Inrange Golf set out to transform the driving range experience by installing radar tracking systems that follow every golf ball hit. These systems enable ranges to offer entertaining games or give golfers precise, shot-by-shot feedback. The challenge lay in reliably processing large volumes of radar and camera data in real time, ensuring accurate ball tracking even when objects like netting disrupted flight paths. Prior methods lacked the automation, processing speed, and adaptability needed to deliver a seamless user experience and support continuous innovation in the product.
MATLAB has been central to the development of Inrange’s radar hardware and tracking algorithms from the very beginning. Key MathWorks products included:
The team also leverages MATLAB Unit Testing Frameworks and MATLAB GUIs (app framework) for production test automation.
Access to the MathWorks Start-Up Program, supported locally by Opti-Num Solutions, played a pivotal role in the early days. Being able to obtain MATLAB at start-up–friendly pricing gave Inrange the ability to experiment, iterate, and build a strong technical foundation without being constrained by licensing costs. This ensured resources could go into product innovation and scaling.
The integration of MATLAB toolboxes and workflows, combined with the Start-Up Program and ongoing technical guidance, has been instrumental in enabling Inrange to scale and refine its tracking systems:
For Inrange, MATLAB stood out from other platforms because it offered a single environment for developing, testing, and deploying algorithms, combined with specialist toolboxes for radar, computer vision, and machine learning. This eliminated the need to stitch together multiple software solutions, reducing complexity and accelerating time to value.
As Inrange explains:
“We can process large amounts of data in MATLAB in real time. Over the years regular updates to MATLAB have resulted in large execution speed improvements that in many cases enabled us to make substantial improvements to how data is processed and used.”
The project remains ongoing, with continued exploration of deep learning to further enhance performance and innovation in the sports technology sector.
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