Prof. Brett Ninness


Some current projects.

System Identification

Theoretical and empirical study of various problems in system identification. Particular attention is paid to robust estimation of Multivariable and Nonlinear systems, and to error quantification.


System Identification Toolbox

This toolbox is a MATLAB-based software package for the estimation of dynamic systems.

A wide range of standard estimation approaches are supported. These include the use of non-parametric, subspace-based and prediction-error algorithms coupled (in the latter case) with either MIMO state space or MISO polynomial model structures.

Additionally, some new approaches are included. These include the support for bilinear and other Hammerstein-Wiener non-linear structures, and the use of the expectation-maximisation (EM) algorithm for time and frequency domain estimation of state space structures.

MIMO Communications Testbed

This is a hardware device designed to be used in the design and testing of wireless MIMO communications systems. It is connected to a PC via USB 2.0 or ethernet and uses an on-board FPGA to allow implementation of algorithms in logic, together with provision for multiple radio modules.

MCMC System Identification

Markov Chain Monte-Carlo methods are used to calculate probability density functions for parameters in dynamic systems models. By virtue of computation of the true posterior density, these methods allow accurate quantification of estimation error, even for short data lengths.

MCMC MIMO Detection

Details Pending.

MCMC Multi-User Detection

The application of Metropolis-Hastings and Gibbs Sampling algorithms to CDMA Multi-User Detection. This approach offers near-maximum likelihood detection with soft-outputs. This project investigates the computational feasibility of this approach.

Future Wireless

Orthgonal Frequency Division Multiplexing (OFDM) is core to emerging and future wireless systems. Of note, 802.16, 802.20 and 3GPP LTE all depend upon OFDM. The goal of this project is to generate core expertise in this area, publish in leading conferences and journals while securing valuable IP for the project participants. Numerous ASIC prototypes will result.


QPC - Quadratic Programming in C

This project offers a collection of software routines for solving quadratic programming problems that can be written in this form

x* = arg min 0.5x'Hx + f'x convex cost
s.t. Ax = b, linear equality constraints,
Lx <= k, general linear inequality constraints,
l <= x <= u, bound constraints.
The routines are written in C and callable from Matlab using the standard syntax. State-of-the-art solvers are available.

Filtering and Smoothing

This project offers a suite of software routines that run under Matlab, which perform various signal filtering and smoothing operations. This includes standard Kalman filtering and Kalman smoothing routines.

Variance Quantification

This project develops quantifications for the frequency domain variance of prediction error system estimates. A key theme is to derive new approximations offering improved accuracy via the principles of reproducing kernel principles and orthonormal parametrizations.
Team Members: Prof. Brett Ninness

Wiener Hammerstein Benchmark

Details of our attempt at the Wiener-Hammerstein Benchmark problem

Maintained by Prof. Brett Ninness
University of Newcastle
27 Jun 2008, © Copyright