Using the methods of time series spectral analysis and Kalman filter, this article discussed the additive problems of two stochastic processes, mainly Auto Regression Moving Average (ARMA) processes.
本文利用时间序列谱分析和卡尔曼滤波的方法讨论了两个随机过程，主要是自回归滑动平均(ARMA)过程，的叠加问题。
Based on one fiber optic gyro SINS, three initial alignment methods including classical gyrocompass, two-position Kalman filter and continuous-rotation Kalman filter are experimentally studied.
基于某型低精度光纤陀螺捷联系统，分别采用经典罗经对准、两位置卡尔曼滤波及连续旋转卡尔曼滤波三种方法进行了初始对准实验研究。
Kalman filter in mathematics is a statistical estimation methods, By a series of errors with the actual measurement data and the physical parameters of the best estimate.
卡尔曼滤波在数学上是一种统计估算方法，通过处理一系列带有误差的实际量测数据而得到的物理参数的最佳估算。
Kalman filter and particle filter is a typical representative of the probability tracking methods.
概率跟踪卡尔曼滤波和粒子滤波是这类方法的典型代表。
Attitude calculation techniques, initial alignment methods and a real-time adaptive extended Kalman filter used for improve the system precision were discussed.
模块的组成、功能，详细说明了姿态角的计算、初始对准以及采用的稳定、实时、变增益自适应扩展卡尔曼滤波估计算法。
At the same time, we use Kalman filter error equations in errors analysis for autonomous navigation algorithm, and compare the analysis results of the two methods.
同时利用卡尔曼滤波误差方程对自主导航算法进行误差分析，并将两种分析结果作比较。
The simulation result shows that the methods based on he Kalman filter perform better than Talor series, and hybrid method has higher precision than only method.
通过研究仿真发现基于卡尔曼滤波的方法要优于泰勒展开法，并且混合定位的定位精度要高于单一定位的定位精度。
Initial value conditions on Expanded-Kalman-Filter are searched by Gald-Cutting and Component-Minimum methods, and then optimized.
应用黄金分割法和元素交替极小化法对扩展的卡尔曼滤波初值条件进行搜索并获得优化解。
Initial value conditions on Expanded-Kalman-Filter are searched by Gald-Cutting and Component-Minimum methods, and then optimized.
应用黄金分割法和元素交替极小化法对扩展的卡尔曼滤波初值条件进行搜索并获得优化解。
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