Online Estimation of ARW Coefficient of Fiber Optic Gyro

As a standard method for noise analysis of fiber optic gyro (FOG), Allan variance has too large offline computational burden and data storages to be applied to online estimation. To overcome the barriers, the state space model is firstly established for FOG. Then the Sage-husa adaptive Kalman filter (SHAKF) is introduced in this field. Through recursive calculation of measurement noise covariance matrix, SHAKF can avoid the storage of large amounts of history data. However, the precision and stability of this method are still the primary matters that needed to be addressed. Based on this point, a new online method for estimation of the coefficient of angular random walk is proposed. In the method, estimator of measurement noise is constructed by the recursive form of Allan variance at the shortest sampling time. Then the estimator is embedded into the SHAKF framework resulting in a new adaptive filter. The estimations of measurement noise variance and Kalman filter are independent of each other in this method. Therefore, it can address the problem of filtering divergence and precision degrading effectively. Test results of both digital simulation and experimental data of FOG verify the validity and feasibility of the proposed method

Are more traditional employees less innovative? Colleagues' and leaders' influences matter

We examined how the cultural value orientation of employees' traditionality influences their innovative behaviors in a group context. Our belief was that the behavioral influence of traditionality on individual innovation would be contingent upon group traditionality and transformational leadership, as well as the combined moderating effect of these two variables. Participants were 282 employees and their 36 group leaders in a large telecommunication corporation in China. Results revealed that although traditionalists were superior to nontraditionalists in terms of individual innovation, the latter group exhibited greater potential for innovative behaviors when their colleagues were less traditional and/or when they were led by a transformational leader. Moreover, traditionalists were found to be more innovative than nontraditionalists were when group traditionality was high and transformational leadership was low. Research limitations and implications of the findings are discussed.</jats:p