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
Single Satellite with Constant Velocity Receiver
It is of academic interest to see if we can estimate the constant velocity receiver with only a single satellite. As in the preceding section, it seems reasonable to make the receiver location and velocity states in our model of the real world. If the receiver travels at constant velocity, that means the receiver acceleration (i.e., derivative of velocity) is zero. Again, to protect ourselves in case the velocity is not constant we will include some process noise. Therefore, in our model of the real world, we will assume that the acceleration of the receiver is white noise or
Listing 11.6: Extended Kalman filter for estimating the states of a receiver moving at constant velocity
<a name="1038"></a><a name="page480"></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) <b class="bold">REAL*8 PHI(4,4),P(4,4),M(4,4),PHIP(4,4),PHIPPHIT(4,4),GAIN(4,2) REAL*8 Q(4,4),HMAT(2,4),HM(2,4),MHT(4,2) REAL*8 PHIT(4,4),RMAT(2,2),HMHTRINV(2,2) REAL8* HMHT(2,2),HT(4,2),KH(4,4),IDN(4,4),IKH(4,4),HMHTR(2,2)</b> INTEGER ORDER SIGNOISE=300. X=0. Y=0. XH=1000. YH+2000. <b class="bold">XDH=0. YDH=0.</b> XR1=1000000. XR1=20000.*3280. XR2=50000000. YR2=20000.*3280. OPEN(2,STATUS='UNKNOWN',FILE='COVFIL') OPEN(1,STATUS='UNKNOWN',FILE='DATFIL') ...
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