A novel temperature-compensation method based on correlation analysis for multi-FOG INS

Aimed at improving the bias stability of Fiber-Optic Gyroscope (FOG)-based inertial navigation systems in environments of various ambient temperatures, a novel temperature-compensation method based on a correlation analysis of the same batch of FOGs is proposed. The empirical mode decomposition method was employed to filter the high-frequency noises of the FOGs. Then, the correlation information of the multiple FOGs was used to analyze the feasibility of the method. Eventually, the same residual error of the FOGs was compensated via the simple piecewise linear models. The experimental results indicate that excellent compensation effects for both high- and low-accuracy FOGs are achieved using the proposed method. Specifically, the accuracies of high-accuracy FOGs are improved by approximately 33.9%, 20%, and 31.2%, while those of low-accuracy FOGs are improved by approximately 39.1%, 20.8%, and 26.1%. The method exhibits the merits of simplicity, validity, and stability, and thus is expected to be widely used in engineering applications.     

扰动线运动对惯性系粗对准影响分析及优化方法

针对扰动线运动对惯性系粗对准精度影响较大的问题,通过分析惯性体坐标系下重力加速度积分矢量的物理含义,给出了受扰动线运动影响的惯性系粗对准的误差分析。并在此基础上提出了基于位移积分矢量构造定姿矢量矩阵的抗扰动粗对准算法,利用对速度积分矢量进一步积分得到的3个时间点的位移积分矢量构建姿态解算矩阵,抑制扰动线运动对粗对准精度的影响。仿真结果表明,该算法可有效减小扰动线运动影响下的对准结果振动幅值,能够快速收敛到满足精对准使用的精度范围,从而提高武器装备环境适应性。     

Study of aerodynamic and inertial forces of a experimental and numerical methods

The force-generation mechanism of a dovelike flapping-wing micro air vehicle was studied by numerical simulation and experiment. To obtain the real deformation pattern of the flapping wing, the digital image correlation technology was used to measure the dynamic deformation of the wing. The dynamic deformation data were subsequently interpolated and embedded into the CFD solver to account for the aeroelastic effects. The dynamic deformation data were further used to calculate the inertial forces...     

基于改进的卷积神经网络的步态识别

为了提高惯性传感器采集到的序列数据中步态识别的准确率，建立了一个激励层改进的卷积神经网络(CNN)模型。针对三轴加速度传感器对运动太过敏感导致步态周期划分不准确的问题，采用加速度传感器与弯曲度传感器组合获取人体运动信息。将CNN模型中激励层的线性整流函数（ReLU）改进为带泄露线性整流函数(Leaky ReLU)，以解决遇到卷积输出数据小于0时神经元被抑制的问题，进而达到提高步态识别准确率的目的。实验结果表明：激励层优化的CNN模型在行走、上下楼和上下坡五种步态模式下识别率达到了95.79%，与未采用弯曲度传感器的改进CNN模型和未进行激励层改进的CNN模型相比，步态识别率有所提高。     

Mahalanobis distance-based fading cubature Kalman filter with augmented mechanism for hypersonic vehicle INS/CNS autonomous integration

Inertial Navigation System/Celestial Navigation System(INS/CNS) integration, especially for the tightly-coupled mode, provides a promising autonomous tactics for Hypersonic Vehicle(HV) in military demands. However, INS/CNS integration is a challenging research task due to its special characteristics such as strong nonlinearity, non-additive noise and dynamic complexity.This paper presents a novel nonlinear filtering method for INS/CNS integration by adopting the emerging Cubature Kalman Filter(C...     

金刚石氮—空位色心惯性测量技术发展与展望

金刚石内嵌负电荷氮—空位(NV-)色心,因其结构性质稳定、常压室温下自旋弛豫时间长、易于操控和检测等特点,已广泛应用于磁场、电场、温度等物理量的测量.基于金刚石NV-色心的惯性测量技术是基于量子效应的原子自旋惯性测量领域的新兴方向之一.首先介绍了金刚石NV-色心的结构与性质,其次阐述了量子体系几何相的基本原理以及NV-...     

车载惯导系统抗扰动自对准方法对比研究

自对准技术是陆基机动平台及装备用惯导系统的一项关键技术,其核心在于惯导系统如何在风扰、发动机振动、人员走动等各种干扰下实现快速、高精度对准.针对目前常用的惯性系解析自对准、Kalman滤波估计自对准和惯性系奇异值分解(SVD)自对准等三种方法进行了研究和对比分析,根据三种对准方法各自的实现原理,提出了一种有针对性的抗扰动优化方法,并通过了数学仿真分析和车载试验验证.结果 表明,在车载使用环境下,后两种方法都能获得较为准确的姿态信息,惯性系SVD方法可以达到与Kalman滤波方法相当的对准精度和收敛速度,且无需先验信息即可获得最优解,表现出了较好的工程应用优势.     

扰动线运动对惯性系粗对准影响分析及优化方法

针对扰动线运动对惯性系粗对准精度影响较大的问题，通过分析惯性体坐标系下重力加速度积分矢量的物理含义，给出了受扰动线运动影响的惯性系粗对准的误差分析。并在此基础上提出了基于位移积分矢量构造定姿矢量矩阵的抗扰动粗对准算法，利用对速度积分矢量进一步积分得到的3个时间点的位移积分矢量构建姿态解算矩阵，抑制扰动线运动对粗对准精度的影响。仿真结果表明，该算法可有效减小扰动线运动影响下的对准结果振动幅值，能够快速收敛到满足精对准使用的精度范围，从而提高武器装备环境适应性。