def LS_Filter_Multiple(refChannel,
srvChannel,
filterLen,
sampleRate,
dopplerBins=[0]):
'''Clutter removal with least squares filter. This function allows the least
squares filter to be applied over several frequency bins. Useful when there
is significant clutter at nonzero doppler values.
Parameters:
refChannel: Array containing the reference channel signal
srvChannel: Array containing the surveillance channel signal
filterLen: Length of the least squares filter (in samples)
dopplerBins: List of doppler bins to filter (default only 0Hz)
Returns:
srvChannelFiltered: Surveillance channel signal with clutter removed
'''
srvChannelFiltered = srvChannel
for doppler in dopplerBins:
if doppler == 0:
srvChannelFiltered = LS_Filter_Toeplitz(refChannel,
srvChannelFiltered,
filterLen)
else:
refMod = frequency_shift(refChannel, doppler, sampleRate)
srvChannelFiltered = LS_Filter_Toeplitz(refMod, srvChannelFiltered,
filterLen)
return srvChannelFiltered
def clutter_removal(s1, s2, nlag, Fs, dbins=[0]):
'''clutter removal with least squares filter
Parameters:
s1: reference signal
s2: surveillance signal
nlag: length of least squares filter
Fs: input signal sample frequency
dbins: list of doppler bins to do filtering on (default 0)
returns: y (surveillance signal with static echoes removed)'''
y = s2
for ds in dbins:
if ds == 0:
y = LS_Filter(s1, y, nlag, 1)
else:
s1s = frequency_shift(s1, ds, Fs)
y = LS_Filter(s1s, y, nlag, 1)
return y
def channel_preprocessing(sig, dec, fc, Fs):
'''deinterleave IQ samples, tune to channel frequency and decimate'''
IQ = deinterleave_IQ(sig)
IQ_tuned = frequency_shift(IQ, fc, Fs)
IQd = decimate(IQ_tuned, dec)
return IQd