Fundamentals of Kalman Filtering: A Practical Approach, Second Edition
Containing numerous real-life problems and examples presented in detail, this text demonstrates the many ways in which Kalman filters can be designed.
TABLE OF CONTENTS Fundamentals of Kalman Filtering: A Practical Approach, Second Edition
Using Extended Kalman Filtering
To see if we can get even better estimates of the receiver location errors, let us try to apply extended Kalman filtering to this problem. Because we are trying to estimate the location of the receiver, it seems reasonable to make the receiver location states. For now, the receiver is assumed to be stationary or fixed in location, which means that the receiver velocity (i.e., derivative of position) is zero. Therefore, we can say that
Listing 11.3: Simulation to see if receiver location can be determined from two noisy range measurements that are filtered
<a name="987"></a><a name="page456"></a>C THE FIRST THREE STATEMENTS INVOKE THE ABSOFT RANDOM NUMBER GENERATOR ON THE MACINTOSH GLOBAL DEFINE INCLUDE 'quickdraw.inc' END IMPLICIT REAL*8(A-H) IMPLICIT REAL*8(O-Z) REAL*8K1,K2,K3 SIGNOISE=300. X=0. Y=0. XR1=1000000. YR1=20000.*3280. YR2=50000000. YR2=20000.*3280. <b class="bold">R1H=0. R1DH=0. R1DDH=0. R2H=0. R2DH=0. R2DDH=0.</b> OPEN(1,STATUS='UNKNOWN',FILE='DATFIL') TS=1. TF=100. T=0. S=0. H=.01 XN=0. WHILE(T < =TF) XR1OLD=XR1 XR2OLD=XR2 XR1D=-14600. XR2D=-14600. ...
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