Self-alignment of full skewed RSINS: observability analysis and full-observable Kalman filter

Traditional orthogonal strapdown inertial navigation sys-tem （SINS） cannot achieve satisfactory self-alignment accuracy in the stationary base： taking more than 5 minutes and al the iner-tial sensors biases cannot get ful observability except the up-axis accelerometer. However, the ful skewed redundant SINS （RSINS） can not only enhance the reliability of the system, but also improve the accuracy of the system, such as the initial alignment. Firstly, the observability of the system state includes attitude errors and al the inertial sensors biases are analyzed with the global perspective method： any three gyroscopes and three accelerometers can be assembled into an independent subordinate SINS （sub-SINS）;the system state can be uniquely confirmed by the coupling connec-tions of al the sub-SINSs;the attitude errors and random constant biases of al the inertial sensors are observable. However, the ran-dom noises of the inertial sensors are not taken into account in the above analyzing process. Secondly, the ful-observable Kalman filter which can be applied to the actual RSINS containing random noises is established; the system state includes the position, ve-locity, attitude errors of al the sub-SINSs and the random constant biases of the redundant inertial sensors. At last, the initial self-alignment process of a typical four-redundancy ful skewed RSINS is simulated： the horizontal attitudes （pitch, rol ） errors and yaw error can be exactly evaluated within 80 s and 100 s respectively, while the random constant biases of gyroscopes and accelero-meters can be precisely evaluated within 120 s. For the ful skewed RSINS, the self-alignment accuracy is greatly improved, mean-while the self-alignment time is widely shortened.

Forecasting of Asian dust storm that occurred on May 10-13, 2011, using an ensemble-based data assimilation system

An ensemble-based assimilation system that used the MASINGAR ink-2 （Model of Aerosol Species IN the Global AtmospheRe Mark 2） dust forecasting model and satellite-derived aerosol optical thickness （AOT） data. processed in the JAXA （Japan Aerospace Exploration Agency） Satellite Monitoring for Environmental Studies （JASMES） system with MODIS （Moderate Resolution Imaging Spectroradiometer） observations. was used to quantify the impact of assimilation on forecasts of a severe Asian dust storm during May 10-13. 2011. The modeled bidirectional reflectance function and observed vegetation index employed in JASMES enable AOT retrievals in areas of high surface reflectance, making JASMES effective for dust forecasting and early warning by enabling assimilations in dust storm source regions. Forecasts both with and without assimilation were validated using PM^0 observations from China, Korea, and Japan in the TEMM WG1 dataset. Only the forecast with assimilation successfully captured the contrast between the core and tail of the dust storm by increasing the AOT around the core by 70-150% and decreasing it around the tail by 20-30% in the 18-h forecast. The forecast with assimilation improved the agreement with observed PMlo concentrations, but the effect was limited at downwind sites in Korea and Japan because of the lack of observational constraints for a mis-forecasted dust storm due to cloud.