Adaptive Filter. This script shows the BER performance of several types of equalizers in a static channel with a null in the passband. The script constructs and implements a linear equalizer object and a decision feedback equalizer (DFE) object. It also initializes and invokes a maximum likelihood sequence estimation (MLSE) equalizer. The MLSE equalizer is first invoked with perfect channel knowledge, then with a straightforward but imperfect channel estimation technique.
This template file is used to completely describe a system in a generalized
％ state space format useable by the ReBEL inference and estimation system.
％ This file must be copied, renamed and adapted to your specific problem. The
％ interface to each function should NOT BE CHANGED however.
Computes BER v EbNo curve for convolutional encoding / soft decision
Viterbi decoding scheme assuming BPSK.
Brute force Monte Carlo approach is unsatisfactory (takes too long)
to find the BER curve.
The computation uses a quasi-analytic (QA) technique that relies on the
estimation (approximate one) of the information-bits Weight Enumerating
Function (WEF) using
A simulation of the convolutional encoder. Once the WEF is estimated, the analytic formula for the BER is used.
This project aim was to build wireless software modem for data communication
between two computers using an acoustic interface in the voice frequency range (20Hz–
20,000Hz). The transmitting antenna is a speaker (frequency response of: 90Hz –
20,000Hz) and the receiving antenna is a microphone (frequency response of: 100Hz –
16,000Hz). The test files used as information files were text files.
This goal was attained both in an incoherent scheme and in a coherent scheme.
Build under Matlab code, our modem uses OFDM (orthogonal frequency division
multiplexing) modulation, synchronization by LMS sequence, channel estimation (no
equalizer) via pilot tones. The symbols are either PSK or ASK for a constellation size of
2 or 4. To optimize the probability of error, these symbols were mapped using Gray
mapping.
Report
On-Line MCMC Bayesian Model Selection
This demo demonstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space estimation with Neural Networks and the EM algorithm After downloading the file, type "tar -xf EMdemo.tar" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type "EMtremor". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets.
This demo nstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
runs Kalman-Bucy filter over observations matrix Z
for 1-step prediction onto matrix X (X can = Z)
with model order p
V = initial covariance of observation sequence noise
returns model parameter estimation sequence A,
sequence of predicted outcomes y_pred
and error matrix Ey (reshaped) for y and Ea for a
along with inovation prob P = P(y_t | D_t-1) = evidence
Kalman filter toolbox written by Kevin Murphy, 1998.
See http://www.ai.mit.edu/~murphyk/Software/kalman.html for details.
Installation
------------
1. Install KPMtools from http://www.ai.mit.edu/~murphyk/Software/KPMtools.html
3. Assuming you installed all these files in your matlab directory, In Matlab type
addpath matlab/KPMtools
addpath matlab/Kalman
Demos
-----
See tracking_demo.m for a demo of 2D tracking.
See learning_demo.m for a demo of parameter estimation using EM.
較早版本的kalman濾波matlab源碼，適合研讀。
