Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-11-22T23:42:45.888Z Has data issue: false hasContentIssue false

Generalised DOPs with Consideration of the Influence Function of Signal-in-Space Errors

Published online by Cambridge University Press:  14 October 2011

Yuanxi Yang*
Affiliation:
(China National Administration of GNSS and Applications, Beijing 100088, China)
Jinlong Li
Affiliation:
(Institute of Surveying and Mapping, Information Engineering University, Zhengzhou 450052, China)
Junyi Xu
Affiliation:
(Institute of Surveying and Mapping, Information Engineering University, Zhengzhou 450052, China)
Jing Tang
Affiliation:
(China National Administration of GNSS and Applications, Beijing 100088, China)
*

Abstract

Integrated navigation using multiple Global Navigation Satellite Systems (GNSS) is beneficial to increase the number of observable satellites, alleviate the effects of systematic errors and improve the accuracy of positioning, navigation and timing (PNT). When multiple constellations and multiple frequency measurements are employed, the functional and stochastic models as well as the estimation principle for PNT may be different. Therefore, the commonly used definition of “dilution of precision (DOP)” based on the least squares (LS) estimation and unified functional and stochastic models will be not applicable anymore. In this paper, three types of generalised DOPs are defined. The first type of generalised DOP is based on the error influence function (IF) of pseudo-ranges that reflects the geometry strength of the measurements, error magnitude and the estimation risk criteria. When the least squares estimation is used, the first type of generalised DOP is identical to the one commonly used. In order to define the first type of generalised DOP, an IF of signal–in-space (SIS) errors on the parameter estimates of PNT is derived. The second type of generalised DOP is defined based on the functional model with additional systematic parameters induced by the compatibility and interoperability problems among different GNSS systems. The third type of generalised DOP is defined based on Bayesian estimation in which the a priori information of the model parameters is taken into account. This is suitable for evaluating the precision of kinematic positioning or navigation. Different types of generalised DOPs are suitable for different PNT scenarios and an example for the calculation of these DOPs for multi-GNSS systems including GPS, GLONASS, Compass and Galileo is given. New observation equations of Compass and GLONASS that may contain additional parameters for interoperability are specifically investigated. It shows that if the interoperability of multi-GNSS is not fulfilled, the increased number of satellites will not significantly reduce the generalised DOP value. Furthermore, the outlying measurements will not change the original DOP, but will change the first type of generalised DOP which includes a robust error IF. A priori information of the model parameters will also reduce the DOP.

