Abstract: Disclosed herein are methods and systems for synchronizing data packets received by distinct unsynchronized receivers from a wireless transmitter. Each of a plurality of distinct unsynchronized receivers having no common time base may receive data packets transmitted by a wireless transmitter and in addition to computing reception data may compute a respective identifier (ID) for each of the packets based on the content of the respective packet which may temporally unique for at least a certain period of time compared to other data packets. A synchronization unit may receive the IDs associated with their reception data and may correlate between similar data packets based on their IDs. The synchronization unit may further output the correlated IDs coupled with their respective reception data to one or more apparatuses configured to process jointly the reception data associated with at least some of the correlated IDs.

Abstract: Disclosed herein are methods and systems for synchronizing data packets received by distinct unsynchronized receivers from a wireless transmitter. Each of a plurality of distinct unsynchronized receivers having no common time base may receive data packets transmitted by a wireless transmitter and in addition to computing reception data may compute a respective identifier (ID) for each of the packets based on the content of the respective packet which may temporally unique for at least a certain period of time compared to other data packets. A synchronization unit may receive the IDs associated with their reception data and may correlate between similar data packets based on their IDs. The synchronization unit may further output the correlated IDs coupled with their respective reception data to one or more apparatuses configured to process jointly the reception data associated with at least some of the correlated IDs.

Abstract: A method comprising receiving a dataset comprising data associated with a plurality of radio frequency (RF) wireless transmissions associated with a plurality of objects within a plurality of physical scenes, wherein the dataset comprises, with respect to each of the objects, at least: (i) signal parameters of the associated wireless transmissions, (ii) data included in the associated wireless transmissions, and (iii) locational parameters with respect to the object; at a training stage, training a machine learning model on a training set comprising the dataset and labels indicating a type of each of said objects; and at an inference stage, applying the trained machine learning model to a target dataset comprising signal parameters, data, and locational parameters obtained from wireless transmissions associated with a target object within a physical scene, to predict a type of the target object.