Kolmostar

Kolmostar Inc., established in 2016, is a specialized technology firm addressing the high-power consumption and low-accuracy challenges of conventional Global Navigation Satellite System (GNSS) positioning. The company was co-founded by CEO Tao Tong and Vincent Hua, who bring together deep academic and industry experience in signal processing and large-scale data systems. Tao Tong's background includes a Ph.D. from Harvard, where he designed the digital processor for the RoboBee, and a stint as a Young Entrepreneur in Residence at IDG Capital Partners. Vincent Hua holds a Ph.D. from Stanford and has experience leading analytics at Apple's Geolocation division and as an early member of Google's machine learning team.

The company operates in the IoT and autonomous vehicle markets, providing ultra-low-power, high-precision GNSS positioning solutions. Kolmostar's business model centers on the design and sale of its proprietary GNSS sensor modules and leveraging a supporting cloud platform. By fundamentally reconstructing the signal processing framework for GNSS signals, the firm's technology drastically cuts down on computational requirements. This approach enables a significant reduction in power consumption, claiming to use as little as 1/100th of the energy of traditional solutions, which is critical for battery-powered IoT devices.

Kolmostar offers two main product lines: the JEDI series and the Titan series. The JEDI modules, such as the JEDI-200, are designed for IoT applications like asset tracking, smart wearables, and logistics, where extending battery life is paramount. These modules achieve meter-level accuracy with an extremely fast time-to-first-fix (TTFF) of one second from a cold start, even in challenging urban environments. The Titan series is engineered for applications demanding higher precision, such as autonomous vehicles, providing centimeter-level accuracy. The company's technology combines advanced silicon, statistical signal processing, and cloud computing to process positioning data efficiently. A cloud-based service complements the hardware, improving accuracy and further minimizing power demands by offloading computation from the end device.

Keywords: GNSS positioning, ultra-low-power, high-precision GPS, IoT tracking, asset tracking, autonomous vehicles, location-based services, signal processing, LoRaWAN positioning, wearables technology, smart farming, JEDI-200, GNSS sensor module, urban canyon navigation, cold boot TTFF, centimeter-level accuracy, battery-powered devices, Tao Tong, Vincent Hua, statistical signal processing