Summary · Review: -Examined definitions of pseudorange and carrier phase Looked at some actual raw measurements from a RINEX file Today we look at:

Basic concepts Basic problem: We measure range and phase data that are related to the positions of the ground receiver, satellites and other quantities How do we determine the \ best position for the receiver and other quantities What do we mean by best \ estimate?

Specific common processes White-noise: Autocorrelation is Dirac-delta function; PSD is flat; integral of power under PSD is variance of process(true in general) First-order Gauss-Markov process (one of most models common in Kalman filtering)

Basic atmospheric structure mosphere Troposphere is 2 100 where the temperature stops decreasing in the tmosphere(10 km altitude)

Basic antenna operation Receiving and transmitting antennas are identical: Time just flows in opposite directions. Antenna problems are solved knowing the current distribution J(x') in the antenna and using a vector potential

Estimation · Summary Examine correlations -Process noise · White noise · Random walk First-order Gauss Markov Processes Kalman filters Estimation in which the parameters to be estimated are changing with time

石河子大学：《遥感导论》课程教学资源（习题解答）第八章 遥感、地理信息系统与全球定位系统综合应用答案

石河子大学：《遥感导论》课程教学资源（习题解答）第八章 遥感、地理信息系统与全球定位系统综合应用

Class expectations This is a graduate level class. There is no final exam Grading in the class is based on homework (75%) and on a final written report(25%) The report will be revised during semester and should be 2000-3000 words(8-10 double spaced pages)

Statistical approach to estimation Examine the multivariate Gaussian distribution: (x-)\v-(x-) Multivariant f(x)= Minimize (x-u)'v(x-u) gives largest probability density By minimizing the argument of the exponential in the probability density function, we maximize the likelihood of the estimates(MLE)