Spelling suggestions: "subject:"generating aignal"" "subject:"generating asignal""
1 |
Generating Signal by Trellis and Study on its RecoveryTsai, Wen-Jung 31 August 2006 (has links)
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.
|
2 |
Distance Spectrum Computation for Generating SignalLi, Ding-Chien 09 September 2011 (has links)
In this thesis, we compute the distance spectrum for non-causal generating signals
and compare the different spectrum effects for different non-causal systems.
The non-causal system is the system which the present output is determined by the
future and the past. The distance spectrum is the list of the difference measures of any
two signals and search through all the possible event paths by trellis as much as
possible.
We use the method of the line difference to compute the distance spectrum of
non-causal generating signal systems by defining the line and the line difference to find
the distance for every pair of signals. Using this method, we have computed the distance
spectrum for non-causal generating signals. Finally, we compare the different spectrums
for different non-causal systems of different coefficients.
|
Page generated in 0.0621 seconds