Why signals are important for space applications

Digital signal processing (DSP) is a well-known combination of advanced technologies and techniques that form the essential building blocks of today’s technological infrastructure. 

For instance, modern communication systems including mobile phones, digital radio and television and satellites rely on sophisticated DSP methods. Similarly, techniques and technologies that are indispensable in modern medicine, including heart monitors, medical imaging scanners, medical implants, dialysis machines and many more are highly dependent on DSP. 

So, what is DSP? In short, it combines electronics, software and mathematics. The term “Signal” is crucial in understanding this modern form of technology. Signals have been around since before the written word. Prehistoric communities that mastered some form of advanced signalling potentially gained advantages in survival-based skills like hunting and other competitive behaviours. Signals allow messages to be communicated at a distance and or covertly; effective signalling is a make-or-break enabler for societies. 

The growth of DSP

Historically, signal types were as varied as the imagination could allow. Today, signals remain diverse, but have evolved to the next level of human endeavour. Many common types of signals rely on different properties around some form of repetition. Mathematics has proved to be an effective way to describe signals. Pioneers such as Claude Shannon, Harry Nyquist, Ralph Hartley, Richard Hamming were instrumental in developing some of the important mathematical theory underlying digital signals throughout the 20th century.  Their work has provided the foundation for the growth of DSP and digital communication systems. 

There are also important synergies between the key properties of signals and the technology used to capture, process and store these types of signals. The ability of a DSP system to be able to perform lots of mathematical calculations - as fast as possible and requiring as little computer memory as possible - is crucial. This can also mean in a pre-determined way, so that a system is specifically designed to work as planned without incurring delays in processing mission critical information.

There are further ramifications and requirements in the design of DSP-based systems, such as limited physical space or power supply. As a result, DSP systems are often designed as part of embedded systems, which are designed to occupy a limited physical space and operate with other constraints such as limited power. These embedded systems are usually based around some form of microcontroller or microprocessor-based system, often requiring special purpose microprocessor cores, which can efficiently perform the mathematical operations associated with DSP. 

Designing an effective DSP

Another important dimension to DSP is the relationship between algorithms, hardware and mathematics. The design of an effective DSP system typically involves families of functions that can be calculated in more efficient ways, using algorithmic principles and refining the understanding of the underlying mathematics. 

For instance, a technique first invented by French mathematician and physicist Jean-Baptiste Joseph Fourier in the early 19th Century, to solve heat conduction has found popular use in DSP systems and other disciplines such as geology. The popularity of the Fourier technique has grown exponentially in the subsequent centuries, not just because of modern computing, but also through the careful re-design of the underlying calculations. The result is the mathematical equivalent to more conventional approaches.

All of these factors make DSP a rich and rewarding area to work in! I hope this inspires you to find out more.