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.     

闪烁输入下比例导引系统无量纲化研究

研究了目标闪烁输入下比例导引系统的无量纲化,得到了在闪烁输入下比例导引系统的脱靶量与弹目相对运动速度Vr无关,与噪声功率谱平方根Ka和系统动力学无阻尼自振频率的平方根(ω_n)~(1-2)成正比的结论,这些结论在工程实用中具有重要意义。     

基于H∞滤波的智能车辆多传感器组合导航研究

为适应自主驾驶智能车辆的高精度、高频率及高可靠性与鲁棒性的导航要求,对智能车辆的多传感器组合导航进行了研究。提出了一种基于H∞滤波的双向光电测速仪和CP-DGPS共同辅助SINS的智能车辆冗余组合导航方法。详细推导并建立了组合系统的滤波模型。仿真结果表明,该组合导航系统具有100Hz的高频输出、厘米级的导航精度以及良好的鲁棒性能与可靠性。即使系统特性发生了显著的变化,组合系统仍能为智能车辆提供可靠的导航信息。     

New rapid transfer alignment method for SINS of airborne weapon systems

Transfer alignment is an effective alignment method for the strapdown inertial navigation system （SINS） of airborne weapon systems. The traditional transfer alignment methods for large misalignment angles alignment use nonlinear transfer align- ment models and incorporate nonlinear filtering. A rapid transfer alignment method with linear models and linear filtering for ar- bitrary misalignment angles is presented. Through the attitude quaternion decomposition, the purpose of transfer alignment is converted to estimate a constant quaternion. Employing special manipulations on measurement equation, velocity and attitude linear measurement equations are derived. Then the linear trans- fer alignment model for arbitrary misalignment angles is built. An adaptive Kalman filter is developed to handle modeling errors of the measurement noise statistics. Simulation results show feasibili- ty and effectiveness of the proposed method, which provides an alternative rapid transfer alignment method for airborne weapons.     

某型飞机导航软件校正部分的国产化

应用无线电近导系统测量的距离和方位校正惯导是一种新的综合导航方式。根据某型飞机无线电近导校正惯导的有关结论 ,给出了适用于国内惯导系统与无线电近导综合导航的新算法 ,其数学模型已被将要国产化的惯导系统所采用 ,此算法对塔康与惯导组合也具有一定的参考价值。     

星座双频测距测速自主定位与守时

传统星座自主导航主要以星间相对测距为主. 该技术定位精度有限,同时缺乏有效的手段克服星座的整体旋转. 该文在测距模型的基础上,将测速信息、传播延时和时钟误差纳入模型中,分析了电离层对测距的影响,推导了星间测距位置与时间的解耦模型. 提出利用GPS辅助测量来固定星座的位置基准和时间基准. 通过Matlab仿真实验表明：测速信息的引入能够进一步提高卫星的位置估计精度,同时GPS辅助测量可有效克服星座的整体旋转.     

应用联邦自适应UKF的卫星多传感器数据融合

在卫星自主导航系统中,一方面,系统状态模型存在难以准确建模的问题,要求信息融合算法具有一定的自适应性;另一方面,系统的量测模型通常具有较强的非线性,又要求信息融合算法在强非线性下保持较高的精度和鲁棒性. 针对以上两个问题,本文提出了基于星敏感器、红外地平仪、磁强计、雷达高度计、紫外敏感器的多信息联邦自适应UKF组合导航方案,该方案将多个导航传感器提供的信息在联邦滤波器里融合,并采用自适应UKF算法构建联邦滤波器的子滤波器. 采用这种方案,可有效组织并充分利用导航传感器提供的导航信息,并且系统模型具有一定的自适应性. 数字仿真结果表明,与传统的联邦卡尔曼滤波方法相比,该方法更适合于非线性较强、系统模型参数不准确的场合,有效提高了导航精度.     

基于异质传感器信息融合的移动机器人SLAM研究

针对采用单一传感器在移动机器人同步定位与构图(SLAM)中存在定位精度低、构图不完整等问题,提出一种基于Kinect视觉传感器和激光传感器信息融合的SLAM算法。首先将Kinect传感器获取的深度图像经过坐标系转换得到三维点云、通过限制垂直方向滤波器过滤三维点云的高度信息、再将剩余三维点云投影到水平面并提取边界点云信息转化为激光扫描数据;然后与激光传感器的扫描数据进行数据级的信息融合;最后输出统一数据实现移动机器人的构图及自主导航。实验结果表明,该方法能够准确的检测小的及特征复杂的障碍物,能够构建更精确、更完整的环境地图,且更好地完成移动机器人自主导航任务。     

某型飞机导航软件参数处理的研究

在分析某型飞机导航软件时发现，该软件在计算位置，航向等参数校正量时要引入相应的系数，根据原始资料中的控制结构图，对位置校正量进行了详细的研究，结果表明：该软件对位置校正量采用了惯性环节进行平滑，其方法能够有效的抑制校正过程出现的干扰。     

Software framework for off-road autonomous robot navigation system

This paper presents a software framework for offroad autonomous robot navigation system. With the requirements of accurate terrain perception and instantaneous obstacles detection, one navigation software framework was advanced based on the principles of “three layer architecture” of intelligence system. Utilized the technologies of distributed system, machine learning and multiple sensor fusion, individual functional module was discussed. This paper aims to provide a framework reference for autonomous robot navigation system design.