Banc 3, Inc.
BANC3 proposes combining the best features of several known 3D tracking technologies into a small, pager-sized, wearable tracking tag. First responders will wear the tag on their hip or in a pocket, and it will connect over a wireless mesh network to an anchor point installed on a vehicle or command center. Each tag will incorporate UWB tracking technology to track the operator’s positions within 20cm. We will augment this by adding a new approach to tracking, using streamed IMU (gyroscope, compass, accelerometer) sensor data through machine learning algorithms trained to track position while ignoring the noise and drift that plague IMU only measurements. This approach will allow us to continue tracking without access to the UWB network in signal denied environments. We present several options to extend the network, including dropping portable anchors in the field, leveraging 5G infrastructure when it exists, and incorporating GPS data where available. BANC3 also shows how multiple tracking systems can communicate and assist each other. Our software will provide an open API and open architecture to allow other systems such as a third-party visualization system to ingest and utilize our tracking positions traveled paths. Finally, we describe our expertise in designing and manufacturing electronic products and introduce our business plan to deploy this system alongside our existing products on AT&T FirstNet.
Basics
-
Number of Employees
-
Stage
-
Primary Industry
Description
BANC3 proposes combining the best features of several known 3D tracking technologies into a small, pager-sized, wearable tracking tag. First responders will wear the tag on their hip or in a pocket, and it will connect over a wireless mesh network to an anchor point installed on a vehicle or command center. Each tag will incorporate UWB tracking technology to track the operator’s positions within 20cm. We will augment this by adding a new approach to tracking, using streamed IMU (gyroscope, compass, accelerometer) sensor data through machine learning algorithms trained to track position while ignoring the noise and drift that plague IMU only measurements. This approach will allow us to continue tracking without access to the UWB network in signal denied environments. We present several options to extend the network, including dropping portable anchors in the field, leveraging 5G infrastructure when it exists, and incorporating GPS data where available. BANC3 also shows how multiple tracking systems can communicate and assist each other. Our software will provide an open API and open architecture to allow other systems such as a third-party visualization system to ingest and utilize our tracking positions traveled paths. Finally, we describe our expertise in designing and manufacturing electronic products and introduce our business plan to deploy this system alongside our existing products on AT&T FirstNet.
