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I'm trying to learn some signal processing and recorded some iq data from the meteor satellite which transmits data with qpsk. I read the file with python and tried applying a rrcos filter on the signal.

sample_rate = 1152000
file = open("file", "rb")

data = []
for i in range(10000):
    point = file.read(8)
    real, img = struct.unpack("ff", point)
    data.append(real)

# https://de.wikipedia.org/wiki/Root-Raised-Cosine-Filter (code at the bottom)
filter = rrcos(len(data), 0.22, 1.38e-5, sample_rate)
# (number of datapoints, rolloff factor, symbol period, sample rate)

new_data = np.convolve(filter, signal)

The FFT plot for the recorded signal looks like this (signal of the sattelite on the left):
fft recorded signal

and for the "filtered" signal like this: fft filtered

The weird thing is that the filtered signal in a time/amplitude plot when using the numpy convolution function seems to have cut off the start and the end:

I implemented the function myself which seems to "fix" that:

The filtered data doesn't really look like a qpsk signal and i don't really know what i should google to fix that. All examples i found just use convolution to apply the filter to the signal.

Does anyone know whats going on?

Thanks

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1 Answer 1

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The reason you're getting lots of zeros is because you're making the RRC filter way too long. If I plot it:

import matplotlib.pyplot as plt
sample_rrc  =  rrcosfilter (10000,0.22, 1.38e-5, 1152000)
plt.plot(sample_rrc)

then I get

Raised Cosine Impulse Response

which is clearly mostly zero.

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