Date of Award


Document Type


Degree Name

Master of Science


Department of Electrical and Computer Engineering

First Advisor

Stuart L. DeVilbiss

Second Advisor

Peter Maybeck, PhD


Previous research at AFIT has resulted in the development of a DGPS-aided INS-based precision landing system (PLS) capable of meeting the FAA precision requirements for instrument landings. The susceptibility of DGPS transmissions to interference/jamming and spoofing must be addressed before DGPS may be used in such a safety-of-flight critical role. This thesis applies multiple model adaptive estimation (MMAE) techniques to the problem of detecting and identifying interference/jamming and spoofing failures in the DGPS signal. Such an MMAE is composed of a bank of parallel filters, each hypothesizing a different failure status, along with an evaluation of the current probability of each hypothesis being correct, to form a probability-weighted average output. Performance for a representative selection of navigation component cases is examined. For interference/jamming failures represented as increased measurement noise variance, results show that, because of the good FDI performance using MMAE, the blended navigation performance is essentially that of a single extended Kalman filter artificially informed of the actual interference noise variance. Standard MMAE is completely unable to detect spoofing failures (modelled as a bias or ramp offset signal directly added to the measurement). This thesis shows the development of a moving-bank pseudo-residual MMAE (PRMMAE) to detect and identify spoofing failures. Using the PRMMAE algorithm, the resulting navigation performance is equivalent to that of an extended Kalman filter operating in a no-fail environment.

AFIT Designator


DTIC Accession Number