Signal model and observation model are commonly used to describe a dynamic system model in system identification or estimation such as Kalman filtering. The signal model is usually described by a linear dynamical equation driven by generating noise. The observation model is composed of a linear transformed signal and an additive white Gaussian noise. In this thesis, we set the generating noise to be a white binary sequence.
This discrete generating noise makes the generating signal to be discrete. In contrast, the conventional generating signal is continuous. Discrete signal is simpler than the continuous signal. However, there still are too many states for this discrete signal. Therefore, defining the states and reducing the number of states are important in our work. In this thesis, we apply the tree structure to define the states. The number of states is reduced by focusing on the most probable working states. Afterwards, we apply two methods to recover the white sequence using the observation data. One is the Viterbi method; the other is Extended Kalman filter. Both methods are based upon the concept of signal states. Finally, we compare the error rates with the signal generated by continues phase modulation method.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0831106-170208 |
| Date | 31 August 2006 |
| Creators | Tsai, Wen-Jung |
| Contributors | Tsung Lee, Shie-Jue Lee, Ben-Shung Chow, Chin-Hsing Chen |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | Cholon |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0831106-170208 |
| Rights | not_available, Copyright information available at source archive |
Page generated in 0.0023 seconds