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105 lines
3.5 KiB
105 lines
3.5 KiB
#!/usr/bin/env python3
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import numpy as np
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from . import gnss_model
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from .kalman_helpers import ObservationKind
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from .ekf_sym import EKF_sym
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from selfdrive.locationd.kalman.loc_kf import parse_pr, parse_prr
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class States():
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ECEF_POS = slice(0,3) # x, y and z in ECEF in meters
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ECEF_VELOCITY = slice(3,6)
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CLOCK_BIAS = slice(6, 7) # clock bias in light-meters,
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CLOCK_DRIFT = slice(7, 8) # clock drift in light-meters/s,
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CLOCK_ACCELERATION = slice(8, 9) # clock acceleration in light-meters/s**2
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GLONASS_BIAS = slice(9, 10) # clock drift in light-meters/s,
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GLONASS_FREQ_SLOPE = slice(10, 11) # GLONASS bias in m expressed as bias + freq_num*freq_slope
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class GNSSKalman():
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def __init__(self, N=0, max_tracks=3000):
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x_initial = np.array([-2712700.6008, -4281600.6679, 3859300.1830,
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0, 0, 0,
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0, 0, 0,
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0, 0])
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# state covariance
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P_initial = np.diag([10000**2, 10000**2, 10000**2,
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10**2, 10**2, 10**2,
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(2000000)**2, (100)**2, (0.5)**2,
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(10)**2, (1)**2])
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# process noise
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Q = np.diag([0.3**2, 0.3**2, 0.3**2,
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3**2, 3**2, 3**2,
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(.1)**2, (0)**2, (0.01)**2,
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.1**2, (.01)**2])
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self.dim_state = x_initial.shape[0]
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# mahalanobis outlier rejection
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maha_test_kinds = []#ObservationKind.PSEUDORANGE_RATE, ObservationKind.PSEUDORANGE, ObservationKind.PSEUDORANGE_GLONASS]
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name = 'gnss'
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gnss_model.gen_model(name, self.dim_state, maha_test_kinds)
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# init filter
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self.filter = EKF_sym(name, Q, x_initial, P_initial, self.dim_state, self.dim_state, maha_test_kinds=maha_test_kinds)
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@property
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def x(self):
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return self.filter.state()
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@property
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def P(self):
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return self.filter.covs()
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def predict(self, t):
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return self.filter.predict(t)
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def rts_smooth(self, estimates):
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return self.filter.rts_smooth(estimates, norm_quats=False)
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def init_state(self, state, covs_diag=None, covs=None, filter_time=None):
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if covs_diag is not None:
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P = np.diag(covs_diag)
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elif covs is not None:
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P = covs
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else:
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P = self.filter.covs()
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self.filter.init_state(state, P, filter_time)
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def predict_and_observe(self, t, kind, data):
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if len(data) > 0:
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data = np.atleast_2d(data)
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if kind == ObservationKind.PSEUDORANGE_GPS or kind == ObservationKind.PSEUDORANGE_GLONASS:
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r = self.predict_and_update_pseudorange(data, t, kind)
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elif kind == ObservationKind.PSEUDORANGE_RATE_GPS or kind == ObservationKind.PSEUDORANGE_RATE_GLONASS:
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r = self.predict_and_update_pseudorange_rate(data, t, kind)
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return r
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def predict_and_update_pseudorange(self, meas, t, kind):
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R = np.zeros((len(meas), 1, 1))
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sat_pos_freq = np.zeros((len(meas), 4))
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z = np.zeros((len(meas), 1))
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for i, m in enumerate(meas):
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z_i, R_i, sat_pos_freq_i = parse_pr(m)
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sat_pos_freq[i,:] = sat_pos_freq_i
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z[i,:] = z_i
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R[i,:,:] = R_i
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return self.filter.predict_and_update_batch(t, kind, z, R, sat_pos_freq)
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def predict_and_update_pseudorange_rate(self, meas, t, kind):
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R = np.zeros((len(meas), 1, 1))
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z = np.zeros((len(meas), 1))
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sat_pos_vel = np.zeros((len(meas), 6))
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for i, m in enumerate(meas):
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z_i, R_i, sat_pos_vel_i = parse_prr(m)
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sat_pos_vel[i] = sat_pos_vel_i
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R[i,:,:] = R_i
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z[i, :] = z_i
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return self.filter.predict_and_update_batch(t, kind, z, R, sat_pos_vel)
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if __name__ == "__main__":
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GNSSKalman()
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