Algorithms have been developed which perform least squares adaptive filtering with great computational efficiency. Unfortunately, the fast recursive least squares (RLS) algorithms all exhibit numerical instability due to finite precision computational errors, resulting in their failure to produce a useful solution after a short number of iterations. In this thesis, a new solution to this instability problem is considered, making use of interval arithmetic. By modifying the algorithm so that upper and lower bounds are placed on all quantities calculated, it is possible to obtain a measure of confidence in the solution calculated by a fast RLS algorithm and if it is subject to a high degree of inaccuracy due to finite precision computational errors, then the algorithm may be rescued, using a reinitialisation procedure. Simulation results show that the stabilised algorithms offer an accuracy of solution comparable with the standard recursive least squares algorithm. Both floating and fixed point implementations of the interval arithmetic method are simulated and long-term stability is demonstrated in both cases. A hardware verification of the simulation results is also performed, using a digital signal processor(DSP). The results from this indicate that the stabilised fast RLS algorithms are suitable for a number of applications requiring high speed, real time adaptive filtering. A design study for a very large scale integration (VLSI) technology coprocessor, which provides hardware support for interval multiplication, is also considered. This device would enable the hardware realisation of a fast RLS algorithm to operate at far greater speed than that obtained by performing interval multiplication using a DSP. Finally, the results presented in this thesis are summarised and the achievements and limitations of the work are identified. Areas for further research are suggested.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:642330 |
| Date | January 1991 |
| Creators | Callender, Christopher Peter |
| Publisher | University of Edinburgh |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/1842/10853 |
Page generated in 0.0027 seconds