Qualcomm Incorporated

Hanno Ackermann

Stephen Jay Shellhammer

Fatih Murat Porikli

Brian Michael Buesker

Daniel Hendricus Franciscus Dijkman

Farhad Ghazvinian Zanjani

Ishaque Ashar Kadampot

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.

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Amsterdam, Netherlands

Hanno Ackermann