Signal Processing
Signal processing, particularly digital signal processing (DSP), is ubiquitous in modern measurement science. Almost all physical events of interest to scientists are ultimately converted to an electrical signal which is then sampled, digitised and downloaded into a computer.
In addition, much modern data acquisition and analysis software contains built-in functions for processes such as windowing, filtering and transforming signals that can be treated as 'black boxes' by the user.
Digital signal conditioning and processing underpins almost all electrically-based measurements and new developments in Information and Communications Technology in the UK economy. Digitised measurements are omnipresent throughout technology; in any measurement or control application where it is necessary to obtain a correct measurement of the parameters of complex (real-world rather than simplified ideal-world) waveforms, digitisation and DSP are required within an uncertainties framework.
Challenges
As a result of uninformed use of the highly sophisticated measurement software that is available today, there is a potential to introduce artefacts and additional sources of uncertainty into the results of measurements if the scientist using these tools is not aware of the limitations of the applied methods. Choice of hardware and firmware introduces further complications, so that the effects of resolution, sampling synchronization and sampling jitter require analysis. Finally, many sensors with frequency-dependent properties are calibrated by deriving their impulse response from comparisons of output and input signals using convolution and deconvolution methods. Such methods need care if one requires a reliable determination of the amplitude and phase response of the sensor in question.
Approach
We advocate a software engineering approach to signal processing that emphasises the need for a clear definition of the problem, good choice of algorithms and of numerical methods, and rigorous testing. We recognise that uncertainties that arise from the choice and implementation of signal processing techniques are often not studied systematically and uncertainty budgets may omit contributions arising from these sources. We aim to provide support, good practice guidance and signal processing tools that will ensure that good practice can be adopted by metrologists in an easily-implementable manner, which can allow them to concentrate on their measurements results with the confidence that the uncertainties arising from their chosen signal processing techniques have been accurately quantified.