What does aliasing refer to in signal processing?

Aliasing in signal processing refers to the distortion that occurs when a signal is sampled at a rate that is insufficient to capture the changes in the signal accurately. This phenomenon typically arises when the sampling frequency is lower than twice the highest frequency present in the signal (the Nyquist rate). When this occurs, higher frequency components of the signal can masquerade as lower frequencies once digitized, leading to inaccuracies in the representation of the signal.

The reason why the chosen answer is apt is that it directly addresses the core issue of aliasing: it describes how an analog signal is misrepresented by digital values due to insufficient sampling. This can create confusion and inaccuracies when reconstructing the original signal from its sampled version, as the high-frequency components can be wrongly interpreted as lower frequencies, causing significant errors in analysis and interpretation.

In contrast, the other options pertain to different aspects of signal integrity and representation. For example, a false representation of a digital signal may relate to issues in the digital coding itself, or excessive noise pertains to background interference that affects signal clarity, neither of which encapsulate the specific mechanism of aliasing. Loss of signal fidelity is a broader term that could encompass various forms of signal degradation, but again, it does not specifically define the