A role of kalman filters in global positioning system
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 seminar class Active In SP Posts: 5,361 Joined: Feb 2011 01-03-2011, 09:46 AM presented by: Y.Surya Deepthi   A ROLE OF KALMAN FILTER IN GLOBAL POSITIONING SYSTEM.ppt (Size: 1.3 MB / Downloads: 81) A role of kalman filters in global positioning system INTRODUCTION  Location tracking plays an important role in many applications  In Kalman filtering method, the smoothing procedure by linear regression makes the estimated location more accurate than that of the GPS method  The Kalman filtering method estimates velocity as well as location  Recursive process of Kalman filtering  An improved location tracking algorithm which uses velocity renovation process with Kalman filter is implemented in this paper ANALAYSIS OF LOCATION ESTIMATION  Tracking Services based on geographic and location information  Collects the location of moving object and presents it on geographic map  GPS satellite signals can be detected by GPS receivers, which calculate their locations anywhere on the Earth at any time  Kalman filter and velocity estimation to get better accuracy  The implementation of Kalman filter has two stages.  S(k) contains location data defined as S(k) = (X(k),Y(k),Vx(k),Vy(k))T  X(k) and Y(k) are the coordinates (x and y) of a GPS’s location at time instant k  Vx(k) and Vy(k) in equation denote x-axis and y-axis directional velocities of a GPS receiver at time instant k  State model of Kalman filter is S(k) = AS(k)  A is a transformation matrix  Kalman filtering method can be summarized like this: At first, predict S(k|k-1) and minimum predicted Mean Square Error (MSE) M(k|k-1) can be obtained by S(k|k-1) = AS(k-1|k-1) M(k|k-1) = AM(k-1|k-1)AT+BQBT  B is an optional control input to current state  Q is system dynamic noise.  Kalman gain can be described as K(k|k-1) = M(k|k-1)HT.{R+HM(k-1|k-1)HT}-1  R is receiver noise  H is measurement sensitivity matrix  Kalman filtering can be updated by  l1(k) and l2(k) are coordinates (x and y) of estimated location by GPS.  Process of Kalman filtering method progresses recursively whenever new estimated location L(k) of GPS comes to Kalman filter.  Block diagram of proposed location tracking algorithm which uses velocity renovation process with Kalman filter LOCATION TRACKING WITH VELOCITY ESTIMATION  Velocity renovation process is to use accurately estimated velocity in Kalman filter for increasing accuracy of location estimation.  It consists of two parts. • Velocity estimator • Direction finder  By estimated velocity and direction in velocity renovation process, x-axis and y-axis directional velocities can be estimated.
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