Date of Award
Master of Science
Department of Electrical and Computer Engineering
Stuart L. DeVilbiss
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.
DTIC Accession Number
White, Nathan A., "MMAE Detection of Interference/Jamming and Spoofing in a DPGS-Aided Inertial System" (1996). Theses and Dissertations. 5933.