Machine Learning for RFID (Radio Frequency Identification) tag localization to support logistics
Description
Currently have two production machine learning approaches to tackle RFID (Radio Frequency Identification) tag localization in the highly reflective environment imposed by the International Space Station. First use case, REALMRFC, is a random forest classifier model with feature engineering performed by an RFID localization expert. The second use case is P-RFIDNet, a neural network with a ResNet50 backbone. In continued work, we have leveraged transfer learning to show how P-RFIDNet can be generalized to new RFID environments with limited training data. We benchmark P-RFIDNet and REALMRFC using data from the RFID Enabled Autonomous Logistics Management (REALM) and using truth derived from the Inventory Management System (IMS).
Detailed example
RFID (Radio Frequency Identification) tag localization
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
Currently have two production machine learning approaches to tackle RFID (Radio Frequency Identification) tag localization in the highly reflective environment imposed by the International Space Station. First use case, REALMRFC, is a random forest classifier model with feature engineering performed by an RFID localization expert. The second use case is P-RFIDNet, a neural network with a ResNet50 backbone. In continued work, we have leveraged transfer learning to show how P-RFIDNet can be generalized to new RFID environments with limited training data. We benchmark P-RFIDNet and REALMRFC using data from the RFID Enabled Autonomous Logistics Management (REALM) and using truth derived from the Inventory Management System (IMS).
Controls / human review
ATO: Not reported; PIA: Not published