from numpy import zeros, arange, array
import matplotlib.pyplot as plt
from cluster_demo import X, P, A, Q, H, R, N_iter
estimate=Estimate(X,P)
plane=Plane('plane',X)
plane.stateSize = A.shape[0]
kf=KalmanFilter('kalmanfilter')
kf.setPosition(estimate)
radar=NormalSensor('radar')
radar.measurementSize = H.shape[0]
kfObserver=KalmanrObserver('Kalmanr Observer')
# allocate space for arrays
# Applying the Kalman Filter
for k in arange(0, N_iter):
kf.predict(A,Q)
plane.move(A)
radar.detect(plane,H,R)
kf.update(radar,H,R)
kfObserver.recieve(kf, plane, radar, k)
#reporter.result()
#import pylab
#print (Real[0,:])
import matplotlib.pyplot as plt
from sensorRegistration_demo import getG, X, P, A, Q, H, R1, R2, N_iter, position1, bias1, position2, bias2, mu, sigma, E, Rvector1, Rvector2
estimate = Estimate(X,P)
biasEstimate = BiasEstimate(X, P, mu)
biasEstimate.uncertainty2parameters(mu, sigma)
plane=NoisedPlane('plane',X)
plane.stateSize = A.shape[0]
# snesor 1
kf1=KalmanFilter('Kalman filter for sensor 1')
kf1.setPosition(estimate)
radar1=BiasSensor('radar 1', position1, bias1)
radar1.measurementSize = H.shape[0]
kfObserver1=KalmanrObserver('Kalmanr Observer for kf 1')
# snesor 2
kf2=KalmanFilter('Kalman filter for sensor 2')
kf2.setPosition(estimate)
radar2=BiasSensor('radar 2', position2, bias2)
radar2.measurementSize = H.shape[0]
kfObserver2=KalmanrObserver('Kalmanr Observer for kf 2')
#decentralized kf
kfdc=KalmanFilterDecentrailized('Kalman filter Centralized')
kfdc.setPosition(estimate)
kfObserverDC=KalmanrObserverDecentralized('Kalmanr Observer for center')
# allocate space for arrays
kfdc=KalmanFilterDecentrailized('Kalman filter Centralized')
kfdc.setPosition(estimate)
kfObserverDC=KalmanrObserverDecentralized('Kalmanr Observer for center')
# variational filtering