Power law noise identification using the LAG 1 autocorrelation by overlapping samples
There are various random errors in the fiber optical gyroscope (FOG) output signal. At the aim of improving its accuracy, it is need to identify the kinds of errors. The most common method for power law noise identification is simply to observe the slope of a log-log plot of the Allan or modified Allan deviation versus averaging time, either manually or by fitting a line to it. The lag 1 autocorrelation method is a new method for power law noise identification that can determine the dominant noise type for all common noise processes, from phase or frequency data, for all averaging factors, in a consistent and analytic manner. This paper describes an improvement of it by overlapping samples, which improves the confidence of the resulting stability estimate at the expense of greater computational time.
POWER LAW NOISE IDENTIFICATION USING THE LAG 1 AUTOCORRELATION
This paper describes a new method for power law noise identification, based on the lag 1 autocorrelation function, that can determine the dominant noise type for all common noise processes, from phase or frequency data, for all averaging factors, in a consistent and analytic manner.
A total estimator of the Hadamard function used for GPS operations
Abstract : We describe a method based on the Total deviation approach whereby we improve the confidence of the estimation of the Hadamard deviation that is used primarily in GPS operations. The Hadamard-total deviation described in this paper provides a significant improvement in confidence indicated by an increase of 1.3 to 3.4 times the one degree of freedom of the plain Uadamard deviation at the longest averaging time. The new Hadamard-total deviation is slightly negativdy biased with respect to the usual Uadamard deviation, and T values are restricted to less than or equal to T/3, to be consistent with the usual Uadamard's definition. We give a method of automatically removing bias by a power-law detection scheme. We review the relationship between Kalman fiber parameters and the Hadamard and Allan variances, illustrate the operational problems associated with estimating these parameters, and discuss how the Uadamard-total variance can improve management of present and future GPS satellite clocks.