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SINS-MatlabImplement
- 本程序是关于四元数法捷联惯导解算算法的matlab程序组合包，其中包括滤波初始对准仿真，罗经法初始对准仿真，捷联惯导解算仿真，组合卡尔曼滤波等演示程序及其必需的参数矩阵转换程序，程序算法皆是本人通过大量阅读捷联惯导经典论文书籍编写的，经过调试已经通过，所得圆锥误差，划桨误差与秦永元所编惯性导航一书相符，可靠性较高。适合惯导学习者参考改进使用 -This procedure is the law about quaternion SINS algorithm matlab solver co
ExampleCode_iSensor_ADIS16405
- 在keil c开发环境下，采用C语言编写的对惯性测量单元（IMU+磁力计）ADIS16405的测试程序，希望能有所帮助-In keil c development environment, written using C language on the inertial measurement unit (IMU+ magnetometer) ADIS16405 testing procedures, hoping to help
chuanyong_tuoluo_wuchamoxing
- 在对船用陀螺漂移数据建立时间序列模型的基础上，采用卡尔曼滤波算法对捷联陀螺漂移数 据进行了处理，以提高陀螺静态漂移误差系数的估计精度，并把得到的陀螺漂移误差模型实时补偿到捷联系统中，得到了满意的效果。-Marine gyro drift in the data time series model based on the Kalman filter algorithm using inertial gyro drift data were processed to enhance the g
kal(VC++)
- 用vc来写的卡尔曼滤波程序实现了车辆惯性导航系统的卡尔曼滤波，包括定位结算，姿态校正，速度校正等-Vc to write with Kalman filtering process to achieve a vehicle inertial navigation system of the Kalman filter, including the positioning settlement, posture correction, the speed of correction
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- 微型惯性测量组合(MIMU：Micro-inertial measurement unit)是国际上近年来发展起来的新技 术，作为一种不依赖任何外部信息，也不向外部辐射能量的自主式系统-Abstract：The Micro strapdown inertial measurement unit System are introduced, attitude algorithm and Kalman filtering equation of parameter identifying
GPSandINS
- 在MATLAB环境下，使用GPS与惯性导航技术相结合的联合导航卡尔曼滤波算法-In the MATLAB environment, the use of GPS and inertial navigation technology joint navigation Kalman filter algorithm
kalmanPresentation1
- Kalman Filtering design for Inertial Navigation System-Kalman Filtering design for Inertial Navigation System
RtwMatrix
- GPS惯性导航系统,解压后会有5M,这是我利用C#针对嵌入式WinCE做的一个GPS/惯性导航系统的组合定位软件，包括了卡尔曼滤波算法（含矩阵控件），界面显示，GPS串口接受，滤波结果输出以及WinCE应用调试控件，超强吧！直接下载可以在Visual.Net下调试使用-GPS inertial navigation system, after decompression will be 5M, which I use C# for embedded WinCE do a GPS/inertial
INS_GPS_integrated_navigation_system
- 通过卡尔曼滤波并采用开环控制方式实现惯性导航和GPS的数据融合-Through the Kalman filter and use the open-loop control method to achieve inertial navigation and GPS, data fusion
Achieving_integrity_in_an_INS_GPS_navigation_loop_
- 本文主要介绍了用模糊自适应卡尔曼滤波技术实现惯导和GPS的数据融合。-This paper presents a fuzzy adaptive Kalman filter technology for inertial navigation and GPS-data fusion.
2007102318148533
- 解压后会有5M,这是我利用C#针对嵌入式WinCE做的一个GPS/惯性导航系统的组合定位软件，包括了卡尔曼滤波算法（含矩阵控件），界面显示，GPS串口接受，滤波结果输出以及WinCE应用调试控件，超强吧！直接下载可以在Visual.Net下调试使用 -After decompression there 5M, which I use C# for embedded WinCE do a GPS/inertial navigation system integrated positioning s
global-positioning-systems-inertial-navigation-an
- This book constitutes an excellent source for understanding the basic operation of the Global Positioning Systems (GPS). It also allows the reader to take the first steps towards understanding how the Kalman Filter is implemented in the estimation of
INSKalmanfilterVCSourceCode
- Inertial navigation system Kalman filter VC + + source code-Inertial navigation system Kalman filter VC++ source code
week11-ben-kalman
- this ppt useful in inertial navigation system.so this currenlty usef in the aeroapce technology
EKF
- Extended Kalman Filter synthesis for integrated global positioning/inertial navigation systems
kalman
- 捷联惯性导航系统多位置对准程序卡尔曼滤波-Strapdown inertial navigation system position alignment procedure Kalman filter
2010041245
- 上传一个word档的联邦式扩展卡尔曼粒子滤波算法，大家学习粒子滤波有益，为了使联邦滤波器够有效处理非高斯、非线性系统的状态估计问题，提出将扩展卡尔曼粒子滤波引入联邦滤波结构中，得到一种新的联邦式扩展卡尔曼粒子滤波算法.使用扩展卡尔曼粒子滤波对联邦滤波子系统的多源数据进行处理，从而摆脱了经典卡尔曼滤波的限制，拓宽了联邦滤波器的实际应用范围.将联邦式扩展卡尔曼粒子滤波算法应用于非线性滤波器的一个标准验证模型进行了仿真实验，结果表明该算法是有效性的.-Abstract: A new particle
GPS-Global-Positioning-Systems---Inertial-Navigat
- this is signal processig and kalman ffilter
main007(Kalman)--
- 惯导系统C语言程序，使用卡尔曼进行滤波，四元数更新姿态矩阵-Inertial Navigation System C language program, using the Kalman filter, quaternion attitude matrix update
Inertial-Navigation-System-program
- 严恭敏老师编写的SINS的Matlab仿真程序，包括大方位误差角扩展卡尔曼（EKF），滤波初始对准仿真，静态惯导/星敏感器组合，大姿态误差角非线性状态模型，UKF滤波。-Yan Gong Min teacher preparation SINS of Matlab simulation program, including large azimuth error extended Kalman (EKF), filtering initial alignment simulation, stat