The on-chip integrated sensing ring, a fundamental component of integrated optical gyroscopes (IOGs), significantly influences its performance, including detection resolution, zero-bias stability, and angle random walk. Reducing the propagation loss in the sensing ring is one of the most effective ways to improve the gyroscope performance. Compared to other laborious loss reduction approaches, such as modifying fabrication processes, optimizing the waveguide geometry proves to be a more flexible and economical strategy and has become a crucial technique to build exceptional integrated gyroscopes. In this article, we design 90 degrees bent silicon nitride waveguides for ultralow-loss quasi-rectangular integrated sensing rings based on the covariance matrix adaptation evolution strategy (CMA-ES) algorithm. The CMA-ES algorithm optimizes the width and the trace of the bends in a global parameter space. The simulation results demonstrate significant loss reduction up to over three orders of magnitude in the spectrum range of 1500-1600 nm for 90 degrees bent waveguides across varying radii, dimensions, and structural configurations. In addition, an ultralow loss of less than 2 x 10(-5) dB/90 degrees at single wavelength was achieved in a bend of 300 mu m effective radius in our optimization, with huge potential awaiting further exploration. The experimental results demonstrate good agreement with the simulated data, confirming the effectiveness of this optimization method and its clear engineering significance. This research proves that the CMA-ES algorithm can effectively optimize the design of on-chip integrated sensing rings, reduce their loss, and thus improve the performance of IOGs, exhibiting high superiority and practicality.
Accurate and stable positioning is significant for vehicle navigation systems, especially in complex urban environments. However, urban canyons and dynamic interference make vehicle sensors prone to disturbance, leading to vehicle positioning errors and even failures. To address these issues, an adaptive loosely coupled IMU/GNSS/LiDAR integrated navigation system based on factor graph optimization with sensor weight optimization and fault detection is proposed. First, the factor nodes and system framework are constructed based on error models of sensors, and the optimization method principle is derived. Second, the interactive multiple-model algorithm based on factor graph optimization (IMMFGO) is utilized to calculate and adjust sensor weights for global optimization, which will reduce the impact of disturbed sensors. Finally, a multi-stage fault detection, isolation, and recovery (MSFDIR) strategy is implemented based on the IMMFGO results and IMU pre-integration measurements, which can detect significant sensor faults and optimize the system structure. Vehicle experiments show that our IMMFGO method generally obtains better performance in positioning accuracy by 23.7% compared to adaptive factor graph optimization (AFGO) methods, and the MSFDIR strategy possesses the capability of fault sensor detection, which provides an essential reference for multi-source vehicle navigation systems in urban canyons.
当前基于视觉二维码的定位定姿方法依赖连续清晰的图像,当图像出现丢失或模糊现象时,性能难以保证.针对这一问题,提出了一种基于视觉二维码/惯性融合的室内高精度定位定姿方法,具有成本低、灵活和便携的特点.所提方法以捷联惯导算法为核心,通过拍摄预先布设且坐标已知的二维码(ArUco码)获得高精度的位置修正信息,并使用扩展卡尔曼滤波进行信息紧耦合.采用反向平滑算法融合二维码观测信息提升定位定姿精度,结合惯性的位姿推算能够有效地应对摄像头短暂遮挡或图像模糊等问题,提升系统的鲁棒性.实验结果表明,视觉观测出现中断时,所提方法也能够连续可靠地实现 3.2 cm(RMS)的定位精度,并达到与参考系统的定姿差异优于 0.1 °(RMS).
为获取高精度的海底地形地貌,提出了基于空间域最小化目标函数的海底地形重力反演方法.采用随机子抽样交叉验证法选取最优超参数来保证反演精度,设计了高精度空间域最小化目标函数海底地形反演流程.采用理论模型计算比较了不同超参数取值策略对反演结果精度的影响,基于随机子抽样交叉验证法选取最优超参数取值策略的复杂海底地形反演计算平均相对误差为 0.6456%,基于超参数固定值取值策略的复杂地形反演计算平均相对误差为 17.2603%.最后,采用实测重力数据对南海某海域实际海底地形进行反演,反演结果平均相对误差小于 5%,进一步验证了所提方法可有效获取高精度海底地形地貌.
为了提高卫星钟差短期预报精度,有效利用钟差数据之间的依赖关系,实现深层隐含特征提取与并行化计算,提出了一种序列到序列的基于优化自注意力机制的神经网络钟差预报模型(Informer).首先,对自注意力机制进行概率稀疏性优化,合理分配权重,给出详细的稀疏优化方法,降低模型的训练与预报时长;其次,引入麻雀优化算法进行超参数优化,得到神经网络的最优超参数;然后,给出Informer模型钟差预报的具体步骤;最后,将Informer模型与已有模型进行实验对比.实验结果表明:在计算效率方面,LSTM模型、Transformer模型和Informer模型在输入长度低于 336个历元时运行时间大致相同;在预报时间方面,Informer模型优势明显;利用 12 h的数据建模,与QP、GM以及LSTM模型相比,Informer模型 3 h的预报精度分别提高了 66.83%、79.78%、71.11%,6 h的预报精度分别提高了 50.50%、70.84%、53.54%,12 h的预报精度分别提高了 37.91%、60.12%和 15.74%,验证了Informer模型在钟差短期预报中具有较高的准确性和稳定性.
