98 lines
3.3 KiB
Python
98 lines
3.3 KiB
Python
import logging
|
|
from queue import Queue
|
|
from typing import Any, Callable
|
|
|
|
import numpy as np
|
|
import numpy.typing as npt
|
|
from scipy.signal import butter, correlate, sosfilt, sosfilt_zi
|
|
|
|
from ..config import config
|
|
from ..visualise import server as visualise
|
|
|
|
logger = logging.getLogger(__name__)
|
|
logger.setLevel(logging.DEBUG)
|
|
|
|
np.seterr(invalid="ignore")
|
|
|
|
|
|
class Preprocessor:
|
|
def __init__(self) -> None:
|
|
self.prev_entries: Queue[npt.NDArray[np.complex64]] = Queue(maxsize=100)
|
|
self.short_term_avg = np.zeros((1,), dtype=np.complex64)
|
|
self.long_term_avg = np.zeros((1,), dtype=np.complex64)
|
|
self.filter = butter(
|
|
5,
|
|
config.preprocessing.bandpass.bounds,
|
|
fs=config.sample_rate,
|
|
btype="band",
|
|
output="sos",
|
|
)
|
|
|
|
def preprocess(
|
|
self,
|
|
h: npt.NDArray[np.complex64],
|
|
visualiser: None
|
|
| Callable[[npt.NDArray[Any], visualise.DataType], None] = None,
|
|
) -> npt.NDArray[np.complex64]:
|
|
# CSI data is not available for pilot subcarriers.
|
|
h_hat: npt.NDArray[np.complex64] = np.where(
|
|
np.expand_dims(h[:, 0, 0] == 0, axis=(1, 2)),
|
|
correlate(h, [[[1 / 2]], [[0]], [[1 / 2]]], mode="same"),
|
|
h,
|
|
)
|
|
|
|
# Skip subcarrierss per config
|
|
h_hat = h_hat[:: config.preprocessing.subcarrier_step, :, :]
|
|
# logger.info(f"CSI shape: {h_hat.shape}")
|
|
|
|
# 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")
|
|
|
|
# Unwrap phase and remove linear fit
|
|
print(h_hat.shape)
|
|
unwrapped = np.unwrap(np.angle(h_hat[:, :, 0]), axis=0).reshape(
|
|
h_hat.shape[0], h_hat.shape[1], 1
|
|
)
|
|
if visualiser:
|
|
visualiser(unwrapped, visualise.DataType.UNWRAPPED_PHASE)
|
|
for antenna in range(h_hat.shape[1]):
|
|
tau, rho = np.linalg.lstsq(
|
|
np.vstack([np.arange(h_hat.shape[0]), np.ones(h_hat.shape[0])]).T,
|
|
unwrapped[:, antenna, 0],
|
|
)[0]
|
|
h_hat[:, antenna, 0] = np.abs(h_hat[:, antenna, 0]) * np.exp(
|
|
1j
|
|
* (
|
|
np.angle(h_hat[:, antenna, 0])
|
|
- (tau * np.arange(h_hat.shape[0]) + rho)
|
|
)
|
|
)
|
|
|
|
return h_hat
|
|
# Assume that all csi matrices will have the same shape
|
|
if self.long_term_avg.shape != h_hat.shape:
|
|
self.long_term_avg = np.zeros(h_hat.shape, dtype=np.complex64)
|
|
|
|
self.long_term_avg = (
|
|
self.long_term_avg * (1 - config.preprocessing.moving_average_alpha)
|
|
+ h_hat * config.preprocessing.moving_average_alpha
|
|
)
|
|
|
|
# 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]
|