An algorithm has been implemented in a CDMA cellular radio system to enable a 5 fold reduction in the stability requirement of the base station time reference oscillator. The algorithm adaptively models the frequency drift characteristics of the base station time reference OCXO whilst locked to a satellite time reference signal. If the satellite time reference is lost, the OCXO model is used to provide time correction of the base station reference oscillator for a holdover period of up to 24 hours during which repair or reacquisition of the satellite time reference signal is conducted. The novel algorithm uses two parallel Kalman filters to model adaptively the temperature and aging dependent frequency stability of the OCXO. The algorithm extracts the stability dependencies of the OCXO with respect to the noisy satellite time reference. Adaptive training of the Kalman filters occurs until satellite visibility is lost, and is re-initiated after the satellite time reference has been reacquired; thus, the algorithm is cognizant of changes in the OCXO frequency stability characteristics over its lifetime. In holdover, the Kalman filters operate as predictive state machines which generate a correction signal for the base station OCXO time reference based on the trained coefficients of the adaptive models. The correction algorithm has been trialed in a CDMA base station network and demonstrated to maintain the 10 MHz timing module reference oscillator to within 1.5 /spl mu/s of the CDMA system time over a holdover period of 24 hr, well within the 3GPP2 CDMA standard cumulative time error specification of 10 /spl mu/s over an 8 hr holdover period. Simulations indicate the feasibility of the algorithm to compensate for a further 10 fold reduction in reference oscillator stability whilst still meeting the 8 hr holdover specification.
[1]
R. L. Filler,et al.
Application of Kalman filtering techniques to the precision clock with non-constant aging
,
1992,
Proceedings of the 1992 IEEE Frequency Control Symposium.
[2]
Richard A. Brown,et al.
Introduction to random signals and applied kalman filtering (3rd ed
,
2012
.
[3]
S. R. Stein.
Kalman filter analysis of precision clocks with real-time parameter estimation
,
1989,
Proceedings of the 43rd Annual Symposium on Frequency Control.
[4]
W. J. Klepczynski,et al.
GLONASS common-view time transfer between North America and Europe and its comparison with GPS
,
1996
.
[5]
S. R. Stein,et al.
Kalman filter analysis for real time applications of clocks and oscillators
,
1988,
Proceedings of the 42nd Annual Frequency Control Symposium, 1988..
[6]
R. E. Kalman,et al.
A New Approach to Linear Filtering and Prediction Problems
,
2002
.
[7]
A. V. Savchuk,et al.
GPS-Based Optimal Kalman Estimation of Time Error, Frequency Offset, and Aging
,
1999
.
[8]
R. L. Filler,et al.
A new approach to clock modeling and Kalman filter time and frequency prediction
,
1993,
1993 IEEE International Frequency Control Symposium.