![CA-based Neural Propulsion Systems (NPS) secures $17.5million in series B round funding. Cota Capital led the investment, with participation from RTX Ventures, the venture capital arm of RTX (NYSE: RTX), and GM Ventures, the venture capital arm of General Motors Co. (NYSE: GM).](https://startuprise.io/wp-content/uploads/2024/03/CA-based-Neural-Propulsion-Systems-NPS-Secures-17.5Million-in-Series-B-Round-Funding.webp)
CA-based Neural Propulsion Systems (NPS) secures $17.5million in series B round funding. Cota Capital led the investment, with participation from RTX Ventures, the venture capital arm of RTX (NYSE: RTX), and GM Ventures, the venture capital arm of General Motors Co. (NYSE: GM).
The money will be used by the business to advance the development of its Atomic Sensing Platform and its application to automotive radar technology.
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Digital imaging radar technology is developed by NPS under the direction of its founder and CEO, Dr. Behrooz Rezvani. Its Atomic Norm Radar SW, which is based on a radar algorithm system that can operate on a variety of hardware platforms, offers improved, dependable radar resolution and precision, opening up possibilities in the defense and mobility sectors.
Behrooz Rezvani said: āBy harnessing the potential of our newly developed radar technology, we can potentially achieve performance enhancements that are over 10 times greater than current radar capabilities, putting us at the forefront of revolutionizing the $28 billion radar market. Our radar software works with all radar hardware and significantly improves the performance of existing sensing platforms with lower cost and more efficiency.ā
About Neural Propulsion Systems
Experts from Silicon Valley launched NPS in 2017, which transforms radar technology. Unprecedented resolution, accuracy, and dependability are provided by their unique algorithms. Their radar allows drivers to maneuver through even the most challenging road conditions since it is more advanced than human eyesight. They reinvent the processing and understanding of sensor data by applying the mathematical framework of the atomic norm.
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