The need for accurate monitoring and analysis of sequential data arises in many scientic, industrial
and nancial problems. Although the Kalman lter is effective in the linear-Gaussian
case, new methods of dealing with sequential data are required with non-standard models.
Recently, there has been renewed interest in simulation-based techniques. The basic idea behind
these techniques is that the current state of knowledge is encapsulated in a representative
sample from the appropriate posterior distribution. As time goes on, the sample evolves and
adapts recursively in accordance with newly acquired data. We give a critical review of recent
developments, by reference to oil well monitoring, ion channel monitoring and tracking
problems, and propose some alternative algorithms that avoid the weaknesses of the current
methods.
Using Gaussian elimination to solve linear equations.
// In this version, we allow matrix of any size. This is done by treating
// the name of a 2-dimensional array as pointer to the beginning of the
// array. This makes use of the fact that arrays in C are stored in
// row-major order.
Analytical constant-modulus algorithm, to separate linear combinations of CM sourcesThe algorithm
is robust in the presence of noise, and is tested on measured data,
collected from an experimental set-up.
