配置BGP 路由协议

gps相关的资料1-a GPS-related information a

成功人事的七个习惯，一本不错的书，包括中文和英文版本。-success of the seven habits of personnel, a good book, including Chinese and English versions.

站的高看的远 多思考人脑与计算机的不同点与相同点 对于自身也有更多认识-Look at the high points far to think more about the human brain and computer different points with the same points are also more aware of their own

ebook演化计算相关的书籍内付有C程序-Evolutionary Computation ebook with pay-related books have C program

讲述Templates知识，为英文版，既可以学习也可以做为参考资料-Templates on the knowledge of English, both can learn also can be used as reference

128*64的LCD中文件资料。对于学习写LCD程序的人来说，相当重要。

Because of the poor observability of Inertial Navigation System on stationary base, the estimation error of the azimuth will converge very slowly in initial alignment by means of Kalmari filtering, and making the time initial alignment is longer. In this paper, a fast estimation method of the azimuth error is creatively proposed for the initial alignment of INS on stationary base. On the basis of the the fast convergence of the leveling error, the azimuth error can be directly calculated. By means of this fast initial alignment method, the time of initial alignment is reduced greatly. The computer simulation results illustrate the efficiency of the method.

张孝祥java就业培训教程配套源码，全得很-Zhang Xiaoxiang employment Training Guide matching source, all very

Advanced Bash− Scripting Guide,是linux程序开发人员必备的参考书之一，相当经典。 压缩包里面含有英文原版以及中文译版两个版本，方便大家阅读。-Advanced Bash

The celebrated Kalman filter, rooted in the state-space formulation or linear dynamical systems, provides a recursive solution to the linear optimal filtering problem. It applies to stationary as well as nonstationary environments. The solution is recursive in that each updated estimate of the state is computed from the previous estimate and the new input data, so only the previous estimate requires storage. In addition to eliminating the need for storing the entire 1 past observed data, the Kalman filter is computationally more efficient than computing the estimate directly from the entire past observed data at each step of the filtering process.