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unwrap CSI phase

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Christos Falas 2025-02-24 14:59:49 +00:00
parent 61be734752
commit 28d096af44
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1 changed files with 32 additions and 4 deletions
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36
where_fi/processing/preprocess.py
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@ -1,11 +1,13 @@
import logging import logging
from queue import Queue from queue import Queue
from typing import Any, Callable
import numpy as np import numpy as np
import numpy.typing as npt import numpy.typing as npt
from scipy.signal import butter, correlate, sosfilt, sosfilt_zi from scipy.signal import butter, correlate, sosfilt, sosfilt_zi
from ..config import config from ..config import config
from ..visualise import server as visualise
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG) logger.setLevel(logging.DEBUG)
@ -26,9 +28,14 @@ class Preprocessor:
output="sos", output="sos",
) )
def preprocess(self, h: npt.NDArray[np.complex64]) -> npt.NDArray[np.complex64]: 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. # CSI data is not available for pilot subcarriers.
h_hat = np.where( h_hat: npt.NDArray[np.complex64] = np.where(
np.expand_dims(h[:, 0, 0] == 0, axis=(1, 2)), np.expand_dims(h[:, 0, 0] == 0, axis=(1, 2)),
correlate(h, [[[1 / 2]], [[0]], [[1 / 2]]], mode="same"), correlate(h, [[[1 / 2]], [[0]], [[1 / 2]]], mode="same"),
h, h,
@ -38,12 +45,33 @@ class Preprocessor:
h_hat = h_hat[:: config.preprocessing.subcarrier_step, :, :] h_hat = h_hat[:: config.preprocessing.subcarrier_step, :, :]
# logger.info(f"CSI shape: {h_hat.shape}") # logger.info(f"CSI shape: {h_hat.shape}")
h_hat = np.multiply(h_hat, h_hat.conj() / abs(h_hat.conj())) # h_hat = np.multiply(h_hat, h_hat.conj() / abs(h_hat.conj()))
h_hat = np.nan_to_num(h_hat) h_hat = np.nan_to_num(h_hat)
# h_hat = correlate(h_hat, np.ones((3, 1, 1)) / 3) # h_hat = correlate(h_hat, np.ones((3, 1, 1)) / 3)
h_hat = correlate(h_hat, [[[1 / 4]], [[1 / 2]], [[1 / 4]]], mode="valid") # 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 # Assume that all csi matrices will have the same shape
if self.long_term_avg.shape != h_hat.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 = np.zeros(h_hat.shape, dtype=np.complex64)