tests have shown that this design, compared to a conventional design using a single Kalman Filter, reduces the coasting error by two-thirds for a medium coasting time of 30 s, and the position, velocity, and attitude errors by at least one-half for a 45-min field navigation experiment.

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Modern attitude and heading reference systems (AHRS) generally use Kalman filters to integrate gyros with some other augmenting sensors, such as accelerometers and magnetometers, to provide a long term stable orientation solution. The construction of the Kalman filter for the AHRS is flexible, while the general options are the methods based on quaternion, Euler angles, or Euler angle errors. But the quaternion and Euler angle based methods need to model system angular motions, and, meanwhile, all these three methods suffer from nonlinear problems which will increase the system complexities and the computational difficulties. This paper proposes a novel implementation method for the AHRS integrating IMU and magnetometer sensors. In the proposed method, the Kalman filtering is implemented to use the Euler angle errors to express the local level frame (lframe) errors, rather than express the body frame (bframe) errors as the customary methods do. A linear system error model based on the Euler angles errors expressing thelframe errors for the AHRS has been developed and the corresponding system observation model has been derived. This proposed method for AHRS does not need to model system angular motions and also avoids the nonlinear problem which is inherent in the commonly used methods. The experimental results show that the proposed method is a promising alternative for the AHRS.

This paper proposes a novel mechanism for the initial alignment of low-cost INS aided by GPS. For low-cost INS, the initial alignment is still a challenging issue because of the high noises from low-cost inertial sensors. In this paper, a two-stage Kalman Filtering mechanism is proposed for the initial alignment of low-cost INS. The first stage is designed for the coarse alignment. To solve the problems encountered by the general coarse alignment approach, an INS error dynamic accounting for unknown initial heading error is developed, and the corresponding observation equation, taking into account the unknown heading error, is also developed. The second stage is designed for the fine alignment, where the classical INS error dynamics based on small attitude error is used. Experimental results indicate that the proposed alignment approach can complete the initial alignment more quickly and more accurately compared with the conventional approach. K E Y W O R D S 1. Low-Cost INS. 2. Initial Alignment. 3. GPS. 4. Land Vehicle.