Use eigh instead of eig

The mean of Hermitian matrices is also Hermitian

where_fi/processing/aoa.py

@@ -113,11 +113,12 @@ class AoA:
 
     def evaluate(self, theta: torch.Tensor, tof: torch.Tensor) -> torch.Tensor:
         R = torch.mean(self.historical_autocorr, dim=0)
+        assert (R == torch.conj(R).T).all()
 
         # The smallest eigenvectors span the noise subspace,
         # and the largest span the signal subspace.
         logger.debug(f"Calculating eigenvectors of R: {R.shape}")
-        eigvals, eigvecs = torch.linalg.eig(R)
+        eigvals, eigvecs = torch.linalg.eigh(R)
         assert isinstance(eigvals, torch.Tensor)
         assert isinstance(eigvecs, torch.Tensor)
         logger.info(f"Eigenvalues: {eigvals}")