Patent ID: 11867833
Assignee: THE UNIVERSITY COURT OF THE UNVERSITY OF ST. ANDREWS
Field: Measurement (Instruments)
Classification: CPC G | IPC G

Claim 21:
22. An apparatus for classification of objects, the apparatus comprising
a radar unit configured to receive raw radar signals reflected from a field of view of the radar unit, whereby the field of view includes an inanimate object positioned in proximity to the radar unit such that a distance between the inanimate object and the radar unit is less than 1 m, and
a processor configured to classify and thereby identify the inanimate object based on at least one feature extracted from the raw radar signals, wherein
the processor is configured to classify by the inanimate object to determine a classification of the inanimate object by using a machine learning classifier to distinguish between objects or classes of objects from a predetermined list of objects or class of objects having at least one of different materials, different internal structure, or a different number of stacked objects, and the inanimate object is an inanimate solid object or a liquid,
the processor is further configured to use the classification of the inanimate object to identify the inanimate object as being a selected object or in a selected class of objects or having a selected number of objects, the selected object, class or number being from the predetermined list of objects or classes of objects having the at least one of different materials, different internal structures, or different number of stacked objects, wherein
the radar unit is further configured to transmit a frequency-swept signal;
in response to the frequency-swept signal, the radar unit is further configured to receive the raw radar signals using a plurality of receivers, each of the plurality of receivers at a different position, and the raw radar signals comprise a plurality of channels each corresponding to afield of view of a respective one of the receivers,
the radar unit is further configured to divide each raw radar signal into a plurality of time bins, such that each time bin corresponds to a respective frequency value or range, and use an amplitude of the raw radar signals for each time bin and channel as a respective feature, thereby to obtain a set of time-binned features from the raw radar signals; and
the processor is further configured to apply the machine learning classifier to the time-binned features obtained from the raw radar signals for the fields of view of the plurality of receivers.