01-03-2011, 09:46 AM
presented by:
Y.Surya Deepthi
[attachment=9259]
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.
Y.Surya Deepthi
[attachment=9259]
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.