Tracking accuracy re-imagined using Edge AI algorithms

Continually Adaptive Navigation algorithm (CAN)

Enables self-reliant inertial sensor system to deliver – finally – truly accurate tracking for field use when GPS is unavailable.

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Early efforts to use sensors as tracking devices were often unable to deliver the accuracy necessary for use in the field, suffering reliability issues caused by gyro drift, magnetic interference and lack of Edge AI algorithms. As GPS has become ubiquitous, however, GPS technology has also run headlong into well known limits of use, whether because of signal unavailability or being vulnerable to jamming or spoofing.

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CAN-augmented systems deliver an astonishing 10 meter positional accuracy for every kilometer traveled – without the use of GPS or RF beacons. CAN allows sensor systems to work indoors or out, is field configurable, and is easily integrated with PNT and battlefield awareness systems.

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Applying decades of knowledge born from creating patented sensor products for the auto, marine, robotics, space and consumer markets, PNI has developed a breakthrough technology: the Continually Adaptive Navigation (CAN) algorithm. Used in inertial sensor systems, CAN continually corrects for gyro drift (over 100 times/second), continually rejects magnetic interference (over 400 times/second) and tracks positioning using a Markov process and neural network classifiers (over 30 times/second).

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The result? The first sensor-based system to offer a truly viable PNT solution for military operations. Our FORT product with CAN* technology uses a hardware platform that enables quick iteration, test and deployment, making it the first viable sensor-based PNT.

Continually Adaptive Navigation (CAN) technology can be found in our:

Field Ops Remote Tracker ⇒

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