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Improve MUSIC implementation

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Christos Falas 2024-12-28 10:18:46 +00:00
parent 4e72d92b09
commit 3199103caa
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2 changed files with 24 additions and 9 deletions
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3
pyproject.toml
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@ -4,3 +4,6 @@ line-length = 88
[tool.pyright] [tool.pyright]
typeCheckingMode = "strict" typeCheckingMode = "strict"
reportMissingTypeStubs = "warning" reportMissingTypeStubs = "warning"
[tool.pytest.ini_options]
python_files = "*.py"

30
src/aoa.py
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@ -50,6 +50,8 @@ class AoA:
H_sm = self.smooth(data) H_sm = self.smooth(data)
auto_corr = np.matmul(H_sm, np.conj(H_sm).T) auto_corr = np.matmul(H_sm, np.conj(H_sm).T)
# This matrix is by definition Hermitian.
# Therefore, all of its eigenvectors are orthogonal.
if self.historical_autocorr.size == 0: if self.historical_autocorr.size == 0:
self.historical_autocorr = np.expand_dims(auto_corr, 0) self.historical_autocorr = np.expand_dims(auto_corr, 0)
@ -62,15 +64,14 @@ class AoA:
if self.historical_autocorr.shape[0] > WINDOW_SIZE: if self.historical_autocorr.shape[0] > WINDOW_SIZE:
self.historical_autocorr = self.historical_autocorr[-WINDOW_SIZE:] self.historical_autocorr = self.historical_autocorr[-WINDOW_SIZE:]
# Is the moving average also Hermitian?
R = np.mean(self.historical_autocorr, axis=0) R = np.mean(self.historical_autocorr, axis=0)
RR_h = np.matmul(R, np.conj(R).T)
# This matrix is by definition Hermitian.
# Therefore, all of its eigenvectors are orthogonal.
# The smallest eigenvectors span the noise subspace, # The smallest eigenvectors span the noise subspace,
# and the largest span the signal subspace. # and the largest span the signal subspace.
eigvals, eigvecs = np.linalg.eig(RR_h) eigvals, eigvecs = np.linalg.eigh(R)
self.E_n = eigvecs[:, eigvals < config.EIGVAL_THRESHOLD] print(eigvals)
self.E_n = eigvecs[:, np.abs(eigvals) < config.EIGVAL_THRESHOLD]
omega_base = np.exp(-2j * np.pi * config.DELTA_F) omega_base = np.exp(-2j * np.pi * config.DELTA_F)
phi_base = np.exp( phi_base = np.exp(
@ -89,9 +90,8 @@ class AoA:
antenna_v = omega_t ** np.arange(self.N_subcarriers // 2) antenna_v = omega_t ** np.arange(self.N_subcarriers // 2)
phis = phi_theta ** np.arange(self.N_rx) phis = phi_theta ** np.arange(self.N_rx)
antenna_v = np.expand_dims(antenna_v, axis=-1) antenna_v = np.expand_dims(antenna_v, axis=-1)
phis = np.expand_dims(phis, axis=-2)
steering = antenna_v * phis steering = antenna_v * phis
return steering.reshape(-1) return steering.T.reshape(-1)
def evaluate(self, theta: float, tof: float): def evaluate(self, theta: float, tof: float):
try: try:
@ -101,9 +101,9 @@ class AoA:
logger.exception(e) logger.exception(e)
return 0 return 0
E_n = self.E_n E_n = self.E_n
E_n_H = np.conj(self.E_n).T E_n_H = np.conj(E_n).T
c = 1 / (0.001 + (steering_h @ E_n @ E_n_H @ steering)) c = 1 / (0.001 + (steering_h @ E_n @ E_n_H @ steering))
return c.real return np.abs(c.real)
def test_smoothing(): def test_smoothing():
@ -118,3 +118,15 @@ def test_smoothing():
print(expected) print(expected)
assert np.allclose(smoothed, expected) assert np.allclose(smoothed, expected)
pass pass
def test_steering_vector():
aoa = AoA()
aoa.N_subcarriers = 10
aoa.N_rx = 2
print(aoa.omega_base)
print(aoa.phi_base)
tau = 1
theta = 0
print(aoa.steering_vector(theta, tau))
assert False