What is Aliasing?
Aliasing in digital signal processing occurs when a continuous analog signal is sampled at too low a rate, causing different signals to become indistinguishable or 'aliases' of one another. Essentially, higher frequency components in the original signal appear as lower frequencies in the sampled digital signal, leading to a misrepresentation of the original data.
How Aliasing Occurs: The Nyquist-Shannon Sampling Theorem
This phenomenon is directly related to the Nyquist-Shannon Sampling Theorem, which states that to accurately reconstruct a continuous signal from its samples, the sampling frequency must be at least twice the highest frequency component present in the original signal. If the sampling rate falls below this Nyquist rate, information about the original signal's true frequency content is lost, and higher frequencies 'fold over' into the observable frequency band.
A Practical Example of Aliasing
A common visual example is seeing wagon wheels in old Western movies appear to spin backward or stand still. This optical illusion happens because the camera's frame rate (sampling rate) is too slow to capture the true rotational speed of the wheel, causing its spokes to appear in a position that suggests backward motion, even though the wagon is moving forward.
Importance and Applications in STEM
Understanding aliasing is critical in fields like electrical engineering, computer science, and acoustics. In data acquisition, engineers use anti-aliasing filters before sampling to remove high-frequency components that could cause aliasing, ensuring that the collected digital data is an accurate representation of the original analog signal for analysis or further processing.