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Signal processing improves digitizer performance

18 May 2016  | Arthur Pini, Greg Tate, Oliver Rovini

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Engineers employ oscilloscopes and modular digitizers to acquire signals, analyse their characteristics, and make decisions. Often, those decisions affect designs or give an indication of a pass/fail condition in production. Working with raw data doesn't always tell you what you need to know. Bench oscilloscopes often have some signal processing included. Digitizers often come with software that lets you view and analyse digitized waveforms. You can also use third-party software to analyse digitized signals in real time or offline.

Common signal-processing applications include ensemble and boxcar averaging, Fast Fourier Transform (FFT), and digital filtering. You can use these functions to extract useful information from a simple measurement or enhance the measurement itself.


Averaging
Averaging can reduce the effects of noise and non-synchronous periodic waveforms on acquired signals. It requires multiple acquisitions and a stable trigger. Averaging will reduce the amplitude of signal components that aren't synchronous with the trigger timing, including random noise. The degree of reduction is dependent on the waveform characteristics and the number of acquisitions added to the average.

Spectrum's SBench 6 software—used in this article—and most oscilloscopes and other oscilloscope PC software applications perform ensemble averaging, meaning that the same sample location in multiple acquisitions are averaged together. If a stable trigger is available, the resulting average has a random noise component lower than that of a single-shot record.


Summed Averaging
Summed Averaging uses a fixed number of acquisitions and is the repeated addition, with equal weight, of the same sample locations from successive waveform acquisitions. Whenever the maximum number of sweeps is reached, the averaging process either stops or is reset to begin again.

Figure 1 shows the concept of a summed ensemble average. The arrows indicate the nth point. The amplitude value of the nth point of each acquisition is summed with those of the other acquisitions. The sum is then divided by the number of acquisitions to determine the nth value of the average. This takes place for all sample points in the acquisition group. The resultant averaged waveform has the same number of points as each acquired waveform.


Figure 1: Summed ensemble averaging adds the nth point of multiple acquisitions and then divides the sum by the number of acquisitions to determine the averaged value for the nth point.


Averaging is supported for both normal acquisition and for multiple (segmented) acquisitions. Multi-averaging calculations permit the average of consecutive segments of the multiple recording acquisition.

When a signal is averaged additive broadband Gaussian noise will be reduced by the square root of the number of averages. Thus, averaging four acquisitions can improve the signal to noise ratio by 2:1. Similarly, non-synchronous periodic signals will be reduced in the average. The degree of reduction depends on the phase variation of the interfering signal from acquisition to acquisition. Signals synchronous to the trigger, such as distortion products, will not be reduced in amplitude by averaging.

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