A role of kalman filters in global positioning system
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#1
01-03-2011, 09:46 AM


presented by:
Y.Surya Deepthi


.ppt   A ROLE OF KALMAN FILTER IN GLOBAL POSITIONING SYSTEM.ppt (Size: 1.3 MB / Downloads: 82)
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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08-02-2012, 10:40 AM


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