diff --git a/peregrine/acquisition.py b/peregrine/acquisition.py
index 9d4cc56..72cc9e8 100644
--- a/peregrine/acquisition.py
+++ b/peregrine/acquisition.py
@@ -54,14 +54,16 @@ class Acquisition:
Array of samples to use for acquisition. Can be `None` but in this case
`init_samples` *must* be called with an array of samples before any other
acquisition functions are used.
- sampling_freq : float, optional
+ sampling_freq : float
The sampling frequency of the samples in Hz.
- IF : float, optional
+ IF : float
The receiver intermediate frequency used when capturing the samples.
- samples_per_code : float, optional
+ samples_per_code : float
The number of samples corresponding to one code length.
- code_length : int, optional
+ code_length : int
The number of chips in the chipping code.
+ n_codes_integrate : int, optional
+ The number of codes to integrate.
offsets : int, optional
Offsets, in units of code length (1ms), to use when performing long
integrations to avoid clobbering by nav bit edges.
diff --git a/peregrine/short_set.py b/peregrine/short_set.py
index bfa9f2a..eb22e5f 100644
--- a/peregrine/short_set.py
+++ b/peregrine/short_set.py
@@ -48,12 +48,12 @@ def resolve_ms_integers(obs_pr, pred_pr, prn_ref, disp = True):
print "Resolving millisecond integers:"
for prn, pr in obs_pr.iteritems():
- pr_int_est = (pred_pr[prn] - pr) / gps.code_wavelength
+ pr_int_est = (pred_pr[prn] - pr) / gps.l1ca_code_wavelength
pr_int = round(pr_int_est)
if abs(pr_int - pr_int_est) > 0.15:
logger.warn("Pseudorange integer for PRN %2d is %.4f" % (
prn + 1, pr_int_est) + ", which isn't very close to an integer.")
- pr += pr_int * gps.code_wavelength
+ pr += pr_int * gps.l1ca_code_wavelength
obs_pr[prn] = pr
if disp:
print ("PRN %2d: pred pseudorange = %9.3f km, obs = %9.3f, " + \
@@ -78,9 +78,9 @@ def fill_remainder(n_ms):
return [k for k,v in itertools.groupby(sorted(hs))]
def long_correlation(signal, ca_code, code_phase, doppler, settings, plot=False, coherent = 0, nav_bit_hypoth = None):
- from swiftnav.correlate import track_correlate_
+ from swiftnav.correlate import track_correlate
code_freq_shift = (doppler / gps.l1) * gps.chip_rate
- samples_per_chip = settings.samplingFreq / (gps.chip_rate + code_freq_shift)
+ samples_per_chip = settings.freq_profile['sampling_freq'] / (gps.chip_rate + code_freq_shift)
samples_per_code = samples_per_chip * gps.chips_per_code
numSamplesToSkip = round(code_phase * samples_per_chip)
remCodePhase = (1.0 * numSamplesToSkip / samples_per_chip) - code_phase
@@ -94,12 +94,14 @@ def long_correlation(signal, ca_code, code_phase, doppler, settings, plot=False,
costas_q = 0.0
for loopCnt in range(n_ms):
rawSignal = signal[numSamplesToSkip:]#[:blksize_]
- E, P, L, blksize, remCodePhase, remCarrPhase = track_correlate_(
+ E, P, L, blksize, remCodePhase, remCarrPhase = track_correlate(
rawSignal,
+ 1023, # Chips to correlate
code_freq_shift + gps.chip_rate,
remCodePhase,
- doppler + settings.IF,
- remCarrPhase, ca_code, settings.samplingFreq)
+ doppler + settings.freq_profile['GPS_L1_IF'],
+ remCarrPhase, ca_code, settings.freq_profile['sampling_freq'],
+ gps.L1CA)
I_E = E.real
Q_E = E.imag
@@ -149,12 +151,10 @@ def refine_ob(signal, acq_result, settings, print_results = True, return_sweeps
# TODO: Fit code phase results for better resolution
from peregrine.include.generateCAcode import caCodes
from scipy import optimize as opt
- samples_per_chip = settings.samplingFreq / gps.chip_rate
+ samples_per_chip = settings.freq_profile['sampling_freq'] / gps.chip_rate
samples_per_code = samples_per_chip * gps.chips_per_code
# Get a vector with the C/A code sampled 1x/chip
ca_code = caCodes[acq_result.prn]
- # Add wrapping to either end to be able to do early/late
- ca_code = np.concatenate(([ca_code[1022]],ca_code,[ca_code[0]]))
dopp_offset_search = 100 # Hz away from acquisition
code_offsets = np.arange(-1,1, 1.0 / 16 / 2)
@@ -296,14 +296,14 @@ def refine_obs(signal, acq_results, settings,
return obs_cp, obs_dopp
-def predict_observables(prior_traj, prior_datetime, prns, ephem, window):
+def predict_observables(prior_traj, prior_datetime, prns, ephem, window, settings):
from datetime import timedelta
from numpy.linalg import norm
from numpy import dot
"""Given a list of PRNs, a set of ephemerides, a nominal capture time (datetime) and a
and a time window (seconds), compute the ranges and dopplers for
each satellite at 1ms shifts."""
