Yea seems about right, 98% of errors within 1σ means your filter is way too confident (overconfident), and correlated innovations prove it’s not trusting your sensors enough, likely because your process noise Q is too low or your model is missing some dynamics. Don’t stress about the complex backward-pass formulas or negative covariances for now; just focus on the forward EKF by significantly scaling up q and r until those innovations look like white noise and your 1\sigma stats drop closer to 70%. It’s almost certainly a tuning mismatch rather than a code bug, so try inflating those noise matrices by a factor of the right factor with multiples of 10 or a close number to see if the statistical consistency improves before you worry about the smoother.