Description: 硕士学位论文捷联惯性系统初始对准研究
惯导系统的初始对准是影响系统使用性能的关键技术之一,对准的精度与速度直接关系到惯性统的精度与启动特性。对卡尔曼滤波及其在初始对准中的应用进行了研究。首先介绍了卡尔曼滤波理论的应用背景,然后推导了离散卡尔曼滤波方程,并对连续系统的状态方程进行离散化提出了适用于舰载捷联惯性系统的动基座对准的2通道10个状态变量和2通道12个状态变量的系统动态模型,并建了相应的速度匹配和位置匹配量测模型。
-master's degree thesis SINS initial alignment study INS initial alignment is affecting system performance one of the key technologies, the alignment precision and speed is directly related to the precision inertial reunification with the startup characteristics. The Kalman filter and the initial alignment of the study. First introduced the theory of Kalman filtering background, and then reasoned a discrete Kalman filter equations, also for the state of discrete equations apply to the carrier SINS System Moving Base Alignment of two channels 10 state variables and two 12-channel system state variables dynamic model and built a corresponding speed matching and position matching measurement model. Platform: |
Size: 8600889 |
Author:王琴 |
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Description: 硕士学位论文捷联惯性系统初始对准研究
惯导系统的初始对准是影响系统使用性能的关键技术之一,对准的精度与速度直接关系到惯性统的精度与启动特性。对卡尔曼滤波及其在初始对准中的应用进行了研究。首先介绍了卡尔曼滤波理论的应用背景,然后推导了离散卡尔曼滤波方程,并对连续系统的状态方程进行离散化提出了适用于舰载捷联惯性系统的动基座对准的2通道10个状态变量和2通道12个状态变量的系统动态模型,并建了相应的速度匹配和位置匹配量测模型。
-master's degree thesis SINS initial alignment study INS initial alignment is affecting system performance one of the key technologies, the alignment precision and speed is directly related to the precision inertial reunification with the startup characteristics. The Kalman filter and the initial alignment of the study. First introduced the theory of Kalman filtering background, and then reasoned a discrete Kalman filter equations, also for the state of discrete equations apply to the carrier SINS System Moving Base Alignment of two channels 10 state variables and two 12-channel system state variables dynamic model and built a corresponding speed matching and position matching measurement model. Platform: |
Size: 8600576 |
Author:王琴 |
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Description: The open source project DSP_INS is an inertial navigation system on the TI DSP
platform TMS320F28335 with MEMS inertial sensors and commercial GPS module. Inertial
sensors are actually used only for estimating attitude, heading and velocity.
Position information totally comes from GPS (see Q2 in FAQ part). Currently the
MEMS IMU (Inertial Measurement Unit) ADIS16355 and GPS module u-blox LEA-5H
are supported. DSP_INS also reads magnetic field data from HONEYWELL HMC1051/1052.
You can also use the latest IMU module ADIS16405, which also contains magnetic field
sensors.
There is a 10-order extended Kalman filter in DSP_INS, which takes angular velocity,
acceleration, magnetic field, and GPS velocity as inputs. It provides outputs
including angular velocity, acceleration, attitude, heading, velocity, and position
(from GPS) every 10 milliseconds. DSP_INS is proved to be effective and reliable in
several robot and UAV projects. Platform: |
Size: 1682432 |
Author:snthej |
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Description: 针对小型无人机在无卫星导航信号条件下的导航问题, 结合光流及地标定位设计了使用摄像头、惯性测量器件、超声测距仪等传感器融合的无人机室内导航方法. 文章使用补偿角速率的光流微分法计算帧间像素点小位移, 并用前后误差算法提取精度较高的点, 避免像素点跟踪错误, 提高了光流测速的精度 对得到的光流场用均值漂移算法进行寻优, 得到光流场直方图峰值, 以此计算光流速度. 本文提出了无累积误差的连续地标定位算法, 实时测量无人机位置. 通过多速率卡尔曼滤波器对观测周期不一致的位置、速度信息进行最优估计. 在搭建的八旋翼无人机平台上试验, 将位置与速度测量结果分别与激光和PX4FLOW数据对比, 结果表明该导航方法可以有效抑制定位跳变与光流测量噪声误差, 给出精确的位置与速度估计.
-Problems for the Navigation satellite navigation signals in the absence of conditions, and in conjunction with optical flow landmark UAV design indoor navigation positioning method using the camera, inertial measurement device, an ultrasonic range finder sensor fusion article using the compensation angle computing optical flow rate of the inter-pixel differentiation small displacements, and extracted with high precision before and after the point of error algorithm, to avoid the tracking error pixel, improve the accuracy of optical flow speed optical flow field obtained for the mean shift algorithm optimization, the histogram peak optical flow, in order to calculate the optical flow velocity. in this paper, the landmark location algorithm continuously accumulated error is measured in real time the position of the UAV by multirate Kalman filter observation period inconsistent positions , optimal estimation speed information. tested on eight rotor UAV platform structures, the position a Platform: |
Size: 882688 |
Author:lou tayzan |
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Description: In this assignment you will study an inertial navigation system (INS) constructed using sensor fusion by a Kalman filter. The start code provides you with a working system with an inertial measurement unit (IMU, here accelerometer+gyro) and GNSS (GPS). Platform: |
Size: 195680 |
Author:ervaskes.pcet@gmail.com |
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