Qualcomm incorporated (20240259984). ZERO-SHOT DEEP LEARNING FOR MULTI-TARGET RF POSITIONING
ZERO-SHOT DEEP LEARNING FOR MULTI-TARGET RF POSITIONING
Organization Name
Inventor(s)
Farhad Ghazvinian Zanjani of Almere NL
Daniel Hendricus Franciscus Dijkman of Haarlem NL
Hanno Ackermann of Amsterdam NL
Ishaque Ashar Kadampot of San Diego CA US
Stephen Jay Shellhammer of Ramona CA US
Brian Michael Buesker of San Diego CA US
Fatih Murat Porikli of San Diego CA US
ZERO-SHOT DEEP LEARNING FOR MULTI-TARGET RF POSITIONING
This abstract first appeared for US patent application 20240259984 titled 'ZERO-SHOT DEEP LEARNING FOR MULTI-TARGET RF POSITIONING
Original Abstract Submitted
aspects presented herein may enable a passive positioning system, which may be a network entity or node, to be trained to identify multiple moving objects based on using training data for a single object. in one aspect, a network entity receives first rf channel data recorded by a set of devices for a coverage area during a first time period. the network entity trains an ml model based on the set of devices and the first rf channel data. the network entity receives second rf channel data recorded by the set of devices at a second time instance that is outside of the first time period. the network entity computes a number of moving objects in the coverage area at the second time instance based on the second rf channel data using the ml model.
- Qualcomm incorporated
- Farhad Ghazvinian Zanjani of Almere NL
- Daniel Hendricus Franciscus Dijkman of Haarlem NL
- Hanno Ackermann of Amsterdam NL
- Ishaque Ashar Kadampot of San Diego CA US
- Stephen Jay Shellhammer of Ramona CA US
- Brian Michael Buesker of San Diego CA US
- Fatih Murat Porikli of San Diego CA US
- H04W64/00
- G06N3/088
- CPC H04W64/00