本文以船载移动通信地球站天线跟踪系统的研制为背景，主要研究了圆锥扫描技术及 其在实际跟踪系统中的实现。论文首先比较了几种跟踪技术的优缺点，分析了圆锥扫描跟 踪的原理，仿真得到了天线接收的圆锥扫描信号幅度波形。对圆锥扫描技术相关参数如交 叉电平、调制指数，波束偏角万等参数进行了详细分析研究，介绍了一种简单的误差信号 解调的方法。研究了船载天线系统的总体方案设计，提出了基于GPS、电子罗盘和陀螺仪 的稳定跟踪系统实施方案，并进行了工程实现，包括硬件的选型以及软件的实现。本文还 对天线系统的相关问题，如方位、俯仰、横滚三轴控制方式、系统隔离度等进行了分析， 并基于方位系统进行了性能仿真。

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哈工大的一篇论文，使用改进后的卡尔曼滤波视觉及惯导融合算法，进行位置和姿态估计，用于航天器，但是无人机也能用，里面的思路可以借签。

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用IMU的数据进行机器人位置和姿态的估计，比如acc或者gyro积分每个sample怎么进行坐标变换，怎么由rawdata得到位置和姿态信息的计算细节等。 In recent years, microelectromechanical system (MEMS) inertial sensors (3D accelerometers and 3D gyroscopes) have become widely available due to their small size and low cost. Inertial sensor measurements are obtained at high sampling rates and can be integrated to obtain position and orientation information. These estimates are accurate on a short time scale, but suer from integration drift over longer time scales. To overcome this issue, inertial sensors are typically combined with additional sensors and models. In this tutorial we focus on the signal processing aspects of position and orientation estimation using inertial sensors. We discuss dierent modeling choices and a selected number of important algorithms. The algorithms include optimization-based smoothing and ltering as well as computationally cheaper extended Kalman lter and complementary lter implementations. The quality of their estimates is illustrated using both experimental and simulated data.