When the measurement errors in Kalman filtering are time

correlated, time-differencing approaches are conventionally

applied to deal with the time-correlated errors, but they are

subject to practical limitations, such as time latency and

numerical issues that stem from matrix inversion. This paper

proposes two new algorithms to resolve the issues by augmenting