Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-18T23:45:17.940Z Has data issue: false hasContentIssue false

Detecting RFI Through Integrity Monitoring at a DGPS Reference Station

Published online by Cambridge University Press:  23 August 2006

Youngsun Yun
Affiliation:
Seoul National University, Korea Email: [email protected]
Changdon Kee
Affiliation:
Seoul National University, Korea Email: [email protected]
Jason Rife
Affiliation:
Stanford University, USA
Ming Luo
Affiliation:
Stanford University, USA
Sam Pullen
Affiliation:
Stanford University, USA
Per Enge
Affiliation:
Stanford University, USA

Abstract

Because GPS is a radio navigation system which has a very low power level, it is vulnerable to RFI. Excessive RFI could cause receiver performance degradation, such as degradation of position accuracy, loss of lock and increased acquisition time. After GPS modernization plans introduce dual-frequency civil signals to mitigate ionospheric errors, RFI will remain as one of the dominant threats for differential GPS navigation systems. Examples of safety-critical civil aviation and military missions threatened by RFI include the Local Area Augmentation System (LAAS) and the Joint Precision Approach and Landing System (JPALS). This paper focuses on RFI mitigation through integrity monitoring for a DGPS system like LAAS or JPALS. The mitigation strategy consists of two parts. First, the paper develops a new RFI detection method, using a raw divergence statistic. Second, the paper investigates strategies for maintaining integrity in the case that RFI is detected.

To validate the utility of the divergence-based RFI monitor, this paper takes an experimental approach. The experiments assess the performance of the divergence metric and compare it to existing alternatives for RFI detection, such as metrics for Automatic Gain Control (AGC) and carrier-to-noise ratio (C/N0). Generating a monitoring threshold for these statistics proves challenging, because the threshold depends both on the type of RFI threat (e.g. continuous wave, narrow band, wideband, pulsed) and on environmental conditions, such as temperature. As experiments illustrate, the divergence statistic resolves these limitations, as divergence directly estimates ranging source error, independent of the type of RFI threat or the environmental conditions.

Type
Research Article
Copyright
Copyright © The Royal Institute of Navigation 2006

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

Bastide, F., Akos, D., Macabiau, C. and Roturier, B. (2003). Automatic Gain Control (AGC) as an Interference Assessment Tool. Proceedings of the Institute Of Navigation GPS 2003, Portland, OR.Google Scholar
Braff, R. and Shively, C. (2005). A Method of Over Bounding Ground Based Augmentation System (GBAS) Heavy Tail Error Distributions. The Journal of Navigation, 58, 83103.CrossRefGoogle Scholar
Lee, J., Pullen, S., Xie, G. and Enge, P. (2001). LAAS Sigma-Mean Monitor Analysis and Failure-Test Verification. Proceedings of the Institute Of Navigation 57TH Annual Meeting, Albuquerque, NM.777Google Scholar
Luo, M., Pullen, S., Zhang, J., Gleason, S., Xie, G., Yang, J., Akos, D. and Enge, P. (2000). Development and Testing of the Stanford LAAS Ground Facility Prototype. Proceedings of the Institute Of Navigation National Technical Meeting, Anaheim, CA.Google Scholar
Luo, M., Xie, G., Akos, D., Pullen, S. and Enge, P. (2003). Radio Frequency Interference Validation Testing for LAAS using the Stanford Integrity Monitor Testbed. Proceedings of the Institute Of Navigation National Technical Meeting, Anaheim, CA.Google Scholar
McGraw, G. A., Murphy, T., Brenner, M., Pullen, S. and Van Dierendonck, A. J. (2000). Development of the LAAS Accuracy Models. Proceedings of the Institute Of Navigation GPS 2000, Salt Lake City, UT.Google Scholar
Ndili, A. and Enge, P. (1998). GPS Receiver Autonomous Interference Detection. Proceedings of the Institute of Electrical and Electronics Engineers Position Location and Navigation Symposium ’98, Palm Springs, CA.Google Scholar
Normark, P. L., Xie, G., Akos, D., Pullen, S., Luo, M., Lee, J. and Enge, P. (2001). The Next Generation Integrity Monitor Testbed (IMT) for Ground System Development and Validation Testing. Proceedings of the Institute Of Navigation GPS 2001, Salt Lake City, UT.Google Scholar
Pullen, S., Luo, M., Gleason, S., Xie, G., Lee, J., Akos, D., Enge, P. and Pervan, B. (2000). GBAS Validation Methodology and Test Results from the Stanford LAAS Integrity Monitor Testbed. Proceedings of the Institute Of Navigation GPS 2000, Salt Lake City, UT.Google Scholar
Pullen, S., Lee, J., Xie, G. and Enge, P. (2003). CUSUM-Based Real-Time Risk Metrics for Augmented GPS and GNSS. Proceedings of the Institute Of Navigation GPS 2003, Portland, OR.Google Scholar
Xie, G., Pullen, S., Luo, M., Normark, P. L., Akos, D., Lee, J. and Enge, P. (2001). Integrity Design and Updated Test Results for the Stanford LAAS Integrity Monitor Testbed. Proceedings of the Institute Of Navigation 57TH Annual Meeting, Albuquerque, NM.Google Scholar
Xie, G. (2004). Optimal On-Airport Monitoring of the Integrity of GPS-based Landing Systems. A Stanford University Ph.D. Dissertation.Google Scholar