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Add Butterworth bandpass filter

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Christos Falas 2024-12-28 10:17:03 +00:00
parent a7a34a6138
commit 4e72d92b09
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2 changed files with 49 additions and 23 deletions
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18
src/config.py
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@ -2,6 +2,10 @@ import os
PREPROCESSING_SHORT_TERM_WINDOW_SIZE = 5
PREPROCESSING_LONG_TERM_ALPHA = 0.01
PREPROCESSING_BANDPASS_LOW_CUTOFF = 2
PREPROCESSING_BANDPASS_HIGH_CUTOFF = 40
AOA_SLIDING_WINDOW_SIZE = 20
RECEIVE_IP_ADDRESS = os.getenv("IP_ADDRESS", "10.0.12.64")
@ -10,18 +14,16 @@ FEITCSI_PORT = 8008
SAMPLE_RATE = 100 # Hz
EIGVAL_THRESHOLD = 1e4
EIGVAL_THRESHOLD = 1000
# DELTA_F = 78_125 # Spacing between subcarriers in Hz
DELTA_F = 312_500 # Spacing between subcarriers in Hz
CENTRAL_FREQUENCY_MHZ = 6195
CENTRAL_FREQUENCY_HZ = CENTRAL_FREQUENCY_MHZ * 1_000_000
ANTENNA_SPACING = 0.0285
# ANTENNA_SPACING = 0.0285 * 3
CENTRAL_FREQUENCY_MHZ = 5220
ANTENNA_SPACING = 0.0285 # 2.85 cm
CHANNEL_WIDTH = 20
FRAME_FORMAT = "HT"
C = 299_792_458
CENTRAL_FREQUENCY_HZ = CENTRAL_FREQUENCY_MHZ * 1_000_000
C = 299_792_458 # m/s

54
src/preprocess.py
View File

@ -5,6 +5,7 @@ from queue import Queue
from . import config
import numpy.typing as npt
from scipy.signal import correlate
from scipy.signal import butter, sosfilt_zi, sosfilt
logger = logging.getLogger(__name__)
@ -18,29 +19,52 @@ class Preprocessor:
self.prev_entries: Queue[npt.NDArray[np.complex128]] = Queue(maxsize=100)
self.short_term_avg = np.zeros((1,), dtype=np.complex128)
self.long_term_avg = np.zeros((1,), dtype=np.complex128)
self.filter = butter(
5,
[
config.PREPROCESSING_BANDPASS_LOW_CUTOFF,
config.PREPROCESSING_BANDPASS_HIGH_CUTOFF,
],
fs=config.SAMPLE_RATE,
btype="band",
output="sos",
)
def preprocess(self, csi: CSI):
h = csi.matrix
h_hat = np.multiply(h, h.conj() / abs(h.conj()))
# CSI data is not available for pilot subcarriers.
h_hat = np.where(
np.expand_dims(h[:, 0, 0] == 0, axis=(1, 2)),
correlate(h, [[[1 / 2]], [[0]], [[1 / 2]]], mode="same"),
h,
)
h_hat = np.multiply(h_hat, h_hat.conj() / abs(h_hat.conj()))
h_hat = np.nan_to_num(h_hat)
# h_hat = correlate(h_hat, np.ones((3, 1, 1)) / 3)
h_hat = correlate(h_hat, [[[1 / 4]], [[1 / 2]], [[1 / 4]]], mode="valid")
# Assume that all csi matrices will have the same shape
if self.short_term_avg.shape != h_hat.shape:
self.short_term_avg = np.zeros(h_hat.shape, dtype=np.complex128)
if self.long_term_avg.shape != h_hat.shape:
self.long_term_avg = np.zeros(h_hat.shape, dtype=np.complex128)
self.prev_entries = Queue(maxsize=100)
while self.prev_entries.full():
old_value = self.prev_entries.get()
self.short_term_avg -= (
old_value / config.PREPROCESSING_SHORT_TERM_WINDOW_SIZE
)
self.prev_entries.put(h_hat)
self.short_term_avg += h_hat / config.PREPROCESSING_SHORT_TERM_WINDOW_SIZE
self.long_term_avg = (
self.long_term_avg * (1 - config.PREPROCESSING_LONG_TERM_ALPHA)
+ self.short_term_avg * config.PREPROCESSING_LONG_TERM_ALPHA
+ h_hat * config.PREPROCESSING_LONG_TERM_ALPHA
)
# Remove static components
current_measurement = self.short_term_avg - self.long_term_avg
return current_measurement
# Remove long term average, to remove static paths
h_hat -= self.long_term_avg
# Apply bandpass filter to remove low and high frequency noise
if not hasattr(self, "filter_zi"):
self.filter_zi = (
np.expand_dims(sosfilt_zi(self.filter), axis=(-1, -2, -3)) * h_hat
)
h_hat_filt, self.filter_zi = sosfilt(
self.filter, [h_hat], zi=self.filter_zi, axis=0
)
return h_hat_filt[0]