- timeres = 50 * gps.code_period # Might be important to keep this an integer number of code periods
+ timeres = 50 * settings.code_period # Might be important to keep this an integer number of code periods
t0 = prior_datetime - timedelta(seconds=window / 2.0)
ranges = {}
dopplers = {}
@@ -360,11 +360,11 @@ def minimize_doppler_error(obs_dopp, times, pred_dopp, plot = False):
def plot_expected_vs_measured(acqed_prns, prn_ref,
obs_pr, obs_dopp,
prior_traj, t_better,
- ephem):
+ ephem, settings):
import matplotlib.pyplot as plt
# Compute predicted observables around this new estimate of capture time
- pred_ranges, pred_dopplers, times = predict_observables(prior_traj, t_better, acqed_prns, ephem, 20)
+ pred_ranges, pred_dopplers, times = predict_observables(prior_traj, t_better, acqed_prns, ephem, 20, settings)
pred_pr = pseudoranges_from_ranges(pred_ranges, prn_ref)
ax = plt.figure(figsize=(12,6)).gca()
@@ -660,7 +660,7 @@ def postprocess_short_samples(signal, prior_trajectory, t_prior, settings,
# Improve the time part of the prior estimate by minimizing doppler residuals
pred_ranges, pred_dopplers, times = predict_observables(prior_traj, t_prior,
acqed_prns, ephem,
- 30)
+ 30, settings)
i, t_better = minimize_doppler_error(obs_dopp, times, pred_dopplers,
plot = plot)
@@ -671,7 +671,7 @@ def postprocess_short_samples(signal, prior_trajectory, t_prior, settings,
print "By minimizing doppler residuals, adjusted the prior time and position by %.6s seconds, %.3f km" % (
delta_t, delta_r/ 1e3)
pred_ranges, pred_dopplers, times = predict_observables(
- prior_traj, t_better, acqed_prns, ephem, 1e-9)
+ prior_traj, t_better, acqed_prns, ephem, 1e-9, settings)
pred_pr_t_better = {prn: pred_ranges[prn][0] for prn in acqed_prns}
@@ -681,7 +681,7 @@ def postprocess_short_samples(signal, prior_trajectory, t_prior, settings,
if plot:
plot_expected_vs_measured(acqed_prns, prn_ref, obs_pr, obs_dopp,
- prior_traj, t_better, ephem)
+ prior_traj, t_better, ephem, settings)
# Perform PVT navigation solution
r_sol, t_sol, los, tot, residuals = pt_solve(r_better, t_better, obs_pr,
diff --git a/peregrine/warm_start.py b/peregrine/warm_start.py
index be2c005..2c2accc 100755
--- a/peregrine/warm_start.py
+++ b/peregrine/warm_start.py
@@ -91,9 +91,12 @@ def warm_start(signal, t_prior, r_prior, v_prior, ephem, settings,
samplingFreq = settings.freq_profile['sampling_freq']
- a = acquisition.Acquisition(signal, samplingFreq,
+ a = acquisition.Acquisition(gps.L1CA,
+ signal,
+ samplingFreq,
settings.freq_profile['GPS_L1_IF'],
samplingFreq * settings.code_period,
+ settings.code_length,
n_codes_integrate=n_codes_integrate,
wisdom_file = wiz_file)
# Attempt to acquire both the sats we predict are visible
@@ -102,7 +105,7 @@ def warm_start(signal, t_prior, r_prior, v_prior, ephem, settings,
prns = pred + notup,
doppler_priors = pred_dopp + [0 for p in notup],
doppler_search = settings.rxFreqTol * gps.l1,
- show_progress = True, multi = True)
+ progress_bar_output='stderr', multi = True)
nacq_results = acq_results[len(pred):]
acq_results = acq_results[:len(pred)]