The impact of Balanced Model Truncation (BMT) resulted from unique, groundbreaking work undertaken at the University of Westminster’s Applied DSP and VLSI Research Group, started in the early/mid 1990s.
This work has lead to a number of significant contributions underpinning the development and commercial exploitation by industry of power-efficient and complexity-reduced integrated Digital Signal Processing (DSP) systems and products.
At the heart of the findings, relating to the impact claimed in this case study, is the ability to rapidly and painlessly design a DSP system that comprises digital filters from a finite impulse response (FIR) start point – with very high dimensionality – that delivers an equivalent performance with a very low dimensionality through the novel deployment of the BMT approach.
Work in the commercial exploitation and use of this approach continues to date with the most recent being an applied research contract from the European Space Agency in partnership with Astrium UK Ltd. That work started in September 2011 and involved the deployment of BMT derived complexity reduced IIR filters in their onboard digital signal processing engines on the commercial communication satellites to notably reduce power consumption and area.
The approach was generalised from just being a humble filter design strategy to one that could take a signal or system that was defined by a very high dimensionality time series, to one that was able to recreate the original time series from a much reduced complexity model that encapsulated all the characteristics of the original time series. This approach continues to open the way to many novel practical applications for commercial products.
This work started as a purely theoretical exercise in deriving an equivalent IIR model for an FIR filter and ended up being a very powerful, practical approach that is capable of solving a plethora of problems in real-life systems, holding its currency and popularity to date.
One outcome of the work is an example of the BMT approach being deployed in a sound analysis/synthesis scene. This led to the use of the approach for the generation of compact and superior models for musical instruments, notably percussive sounds. This has found its way into use on commercial synthesisers. This work has also led to the deployment of the approach as an alternative to Linear Prediction in speech coding and efficient motor car acoustic response modelling, hearing aids, binaural headsets and Head Related Transfer Functions (HRTF).
The BMT approach has many wide ranging applications from hearing aids to binaural headsets and more recently making the DSP onboard satellites more efficient.Professor Izzet Kale, University of Westminster
Supported by: EPSRC