As a team we are continually seeking to improve location accuracy – opening new applications for innovation. Errors caused by reflected signals and multipath interference are particularly problematic for industries reliant on GNSS, such as autonomous vehicles, where positioning accuracy is critical for safe and reliable operation. Finding solutions to these problems is the reason my role in signal processing is so critical.

From healthcare to aerospace, Dr Nguyen’s career in signal processing has the ability to impact our everyday lives. Our latest recruit to the team as a Senior Signal Processing Engineer, Nghia tells us more about the importance of his role.

Can you tell us more about your role as Senior Signal Processing Engineer?

As a Signal Processing Engineer, my responsibilities are to analyse real-world signals such as radio, voice, audio and imagery from an electronic system. I create algorithms to process these signals and mathematically manipulate them in a way that enhances the overall system performance or improves the efficiency of a specific task.

My role at FocalPoint involves working with the research and engineering teams to help develop our Supercorrelation™ technology – software that enhances the accuracy, reliability and security of GNSS positioning. As a team we’re continually working to improve location accuracy, opening new applications for innovation. Whilst GNSS is sufficiently accurate for many applications, it’s not consistent across environments prone to errors from reflected signals and multipath interference, particularly in urban areas.

Can you explain the importance and necessity of cleaning the signal?

The signal data we receive from a GNSS receiver isn’t direct from the satellite. In defence, for example, GNSS receivers are often targeted by jamming and spoofing attacks. This creates unwanted noise and interference which can suppress and corrupt the satellite signals. In civilian use of satellite navigation, multipath is an example of external interference and occurs when satellite signals bounce off buildings, trees, or other obstacles before reaching the receiver, creating multiple signal paths. This causes errors in the position calculation, which is particularly problematic for industries such as autonomous vehicles, where accurate positioning is critical for safe and reliable operation. We have to use novel signal processing techniques to remove that noise and interference and try to get the signal as clean as possible before calculating position.

How transferable are your skills from one sector to another?

Working as a signal processing engineer usually requires a broad knowledge of electronic devices. You need to understand how signals are generated and acquired inside individual devices or the whole system, before we are able to process and extract the necessary information. Many systems share common features or have similar architectures.

Signal processing engineering requires solid math skills, such as calculus, linear algebra and statistics. It also requires programming skills to develop prototypes of your algorithms applied to the signals. With these core skills you can certainly work in different fields of engineering to create intelligent processing applications.

Whilst I worked in GNSS for a year before I joined FocalPoint, the majority of my career has been spent working in academia. During this time I was a researcher in the field of signal processing for medical ultrasound imaging working specifically on breast cancer diagnosis. It involved understanding the interactions between sound waves and biological tissue that scatter signals. The algorithm that we use is the same, but on a different problem. Cancerous features are encoded in those generated signals and I helped to develop a new technique that filters out all of the imaging points around the key location we want to capture. And it’s exactly the same problem in GNSS – we want to take the GPS signal from the satellite, but at the same time we try to filter out all the noisy signals around it.

Your role is essential in demonstrating the benefits of Supercorrelation™ – can you explain more?

In our trials, we begin by working with synthetic cases where we can precisely control the models for the generation of data, noise and interference. Essentially we’re working on a mathematics problem where we analyse the equations of those models and translate them into algorithms for filtering the unwanted noise, building a good version of the desired signal. We then apply the algorithms to real-world experimental data, solving the physics problem. At this stage, we often need to adjust our algorithms to deal with extra noise arising not only from inside the device but also from the external environment such as multipath interference.

We are also investigating how we can leverage machine learning techniques alongside our signal processing algorithms. Machine learning can be useful for recognising patterns in datasets and extracting information that can help aid the navigation process, and offers a new and exciting opportunity to further enhance our Supercorrelation™ technology.

If you’re interested in developing scalable software solutions that supercharges GPS accuracy and integrity, take a look at our jobs board.