Motion and Gesture Sensing with Radar, reviewed

I reviewed ‘Motion and Gesture Sensing with Radar’ by Jian Wang and Jaime Lien over my festive pudding during the holidays. As an engineer who is not in the radar field, I found it to be an intriguing read. It’s well written, has very concise concept descriptions and provides a deep background into the history and core concepts around radar technology. Despite its strong mathematical disposition and very detailed technical explorations, I found myself with my poor skills in calculus to be able to follow along and gain understanding as the book developed in its explorations.  

Motion and Gesture Sensing with Radar, reviewed

Anybody who’s interested in technology will appreciate some aspects of this book yet the key target audience in my view will be mobile application developers. To these technologists, it will be a one-stop guide on incorporating radar technology into mobile applications, which will materially upgrade the compatible handset’s use of hand gestures and motion detection in applications.  

Chapter 1 was the introduction chapter offering background in the history of radar with nice explanations and illustrations to simplify complex technological concepts starting over 100 years ago from Hertz’s discoveries to the current day. It then offers a nice tie into the consumer market with Google’s Pixel 4 mobile handset harnessing soli chips with radar technology baked into it. From there, it moves onto other touch points in consumer innovations like ADAS (Advanced Driver Assist Systems) plus more radar-embracing technologies that are in existence today. 

Chapters 2 to 6 are meaty chapters with a lot of advanced calculus which was never really my thing. Radar architecture, signal modelling, signal processing and motion detection were covered in real depth affording a deep understanding in order to be technically conversant in these areas before you move on. Here’s the kicker, despite my poor calculus knowledge, the explanations of each equation were actually understandable and I could read 80% of them upon processing the commentary in my own head.

This is a first for me, as no one ever bothered to explain calculus to me but I could follow it with these explanations that actually made sense. I wonder what I would be doing now if I had a book like this in secondary (high) school.  

Chapter 7 was a slowdown and read it twice chapter where machine learning was covered in a detail with academic conventions on referencing used. This referencing system was used throughout but had a larger number of entries compared with other chapters.  That said, the subtopics were neatly broken down with very clear introductory paragraphs and clear explanations throughout. If you buy this book during the festive season, just read this chapter with a cup of coffee and not a glass of whiskey.

I did like the differentiation and application of dataset usage for machine learning regarding radar and the exploration of core concepts like linear regression, gradient descent and more. These were neatly explained and from experience not understood by many outside the data science and data engineering fields.  

The chapter moves by sub-section into more complex areas like functions and associated concepts offering important context to allow the reader some element of understanding on the first read. It then considers radar aspects like hardware, what it does, latency, clutter, and complex signals plus more when thinking about a machine-learning solution for a product. If the process of data modelling is not matured via open-source libraries and the like, it can be very complex to get right and requires considerable development focus. This chapter provides key insights into this area aiding anybody engaged in such a project. 

Chapter 8 focused on UX and application design. The read was picking up the pace again with some introduction around modern apps, and mobile input methods expanding after web 2.0 to include things like voice, medical devices and now radar integration. These continuous input streams on constrained devices are an important point given the processing is done on the handset and not on a server in the case of LIDAR-based imagery over an app camera.

In my view, it’s a breakthrough point for radar and its local use on the handset. The chapter concludes by nicely tying in radar integration into the application development process along with the best approaches to use in meeting functional requirements.  

Chapter 9 finishes the book on research and development describing important milestones in the technology’s lifecycle like standardisation via IEEE/API Ripple standard, RFIC with new UWB chipsets, machine learning and more. A final point made on the expanding use cases for radar integration in the mobile development world is fiercely relevant as the consumer value creation aspects of radar if done correctly can only enhance the mobile experience.

I would submit that radar’s integration using the same technologies can also be of benefit to businesses via drone technology, manufacturing automation plus more. The sky is the limit as these use cases expand over time. 

John Mulhall @johnmlhll | is a writer with Irish Tech News for over 6 years and also Founder, Writer and Engineer with Maolte Technical Solutions Limited. You can learn more about John and his new company at

See more stories by John here, and more reviews here.

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