You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
182 lines
6.1 KiB
182 lines
6.1 KiB
#!/usr/bin/env python3
|
|
import sys
|
|
from typing import List
|
|
|
|
import numpy as np
|
|
import sympy as sp
|
|
|
|
from rednose.helpers.ekf_sym import EKF_sym, gen_code
|
|
from selfdrive.locationd.models.constants import ObservationKind
|
|
from selfdrive.locationd.models.loc_kf import parse_pr, parse_prr
|
|
|
|
|
|
class States():
|
|
ECEF_POS = slice(0, 3) # x, y and z in ECEF in meters
|
|
ECEF_VELOCITY = slice(3, 6)
|
|
CLOCK_BIAS = slice(6, 7) # clock bias in light-meters,
|
|
CLOCK_DRIFT = slice(7, 8) # clock drift in light-meters/s,
|
|
CLOCK_ACCELERATION = slice(8, 9) # clock acceleration in light-meters/s**2
|
|
GLONASS_BIAS = slice(9, 10) # clock drift in light-meters/s,
|
|
GLONASS_FREQ_SLOPE = slice(10, 11) # GLONASS bias in m expressed as bias + freq_num*freq_slope
|
|
|
|
|
|
class GNSSKalman():
|
|
name = 'gnss'
|
|
|
|
x_initial = np.array([-2712700.6008, -4281600.6679, 3859300.1830,
|
|
0, 0, 0,
|
|
0, 0, 0,
|
|
0, 0])
|
|
|
|
# state covariance
|
|
P_initial = np.diag([1e16, 1e16, 1e16,
|
|
10**2, 10**2, 10**2,
|
|
1e14, (100)**2, (0.2)**2,
|
|
(10)**2, (1)**2])
|
|
|
|
# process noise
|
|
Q = np.diag([0.03**2, 0.03**2, 0.03**2,
|
|
3**2, 3**2, 3**2,
|
|
(.1)**2, (0)**2, (0.005)**2,
|
|
.1**2, (.01)**2])
|
|
|
|
maha_test_kinds: List[int] = [] # ObservationKind.PSEUDORANGE_RATE, ObservationKind.PSEUDORANGE, ObservationKind.PSEUDORANGE_GLONASS]
|
|
|
|
@staticmethod
|
|
def generate_code(generated_dir):
|
|
dim_state = GNSSKalman.x_initial.shape[0]
|
|
name = GNSSKalman.name
|
|
maha_test_kinds = GNSSKalman.maha_test_kinds
|
|
|
|
# make functions and jacobians with sympy
|
|
# state variables
|
|
state_sym = sp.MatrixSymbol('state', dim_state, 1)
|
|
state = sp.Matrix(state_sym)
|
|
x, y, z = state[0:3, :]
|
|
v = state[3:6, :]
|
|
vx, vy, vz = v
|
|
cb, cd, ca = state[6:9, :]
|
|
glonass_bias, glonass_freq_slope = state[9:11, :]
|
|
|
|
dt = sp.Symbol('dt')
|
|
|
|
state_dot = sp.Matrix(np.zeros((dim_state, 1)))
|
|
state_dot[:3, :] = v
|
|
state_dot[6, 0] = cd
|
|
state_dot[7, 0] = ca
|
|
|
|
# Basic descretization, 1st order integrator
|
|
# Can be pretty bad if dt is big
|
|
f_sym = state + dt * state_dot
|
|
|
|
#
|
|
# Observation functions
|
|
#
|
|
|
|
# extra args
|
|
sat_pos_freq_sym = sp.MatrixSymbol('sat_pos', 4, 1)
|
|
sat_pos_vel_sym = sp.MatrixSymbol('sat_pos_vel', 6, 1)
|
|
# sat_los_sym = sp.MatrixSymbol('sat_los', 3, 1)
|
|
# orb_epos_sym = sp.MatrixSymbol('orb_epos_sym', 3, 1)
|
|
|
|
# expand extra args
|
|
sat_x, sat_y, sat_z, glonass_freq = sat_pos_freq_sym
|
|
sat_vx, sat_vy, sat_vz = sat_pos_vel_sym[3:]
|
|
# los_x, los_y, los_z = sat_los_sym
|
|
# orb_x, orb_y, orb_z = orb_epos_sym
|
|
|
|
h_pseudorange_sym = sp.Matrix([
|
|
sp.sqrt(
|
|
(x - sat_x)**2 +
|
|
(y - sat_y)**2 +
|
|
(z - sat_z)**2
|
|
) + cb
|
|
])
|
|
|
|
h_pseudorange_glonass_sym = sp.Matrix([
|
|
sp.sqrt(
|
|
(x - sat_x)**2 +
|
|
(y - sat_y)**2 +
|
|
(z - sat_z)**2
|
|
) + cb + glonass_bias + glonass_freq_slope * glonass_freq
|
|
])
