Dan leads development for RF and radar systems and holds advisory roles across the industry, accelerating the development of integrated RF products. His breakthrough technologies include RF SOI Antenna switches and Tuning products as well as CMOS power amplifiers which led to changes in the cell phone industry technology adoption, phone functionality and design.
We asked Dan about what he sees as the next frontier for radar technologies and the top problems he sees customers needing to solve.
Q: Radar has been around for so long – is there really anything new that radar can do?
A: Radar concepts emerged late in the nineteenth century, with very rapid evolution during World War II. But the technology for radar implementation is new. Magnetrons evolved into solid state solutions using III-V GaAs devices and the assemblies were large and complex. Today, thanks to Moore’s law and improved silicon capabilities, integrated beamforming technology exists for 5G cellular communications. At MatrixSpace we’ve retargeted these capabilities into unique radar and communications products which enables very low size, weight, power and cost solutions, which in turn enable new markets. We’ve leveraged our team’s experience in RF semiconductors, software defined radios, cell phones, cellular base stations and mmWave 5G to create a fresh approach to the modern radar.
Automobiles were an obvious candidate for radars, but it wasn’t until the radar size and cost hit the right level before they were included. With high levels of integration, these automotive radar modules took off – modern cars may include up to 15 radar modules as a result. We see the same thing happening with higher performance radar as well. As we decrease the size, weight, power, and cost (SWAP-C), we see opportunities, perhaps previously unimagined, opening up in many markets.
This is what we mean when we talk about radar re-imagined.
Q: Radar is so well established, certainly across sectors such as defense and aviation. What are some of these customers trying to do that they can’t already do?
A: It’s all about the low SWAP-C. Creating a low SWAP-C radar enables mobile, deployable radars for use on drones, in large scale distributed radar sensors for sites and infrastructure, and a variety of other applications. The story parallels the automotive example – many markets want the ability to detect and track targets day or night, in any weather condition, over long ranges, but it’s only when the SWAP-C reaches the right level that the technology adoption occurs.
Q: How does low SWAP-C change the higher performance radar market?
A: It’s all about the ability to deploy radars in a highly distributed manner. We see an opportunity to shift from highly centralized extremely expensive radars to smaller, localized radars that enable enhanced local detection capabilities. It also allows radar to be used in previously unserved markets. Radar for the masses, so to speak. This is a radar that can be placed nearly anywhere, carried in a backpack, or mounted on a drone. We’ll enable you to take the sensing capability to where it is needed.
Q: With the rise of edge computing and the Internet of Things, does radar have a role to play in that space?
A: Absolutely. Radar offers an additional sensing modality. IoT is all about vast deployments of sensors, which then enable machines to process the sensed data and respond. Much the same as mammals use their various senses to interpret and process the world around them, radar is one of the IoT senses.
Once all of this data is sensed, we rely on edge computing to do the initial processing, distilling the raw data down to the interesting elements which are shared for enhanced collaboration. These sensors then feed AI and ML engines, which thrive on large amounts of data. This meets the need for real-time information.
Q: We see a lot around machine learning and computer vision. Is that primed to replace radar in the area of long-distance sensing, detection and surveillance?
A: MatrixSpace has expertise in AI/ML capabilities. Coupling these capabilities to radar will allow us to do radar object classification, pattern recognition, and event detection – day or night, rain or shine. AI applied to radar has similarities to AI applied to optical images, just using a different sensing modality. Radar offers outstanding range and velocity resolution. It generates lots of unique and precise data, and that’s what AI likes to feed on.
Q:What does your crystal ball project for the next 10 years?
A. Looking back over the last couple of decades we’ve seen the emergence of semiconductors, computing and personal computers, the internet (wired connectivity), wireless communications, electric vehicles, and the internet of things. As each capability emerges, it’s quickly followed by efforts to connect it to other devices, yielding exponential benefits.
Just as people have become hyper-connected, so too are machines, with a level of intelligence to make them critical in solving some of the most pressing challenges we face. We see sensor fusion as a key part of this smarter world, with radar as a key element of this sensor fusion roadmap. We see a future that includes a lot more aircraft activity, both eVTOL and UAS, in low altitude airspaces and we know that protection of critical assets is going to be key.
I think we’ve only begun to see how this will change the way we literally see our outside world.