> over time the Kalman filter would "learn" which sensor is lying and adjust its accuracy estimate down
No, it won't. The plain old Kalman filter believes precisely what you tell it. In this scenario, the Kalman filter will oscillate about the mean of the two measurements, with the amplitude of that oscillation depending on the relative sizes of the measurement variance and estimate variance. A smaller process noise will cause the estimate covariance to shrink faster, which will dampen the oscillation faster. The oscillation will eventually settle on some minimum amplitude.
An important point here that many don't realize is that the covariance of the Kalman filter is completely independent of the residual. Go ahead, look at the equations - covariance is a function of the measurement model, the prior covariance, and the measurement covariance. The actual measurement doesn't matter.
No, it won't. The plain old Kalman filter believes precisely what you tell it. In this scenario, the Kalman filter will oscillate about the mean of the two measurements, with the amplitude of that oscillation depending on the relative sizes of the measurement variance and estimate variance. A smaller process noise will cause the estimate covariance to shrink faster, which will dampen the oscillation faster. The oscillation will eventually settle on some minimum amplitude.
An important point here that many don't realize is that the covariance of the Kalman filter is completely independent of the residual. Go ahead, look at the equations - covariance is a function of the measurement model, the prior covariance, and the measurement covariance. The actual measurement doesn't matter.