def __init__(self, I, O, C):
'''
Constructor
'''
self.Inputs = I
self.Outputs = O
self.Controls = C
self.x_hat_k = Matrix((O,1))
self.P_hat_k = Matrix((O,O))
#update
self.x_k = Matrix((O,1))
self.P_k = Matrix((O,O))
#Matrix
self.H = Matrix((I,O))
self.F = Matrix((O,O))
self.B = Matrix((O,C))
#self.G = Matrix((O,C))
self.Q = Matrix((O,O))
self.R = Matrix((I,I))
'''self.x_k = Matrix([O,1])
def setMatH(self, arr):
if len(arr)==self.Inputs and len(arr[0])==self.Outputs:
self.H = Matrix(arr)
else:
print "Bad array size %sx%s for H matrix (expect %sx%s)" % (len(arr), len(arr[0]), self.Inputs, self.Outputs)
class KalmanGeneric(object):
'''
classdocs
'''
#estimation
x_hat_k = None
P_hat_k = None
#update
x_k = None
P_k = None
#Matrix
H = None
F = None
B = None
#G = None
Q = None
R = None
#z_k = None
#u_k = None
#v_k = None
#w_k = None
#Estimated state
#x_est = None
#P_est = None
#Updated state
#x_upd = None
#P_upd = None
#Matrices (constants)
#F = None
#Q = None
#H = None
#R = None
#G = None
#Transposed matrices
#FT = None
#HT = None
Inputs = 0
Outputs = 0
Controls = 0
def __init__(self, I, O, C):
'''
Constructor
'''
self.Inputs = I
self.Outputs = O
self.Controls = C
self.x_hat_k = Matrix((O,1))
self.P_hat_k = Matrix((O,O))
#update
self.x_k = Matrix((O,1))
self.P_k = Matrix((O,O))
#Matrix
self.H = Matrix((I,O))
self.F = Matrix((O,O))
self.B = Matrix((O,C))
#self.G = Matrix((O,C))
self.Q = Matrix((O,O))
self.R = Matrix((I,I))
'''self.x_k = Matrix([O,1])
self.P_k = Matrix([O,O])
self.z_k = Matrix([I,1])
self.u_k = Matrix([O,1])
self.v_k = Matrix([I,1])
self.w_k = Matrix([O,1])
self.x_est = Matrix([O,1])
self.P_est = Matrix([O,O])
self.x_upd = Matrix([O,1])
self.P_upd = Matrix([O,O])
self.F = Matrix([O,O])
self.Q = Matrix([O,O])
self.H = Matrix([I,O])
self.R = Matrix([I,I])
self.G = Matrix([I,1])
self.FT = Matrix([O,O])
self.HT = Matrix([O,I])'''
def setMatF(self, arr):
if len(arr)==self.Outputs and len(arr[0])==self.Outputs:
self.F = Matrix(arr)
else:
print "Bad array size %sx%s for F matrix (expect %sx%s)" % (len(arr), len(arr[0]), self.Outputs, self.Outputs)
def setMatH(self, arr):
if len(arr)==self.Inputs and len(arr[0])==self.Outputs:
self.H = Matrix(arr)
else:
print "Bad array size %sx%s for H matrix (expect %sx%s)" % (len(arr), len(arr[0]), self.Inputs, self.Outputs)
#def setMatG(self, arr):
# if len(arr)==self.Ouputs and len(arr[0]==self.Controls):
# self.G = Matrix(arr)
# else:
# print "Bad array size %sx%s for G matrix" % (len(arr), len(arr[0]))
def newMeasure(self, m, control):
self.predict(Matrix(control))
self.update(Matrix(m))
return self.x_k
def setQ(self, arr):
if len(arr)==self.Outputs and len(arr[0])==self.Outputs:
self.Q = Matrix(arr)
else:
print "Bad array size %sx%s for Q matrix (expect %sx%s)" % (len(arr), len(arr[0]), self.Outputs, self.Outputs)
def setB(self, arr):
if len(arr)==self.Outputs and len(arr[0])==self.Controls:
self.B = Matrix(arr)
else:
print "Bad array size %sx%s for B matrix (expect %sx%s)" % (len(arr), len(arr[0]), self.Outputs, self.Controls)
def setR(self, arr):
if len(arr)==self.Inputs and len(arr[0])==self.Inputs:
self.R = Matrix(arr)
else:
print "Bad array size %sx%s for R matrix (expect %sx%s)" % (len(arr), len(arr[0]), self.Inputs, self.Inputs)
def setX0(self, arr):
if len(arr)==self.Outputs and len(arr[0])==1:
self.x_k = Matrix(arr)
else:
print "Bad array size %sx%s for x_k matrix (expect %sx%s)" % (len(arr), len(arr[0]), self.Outputs, 1)
def setP0(self, arr):
if len(arr)==self.Outputs and len(arr[0])==self.Outputs:
self.P_k = Matrix(arr)
else:
print "Bad array size %sx%s for P_k matrix (expect %sx%s)" % (len(arr), len(arr[0]), self.Outputs, self.Outputs)
def __repr__(self):
ret = ""
ret += "F: \n" + str(self.F)
ret += "B: \n" + str(self.B)
ret += "H: \n" + str(self.H)
ret += "Q: \n" + str(self.Q)
ret += "R: \n" + str(self.R)
return ret
def predict(self, u_k):
'''
Predict
'''
random.seed()
#w_k = Matrix([random.multivariate_normal([0]*self.Outputs, self.Q.components).tolist()]).transpose()
w_k = Matrix([[0.001], [0.001]])
#x_{k|k-1} = F*x_{k-1|k-1} + B*u_k + w_k
if(self.Controls>0):
cpart = self.B*u_k
else:
cpart = Matrix((self.Outputs, 1))
#print "F" + str(self.F)
#print "x_k" + str(self.x_k)
#print "controlPart" + str(cpart)
#print "w_k" + str(w_k)
self.x_hat_k = self.F*self.x_k + cpart + w_k
#print "x_hat_k: " + str(self.x_hat_k)
#P_{k|k-1} = F*P_{k-1|k-1}*F^T + Q_k
self.P_hat_k = self.F*self.P_k*self.F.transpose() + self.Q
#print "P_hat_k: " + str(self.P_hat_k)
def update(self, z_k):
'''
Update
'''
#y_k = z_k - H*x_{k|k-1}
y = z_k - self.H*self.x_hat_k
#print "y: " + str(y)
#S_k = H*P_{k|k-1}*H^T + R_k
S = self.H*self.P_hat_k*self.H.transpose() + self.R
#print "S: " + str(S)
#K_k = P_{k|k-1}*H^T*S^{-1}
K = self.P_hat_k*self.H.transpose()*S.invert()
#print "K: " + str(K)
#x_{k|k} = x_{k|k-1} + K_k*y_k
self.x_k = self.x_hat_k + K*y
#print "x_k: " + str(self.x_k)
#P_{k|k} = (I-K_k*H_k)*P_{k|k-1}
self.P_k = (Matrix((self.Outputs, self.Outputs)).identity() - K*self.H)*self.P_hat_k