How Undersampling Affects Your Signal

July 24, 2019

The fact that analog signals provide continuous time values makes them ideal for real-time data monitoring. And while that’s an exceptional use of analog signal processing, we can’t ignore the fact that analog signals are more susceptible to errors, which makes their processing an extremely complex job.

In order to find a solution that allows easy processing without compromising on the signal strength, designers switched to the use of digital signal processing. Converting a continuous time signal to a discrete one is the first step taken to convert an analog signal to a digital one.

Also referred to as “sampling,” this process involves taking samples of the data at discrete time values. The biggest advantage of sampling is the accumulation of a finite set of values, making their processing relatively easier and reducing the chances of error.

Converting an analog signal to a digital one has its own limitations. You’re basically converting continuous-time values to discrete ones, leaving enough room for error. To minimize these conversion errors, you need to follow the Nyquist Sampling Theorem:

f_s ≥ 2 f_m

According to this theorem, the sampling frequency should be at least twice the maximum frequency component in your signal to avoid unambiguous data. Failure to comply with this sampling criterion results in an unwanted signal effect such as undersampling.

What Is Undersampling?

Undersampling occurs when the sampling frequency is less than the defined criterion. The phenomenon goes by many names—harmonic sampling, super-Nyquist sampling, and bandpass sampling.

While undersampling results in data loss and affects the signal in many ways, the aliasing effect is by far the biggest issue faced by engineers because of undersampling. Simply put, aliasing occurs when two signals override each other and become indistinguishable, a reason why they’re called ‘aliases’ of each other.

Aliasing effect generally occurs in two forms: temporal and spatial. Temporal aliasing refers to the effect that arises in signals that are sampled in time, while spatial aliasing occurs in spatially sampled signals.

The biggest disadvantage of undersampling is signal loss. When your sampling frequency isn’t up to the mark, you’re basically discarding all the frequency components that are above your sampling frequency. It’s important to note that you’re likely to get an entirely different signal at the receiving end due to altered frequency components.

How Can You Avoid Undersampling?

The easiest way of avoiding undersampling is by following the Nyquist Theorem. However, with the latest advances in technology, engineers have now developed a method of utilizing undersampling to their advantage by limiting the signal bandwidth. This technique follows the Nyquist-Shannon Sampling theorem, which requires the sampling frequency to be twice the signal bandwidth.

fs ≥ 2 (BW)

With a bandpass filter, designers can now limit the signal bandwidth and can easily reconstruct an undersampled signal without information loss.

Whether you’re looking for electronic components to design or in search of a pre-designed filter, ADSANTEC has you covered.

Our range of products includes digital filters, FIR filters, 4-bit ADC, delay lines, limiters, and other electronic components.

Browse through our products today and take your pick!