def __init__(self, params):
"""
Initialize the parameters, which are common across different
types of tracking channels.
Parameters
----------
params : dictionary
The subset of tracking channel parameters that are deemed
to be common across different types of tracking channels.
"""
for (key, value) in params.iteritems():
setattr(self, key, value)
self.prn = params['acq'].prn
self.signal = params['acq'].signal
self.results_num = 500
self.stage1 = True
self.lock_detect = LockDetector(
k1=self.lock_detect_params["k1"],
k2=self.lock_detect_params["k2"],
lp=self.lock_detect_params["lp"],
lo=self.lock_detect_params["lo"])
self.alias_detect = AliasDetector(
acc_len=defaults.alias_detect_interval_ms / self.coherent_ms,
time_diff=1)
self.cn0_est = CN0Estimator(
bw=1e3 / self.coherent_ms,
cn0_0=self.cn0_0,
cutoff_freq=0.1,
loop_freq=self.loop_filter_params["loop_freq"]
)
self.loop_filter = self.loop_filter_class(
loop_freq=self.loop_filter_params['loop_freq'],
code_freq=self.code_freq_init,
code_bw=self.loop_filter_params['code_bw'],
code_zeta=self.loop_filter_params['code_zeta'],
code_k=self.loop_filter_params['code_k'],
carr_to_code=self.loop_filter_params['carr_to_code'],
carr_freq=self.acq.doppler,
carr_bw=self.loop_filter_params['carr_bw'],
carr_zeta=self.loop_filter_params['carr_zeta'],
carr_k=self.loop_filter_params['carr_k'],
carr_freq_b1=self.loop_filter_params['carr_freq_b1'],
)
self.next_code_freq = self.loop_filter.to_dict()['code_freq']
self.next_carr_freq = self.loop_filter.to_dict()['carr_freq']
self.track_result = TrackResults(self.results_num,
self.acq.prn,
self.acq.signal)
self.alias_detect_init = 1
self.code_phase = 0.0
self.carr_phase = 0.0
self.samples_per_chip = int(round(self.sampling_freq / self.chipping_rate))
self.sample_index = params['samples']['sample_index']
self.sample_index += self.acq.sample_index
self.sample_index += self.acq.code_phase * self.samples_per_chip
self.sample_index = int(math.floor(self.sample_index))
self.carr_phase_acc = 0.0
self.code_phase_acc = 0.0
self.samples_tracked = 0
self.i = 0
self.pipelining = False # Flag if pipelining is used
self.pipelining_k = 0. # Error prediction coefficient for pipelining
self.short_n_long = False # Short/Long cycle simulation
self.short_step = True # Short cycle
if self.tracker_options:
mode = self.tracker_options['mode']
if mode == 'pipelining':
self.pipelining = True
self.pipelining_k = self.tracker_options['k']
elif mode == 'short-long-cycles':
self.short_n_long = True
self.pipelining = True
self.pipelining_k = self.tracker_options['k']
else:
raise ValueError("Invalid tracker mode %s" % str(mode))
