openpilot v0.9.6 release

date: 2024-01-12T10:13:37
master commit: ba792d576a49a0899b88a753fa1c52956bedf9e6

common/simple_kalman.py

@@ -0,0 +1,54 @@
+import numpy as np
+
+
+def get_kalman_gain(dt, A, C, Q, R, iterations=100):
+  P = np.zeros_like(Q)
+  for _ in range(iterations):
+    P = A.dot(P).dot(A.T) + dt * Q
+    S = C.dot(P).dot(C.T) + R
+    K = P.dot(C.T).dot(np.linalg.inv(S))
+    P = (np.eye(len(P)) - K.dot(C)).dot(P)
+  return K
+
+
+class KF1D:
+  # this EKF assumes constant covariance matrix, so calculations are much simpler
+  # the Kalman gain also needs to be precomputed using the control module
+
+  def __init__(self, x0, A, C, K):
+    self.x0_0 = x0[0][0]
+    self.x1_0 = x0[1][0]
+    self.A0_0 = A[0][0]
+    self.A0_1 = A[0][1]
+    self.A1_0 = A[1][0]
+    self.A1_1 = A[1][1]
+    self.C0_0 = C[0]
+    self.C0_1 = C[1]
+    self.K0_0 = K[0][0]
+    self.K1_0 = K[1][0]
+
+    self.A_K_0 = self.A0_0 - self.K0_0 * self.C0_0
+    self.A_K_1 = self.A0_1 - self.K0_0 * self.C0_1
+    self.A_K_2 = self.A1_0 - self.K1_0 * self.C0_0
+    self.A_K_3 = self.A1_1 - self.K1_0 * self.C0_1
+
+    # K matrix needs to  be pre-computed as follow:
+    # import control
+    # (x, l, K) = control.dare(np.transpose(self.A), np.transpose(self.C), Q, R)
+    # self.K = np.transpose(K)
+
+  def update(self, meas):
+    #self.x = np.dot(self.A_K, self.x) + np.dot(self.K, meas)
+    x0_0 = self.A_K_0 * self.x0_0 + self.A_K_1 * self.x1_0 + self.K0_0 * meas
+    x1_0 = self.A_K_2 * self.x0_0 + self.A_K_3 * self.x1_0 + self.K1_0 * meas
+    self.x0_0 = x0_0
+    self.x1_0 = x1_0
+    return [self.x0_0, self.x1_0]
+
+  @property
+  def x(self):
+    return [[self.x0_0], [self.x1_0]]
+
+  def set_x(self, x):
+    self.x0_0 = x[0][0]
+    self.x1_0 = x[1][0]