World’s first commercially available L5-only GNSS solution includes Machine Learning (ML) algorithms to leverage increased L5 signal ranging precision and deliver unmatched performance in challenging signal conditions
Sunnyvale, California -- May 19, 2022 -- – oneNav today announced performance results from field testing its latest pureL5TM CES software in both open sky and very challenging signal environments. The patent-pending oneNav GNSS system, including a custom array processor and a library of Machine Learning (ML) algorithms, demonstrated consistent sub-meter accuracy and rapid TTFF (<2 sec) in open sky testing. In very challenging urban and deep urban canyon environments, the pureL5TM CES field test equipment outperformed the commercial precision L1 GNSS unit against which it was compared, demonstrating tracking of satellite signals as weak as -160 dBm. The oneNav system was able to acquire directly and track L5 signals in all environments with no L1 receiver present, greatly simplifying the RF front end and antenna subsystem and making the pureL5TM solution ideal for space- and power- constrained mobile and IoT devices requiring reliable high performance.
pureL5TMGNSS—all the benefits of high performance, next generation L5 in a single frequency L5 receiver
- Smaller footprint than L1+L5 hybrids, simplifying implementation in highly space-constrained devices like 5G smartphones and wearables
- Lowers BOM cost and simplifies the RF front end and antenna subsystem by eliminating the entire L1 RF chain
- No L1 aiding required—directly acquires L5/E5/B2 with 1 second TTFF
- Less software complexity, simplifies RF coexistence engineering
- Better interference resiliency
- Scalable IP signal processing core is semiconductor process node independent
- Multi-constellation L5—Beidou, Galileo, GPS, QZSS, GLONASS
Results of a representative urban drive test route are shown below (the map describes the route driven). During this test, the CES and the commercial precision L1 receiver were both connected to a common antenna, fixes were taken once/second, and the results were compared to a common ground truth position. On average, the oneNav system demonstrated a 55% improvement in accuracy over the precision L1 receiver.
oneNav’s family of ML algorithms improves pureL5TM system performance by predicting whether the received signal is Line of Sight (LOS); and correcting Non-Line of Sight (NLOS) signals to increase the number of measurements available for accurate positioning.
The pureL5TM ML algorithms characterize signal and multipath environments. Accordingly, algorithms developed in one deep urban area can be used to mitigate multipath in areas which are geographically different, but which present similar multipath signatures. This obviates the need for field test teams to collect data in thousands of urban areas around the globe.
oneNav is powering high performance positioning for location-dependent mobile services. Based in Silicon Valley, California, oneNav is developing the next generation GNSS receiver for smartphones, wearables, and tracking devices. oneNav’s team comprises top GNSS experts from Qualcomm, Apple, Intel, SnapTrack, SiRF, Trimble and eRide with decades of GNSS and mobile industry experience. The team has expertise in GNSS system architecture, multipath mitigation, signal processing, ASIC design and AI/machine learning, and collectively has filed over 300 career GNSS patents. Investors include Google Ventures, Norwest Venture Partners and GSR Ventures. To learn more, please visit www.onenav.ai.