文档介绍：SECTION 5
UNDERSAMPLING APPLICATIONS
Fundamentals of Undersampling
Increasing ADC SFDR and ENOB using External SHAs
Use of Dither Signals to Increase ADC Dynamic Range
Effect of ADC Linearity and Resolution on SFDR and
Noise in Digital Spectral Analysis Applications
Future Trends in Undersampling ADCs
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SECTION 5
UNDERSAMPLING APPLICATIONS
Walt Kester
An exciting new application for wideband, low distortion ADCs is called
undersampling, harmonic sampling, bandpass sampling, or Super-Nyquist
Sampling. To understand these applications, it is necessary to review the basics of
the sampling process.
The concept of discrete time and amplitude sampling of an analog signal is shown in
Figure . The continuous analog data must be sampled at discrete intervals, ts,
which must be carefully chosen to insure an accurate representation of the original
analog signal. It is clear that the more samples taken (faster sampling rates), the
more accurate the digital representation, but if fewer samples are taken (lower
sampling rates), a point is reached where critical information about the signal is
actually lost. This leads us to the statement of Shannon's Information Theorem and
Nyquist's Criteria given in Figure . Most textbooks state the Nyquist theorem
along the following lines: A signal must be sampled at a rate greater than twice its
maximum frequency in order to ensure unambiguous data. The general assumption
is that the signal has ponents from dc to some upper value, fa. The
Nyquist Criteria thus requires sampling at a rate fs > 2fa in order to avoid
overlapping ponents. For signals which do not extend to dc, however, the
minimum required sampling rate is a function of the bandwidth of the signal as well
as its position in the frequency spectrum.
SAMPLING AN ANALOG SIGNAL
Figure
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SHANNON’S INFORMATION THEOREM
AND NYQUIST’S CRITERIA
Shannon:
An Analog Signal with a Bandwidth of fa Must be Sampled
at a Rate of fs>2