Type
Research Article
Copyright
Copyright © The Royal Institute of Navigation 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Doong, S. H. (2009). A closed-form formula for GPS GDOP computation. GPS Solutions, 13, 183190.CrossRefGoogle Scholar
Feng, Y. (2008). GNSS three carrier ambiguity resolution using ionosphere-reduced virtual signals. Journal of Geodesy, 82(12), 847862.CrossRefGoogle Scholar
Feng, Y. and Li, B. (2008). A benefit of multiple carrier GNSS signals: regional scale network-based RTK with doubled inter-station distances. Journal of Spatial Sciences, 53(1), 135147.CrossRefGoogle Scholar
Feng, Y. and Rizos, C. (2009). Network-based geometry-free three carrier ambiguity resolution and phase bias calibration. GPS Solutions, 13, 4356.CrossRefGoogle Scholar
Hampel, F. R., Ronchetti, E. M., Rousseeuw, P. J. and Stahel, W. A. (1986). Robust Statistics: the Approach Based on Influence Functions. John Wiley, New York.Google Scholar
Huber, P. J. (1981). Robust Statistics. John Wiley, New York.CrossRefGoogle Scholar
Kelly, R. J. (1990). Reducing geometric dilution of precision using ridge regression. IEEE Transactions on Aerospace and Electronic Systems, 26(1), 154168.CrossRefGoogle Scholar
Koch, K. R. (1987). Parameter Estimation and Hypothesis Testing in Linear Models. Springer, Berlin Heidelberg, New York.Google Scholar
Koch, K. R. (1990). Bayesian Inference with Geodetic Applications. Springer, Berlin Heidelberg, New York.Google Scholar
Langley, R. B. (1999). Dilution of precision. GPS World, 10(5), 5259.Google Scholar
Leva, J. L. (1994). Relationships between navigation vertical error, VDOP, and pseudo-range error in GPS. IEEE Transactions on Aerospace Electronic Systems, 30(4), 11381142.CrossRefGoogle Scholar
Li, B., Shen, Y. and Feng, Y. (2010). Fast GNSS ambiguity resolution as an ill-posed problem. Journal of Geodesy, 84(11), 683698.CrossRefGoogle Scholar
Milbert, D. (2008). Dilution of precision revisited. NAVIGATION, Journal of The Institute of Navigation, 55(1), 6781.CrossRefGoogle Scholar
Milbert, D. (2009). Improving dilution of precision, a companion measure of systematic effects. GPS World, 20(11), 3847.Google Scholar
Odijk, D. and Teunissen, P. J. G. (2008). ADOP in closed form for a hierarchy of multi-frequency single-baseline GNSS models. Journal of Geodesy, 82, 473492.CrossRefGoogle Scholar
Park, C. and Kim, I. (2000). Comments on “Relationship between dilution of precision for point positioning and for relative positioning with GPS”. IEEE Transactions on Aerospace Electronic Systems, 36(1), 315316.CrossRefGoogle Scholar
Parkinson, B. W. and Spilker, J. J. (1996). Global Positioning System: Theory and Applications, vol. II. American Institute of Aeronautics and Astronautics, Inc., Cambridge, MA, USA.Google Scholar
Sairo, H., Akopian, D. and Takala, J. (2003). Weighted dilution of precision as quality measure in satellite positioning. IEE Proc.-Radar Sonar Navig, 150(6), 430436.CrossRefGoogle Scholar
Shen, Y., Li, B. and Chen, Y. (2010). An iterative solution of weighted total least-squares adjustment. Journal of Geodesy, 85(4), 229238.CrossRefGoogle Scholar
Teunissen, P. J. G. (1990). Quality control in integrated navigation systems. IEEE Aerospace and Electronics Systems, 5(7), 3541.CrossRefGoogle Scholar
Teunissen, P. J. G. and Odijk, D. (1997). Ambiguity dilution of precision: definition, properties and application. In: Proc of ION GPS-1997, Kansas City, September 16–19, 891899.Google Scholar
Teunissen, P. J. G. and Kleusberg, A. (1999). GPS for Geodesy, 2nd Edition. Springer, Berlin Heidelberg, New York.Google Scholar
Xu, P. L., Fukuda, Y. and Liu, Y. M. (2006). Multiple parameter regularization: numerical solutions and applications to the determination of geopotential from precise satellite orbits. Journal of Geodesy, 80(1), 1727.CrossRefGoogle Scholar
Xu, P. (2009). Iterative generalized cross-validation for fusing heteroscedastic data of inverse ill-posed problems. Geophysical Journal International, 179(1), 182200.CrossRefGoogle Scholar
Yang, Y. (1991). Robust Bayesian Estimation. Bulletin Géodésique, 65(3), 145150.Google Scholar
Yang, Y. (1997). Estimators of covariance matrix at robust estimation based on influence functions. Zeitschrift für Vermessungswesen, 122(4), 166174.Google Scholar
Yang, Y., Song, L. and Xu, T. (2002). Robust estimator for correlated observations based on bifactor equivalent weights. Journal of Geodesy, 76, 353358.CrossRefGoogle Scholar
Yarlagadda, R., Ali, I., Al-Dhahire, N. and Hershey, J. (2000). GPS GDOP metric. IEE Proc.-Radar Sonar Navig, 147(5), 259264.CrossRefGoogle Scholar