|
|
|
|
los_vector = (sp.Matrix(sat_pos_vel_sym[0:3]) - sp.Matrix([x, y, z]))
|
|
los_vector = los_vector / sp.sqrt(los_vector[0]**2 + los_vector[1]**2 + los_vector[2]**2)
|
|
h_pseudorange_rate_sym = sp.Matrix([los_vector[0] * (sat_vx - vx) +
|
|
los_vector[1] * (sat_vy - vy) +
|
|
los_vector[2] * (sat_vz - vz) +
|
|
cd])
|
|
|
|
obs_eqs = [[h_pseudorange_sym, ObservationKind.PSEUDORANGE_GPS, sat_pos_freq_sym],
|
|
[h_pseudorange_glonass_sym, ObservationKind.PSEUDORANGE_GLONASS, sat_pos_freq_sym],
|
|
[h_pseudorange_rate_sym, ObservationKind.PSEUDORANGE_RATE_GPS, sat_pos_vel_sym],
|
|
[h_pseudorange_rate_sym, ObservationKind.PSEUDORANGE_RATE_GLONASS, sat_pos_vel_sym]]
|
|
|
|
gen_code(generated_dir, name, f_sym, dt, state_sym, obs_eqs, dim_state, dim_state, maha_test_kinds=maha_test_kinds)
|
|
|
|
def __init__(self, generated_dir):
|
|
self.dim_state = self.x_initial.shape[0]
|
|
|
|
# init filter
|
|
self.filter = EKF_sym(generated_dir, self.name, self.Q, self.x_initial, self.P_initial, self.dim_state, self.dim_state, maha_test_kinds=self.maha_test_kinds)
|
|
self.init_state(GNSSKalman.x_initial, covs=GNSSKalman.P_initial)
|
|
|
|
@property
|
|
def x(self):
|
|
return self.filter.state()
|
|
|
|
@property
|
|
def P(self):
|
|
return self.filter.covs()
|
|
|
|
def predict(self, t):
|
|
return self.filter.predict(t)
|
|
|
|
def rts_smooth(self, estimates):
|
|
return self.filter.rts_smooth(estimates, norm_quats=False)
|
|
|
|
def init_state(self, state, covs_diag=None, covs=None, filter_time=None):
|
|
if covs_diag is not None:
|
|
P = np.diag(covs_diag)
|
|
elif covs is not None:
|
|
P = covs
|
|
else:
|
|
P = self.filter.covs()
|
|
self.filter.init_state(state, P, filter_time)
|
|
|
|
def predict_and_observe(self, t, kind, data):
|
|
if len(data) > 0:
|
|
data = np.atleast_2d(data)
|
|
if kind == ObservationKind.PSEUDORANGE_GPS or kind == ObservationKind.PSEUDORANGE_GLONASS:
|
|
r = self.predict_and_update_pseudorange(data, t, kind)
|
|
elif kind == ObservationKind.PSEUDORANGE_RATE_GPS or kind == ObservationKind.PSEUDORANGE_RATE_GLONASS:
|
|
r = self.predict_and_update_pseudorange_rate(data, t, kind)
|
|
return r
|
|
|
|
def predict_and_update_pseudorange(self, meas, t, kind):
|
|
R = np.zeros((len(meas), 1, 1))
|
|
sat_pos_freq = np.zeros((len(meas), 4))
|
|
z = np.zeros((len(meas), 1))
|
|
for i, m in enumerate(meas):
|
|
z_i, R_i, sat_pos_freq_i = parse_pr(m)
|
|
sat_pos_freq[i, :] = sat_pos_freq_i
|
|
z[i, :] = z_i
|
|
R[i, :, :] = R_i
|
|
return self.filter.predict_and_update_batch(t, kind, z, R, sat_pos_freq)
|
|
|
|
def predict_and_update_pseudorange_rate(self, meas, t, kind):
|
|
R = np.zeros((len(meas), 1, 1))
|
|
z = np.zeros((len(meas), 1))
|
|
sat_pos_vel = np.zeros((len(meas), 6))
|
|
for i, m in enumerate(meas):
|
|
z_i, R_i, sat_pos_vel_i = parse_prr(m)
|
|
sat_pos_vel[i] = sat_pos_vel_i
|
|
R[i, :, :] = R_i
|
|
z[i, :] = z_i
|
|
return self.filter.predict_and_update_batch(t, kind, z, R, sat_pos_vel)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
generated_dir = sys.argv[2]
|
|
GNSSKalman.generate_code(generated_dir)
|
|
|