Christophe LOUSSERT, MOJIX, France

Passive UHF RFID tags have reached a very low cost level <0.05 € and start to be massively deployed in various sectors of activity (20 billions units sold in 2018). The readers able to acquire the data contained in the tags are mainly hand held devices, with operators scanning the products at a distance of less than 0.5 m, but also fixed devices installed at doorways, which dynamically read tags happening to pass in front of them.

In the future it will be extremely useful, and is one of the big challenges, to achieve Real Time Location System (RTLS) in order to carry out the automatic inventory of static tags distributed on a large area (i.e. 100 m²), typically a stockroom with hundreds of meters of shelves containing RFID tagged products. To that aim, an innovative RTLS will be presented in this talk, based on a bistatic system with:
– one single central Rx point, called HotSpot (HS), able to read all tags which have been powered up
– hundreds of distributed wireless transmitters, called Power Nodes (PN), placed inside the shelves and transmitting the maximum allowed power in order to provide sufficient energy to the surrounding tags
– tags operating in backscattering mode toward the HS, when they are powered up

The HS+PN combination enables successful reads in excess of 99.9% (i.e. a handful of no reads, out of a typical stock of 5000 items) and a localisation accuracy better than 0.5 m for 90% of the tags. The principle allowing to achieve these results is, for a given tag that in practice has been powered up by typically 10 PN transmitters, to localise it onto the one generating the largest received signal at the HS. However the accuracy is less than 1m for 10% of the tags, the reason being still under investigation although it seems to involve the propagation channel between the PN transmitter and the tag. One promising direction for improvement is machine learning, since a large amount of training data are available, thye system typically generating daily 1million tag data. Indeed it turns out that the well known KNN algorithm (K Nearest Neighbors), a « lazy » machine learning process, already delivers good results in some RFID applications.