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The position and orientation system (POS) is a key technology which provides motion compensation information for an imaging sensor in airborne remote sensing. But the POS accuracy decreases with time as gyroscope bias excursion as time flows, in-flight calibration is the most convenient way to solve this problem to the users, but the existing methods are mostly based on Kalman filter, the effect of them to estimate the bias error isn' t reliable enough as affected severely by global position system measurement error and model error. A novel airborne POS in-flight calibration approach based on quaternion optimization is devised in this paper, which adds the factor of attitude error promulgation by gyroscope bias error into the algorithm, and gyroscope bias error is recognized by parse analysis. Simulations and flight experiments demonstrated that, with the proposed algorithm, the gyroscope bias error can be recognized in acceptable precision. [ABSTRACT FROM PUBLISHER]

This paper developed a measurement-while-drilling instrument based on predigested inertial measurement unit (PIMU) and presented a PIMU design with a rotational modulation device, thus improving precision of inertial sensors. Then, this paper proposed an integral surveying method based on inertial navigation system and dead reckoning, measuring motion parameters characterized by long-term high accuracy. The semi-physical simulation was conducted under the laboratory conditions. The results indicate that, the suggested methodology dramatically improves measuring accuracy of attitude angles (azimuth, pitch, and roll) and position. [ABSTRACT FROM PUBLISHER]

Position and Orientation System (POS) is a key technology that provides motion compensation information for imaging sensor in airborne remote sensing. In-flight alignment (IFA) is an important alignment method that can improve reaction speed and accuracy of measurement for airborne POS. The traditional IFA methods based on the Kalman filter and the adaptive extended Kalman filter (AEKF) are severely affected by flight maneuver; severe maneuver may result in alignment accuracy degeneration or failure. On the basis of optimization-based alignment method for static base alignment, a novel IFA method based on quaternion optimization is devised in this paper, aiming to boost up the robustness and the accuracy of IFA, which extends the range of the application of optimization-based alignment method from static base alignment to IFA. The proposed algorithm has the advantage that it can avoid the affection from severe maneuver. The real-time implementation is given. Flight experiment demonstrates that, compared with the IFA method based on AEKF, the proposed algorithm is better in converging speed and robusticity with the same alignment accuracy. [ABSTRACT FROM PUBLISHER]

The Position and Orientation measurement System (POS) is a dedicated Strapdown Inertial Navigation System (SINS)/Global Positioning System (GPS) integrated system for airborne remote sensing. In-flight alignment (IFA) is an effective way to improve the accuracy and speed of initial alignment for an airborne POS. During IFA, the GPS provides the position and velocity references for the SINS, so the alignment accuracy will be degraded by unstable GPS measurements. To improve the alignment accuracy under unstable GPS measurement, an adaptive filtering algorithm of an extended Kalman filter (EKF) combined with innovation-based adaptive estimation is proposed, which introduces the calculated innovation covariance into the computation of the filter gain matrix directly. Then, this innovation adaptive EKF algorithm is used for the IFA of the POS with a large initial heading error. Moreover, it is optimized by blocked matrix multiplication to reduce the computational burden and improve the real-time performance. To validate the proposed algorithm, the car-mounted IFA experiment is carried out for the prototype of the airborne POS (TX-D10) under a turning maneuver, taking Applanix's POS/AV510 as a reference and changing the GPS measurement artificially. The experiment results demonstrate that the proposed algorithm can reach a better alignment accuracy than the EKF under unknown GPS measurement noises. [ABSTRACT FROM PUBLISHER]