Aiming at the problem of poor observability of measurement information in the loosely-coupled integration of the inertial navigation system （INS） and the wireless sensor network （WSN）, this paper presents a tightly-coupled integration based on the Kalman filter （KF）. When the WSN is available, the difference between the distances from the blind node（BN） to the reference nodes （RNs） measured by the INS and those measured by the WSN are used as measurement information for the KF due to its better observability and independence, which can effectively improve the accuracy of the KF. Simulations show that the proposed approach reduces the mean error of the position by about 50% compared with loosely-coupled integration, while the mean error of the velocity is a little higher than that of loosely-coupled integration.
Aiming to improve the maneuver performance of the strapdown inertial navigation attitude coning algorithm a new coning correction structure is constructed by adding a sample to the traditional compressed coning correction structure. According to the given definition of classical coning motion the residual coning correction error based on the new coning correction structure is derived. On the basis of the new structure the frequency Taylor series method is used for designing a coning correction structure coefficient and then a new coning algorithm is obtained.Two types of error models are defined for the coning algorithm performance evaluation under coning environments and maneuver environments respectively.Simulation results indicate that the maneuver accuracy of the new 4-sample coning algorithm is almost double that of the traditional compressed 4-sample coning algorithm. The new coning algorithm has an improved maneuver performance while maintaining coning performance compared to the traditional compressed coning algorithm.
In order to keep stable navigation accuracy when the blind node （BN） moves between two adjacent clusters, a distributed fusion method for the integration of the inertial navigation system （INS） and the wireless sensor network （WSN） based on H∞ filtering is proposed. Since the process and measurement noise in the integration system are bounded and their statistical characteristics are unknown, the H∞ filter is used to fuse the information measured from local estimators in the proposed method. Meanwhile, the filter can yield the optimal state estimate according to certain information fusion criteria. Simulation results show that compared with the federal Kalman solution, the proposed method can reduce the mean error of position by about 45% and the mean error of velocity by about 85 %.
为解决磁屏蔽筒制约原子自旋磁强计灵敏度的问题,通过改进多层磁屏蔽筒轴向系数公式获得磁屏蔽筒参数优化模型,并在仅改变一项参数而其他参数固定的条件下,依据优化模型,利用Matlab软件对各参数对轴向屏蔽系数的影响程度进行仿真.结果表明:随着最内层筒半径、筒长及径向层间距的增大,轴向屏蔽系数迅速减小;轴向间隙越大,则屏蔽系数越大.根据仿真结果及实际应用需要优化设计磁屏蔽筒参数,并利用Ansoft软件对优化筒和非优化筒的屏蔽效果进行仿真.结果表明,在外界磁场相同的情况下,未优化和经优化设计的磁屏蔽筒屏蔽能效分别约为152.1和158.6 d B.因此,通过参数优化模型可获得体积小、质量轻、成本低、屏蔽性能大的磁屏蔽筒.
