碩士 / 國立臺灣海洋大學 / 通訊與導航工程系 / 98 / In this thesis, application of the ultra-tight integration navigation algorithm using interacting multiple model (IMM) nonlinear filtering is studied for GPS/INS. The ultra-tight integration is also known as deep integration, which increases the receiver tracking bandwidth and suppresses noise, so as to promote GPS receiver performance. When the GPS signal losses, assistance of the aiding INS in the receiver's acquisition and re-acquisition process can still use the position, velocity on the delay lock loop (DLL) and phase lock loop (PLL) to promote the receiver’s tracking loop performance. Using the structure of ultra-tight in the receiver has many advantages, such as disturbance rejection and multi-path rejection, promoting high dynamic performance, tracking weak signals, improving the accuracy, urban or indoor positioning capability, shorten acquisition time, improved phase locked loop bandwidth, achieve a more accurate Doppler frequency shift and measurement of phase, etc.
The unscented Kalman filter (UKF) employ a set of sigma points through deterministic sampling, such that the linearization process is not necessary, and therefore the error caused by linearization as in the traditional extended Kalman filter (EKF) can be avoided. There is no need to evaluate the Jacobian matrix. The use of IMM, which describes a set of switching models, finally provides the suitable value of process noise covariance. Consequently, the resulting sensor fusion strategy can efficiently deal with the nonlinear problem in vehicle navigation. The proposed IMMUKF algorithm shows significant improvement in navigation estimation accuracy as compared to the UKF approaches.
| Identifer | oai:union.ndltd.org:TW/098NTOU5300036 |
| Date | January 2010 |
| Creators | Chia-Wei Hu, 胡家維 |
| Contributors | Dah-Jing Jwo, 卓大靖 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 110 |
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