Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, an initiating device may transmit a signal that includes multiple packets. The initiating device may receive, from a responding device, a channel impulse response (CIR) report for each packet of the multiple packets. The initiating device may align, using one or more taps in the CIR report for each packet, the CIR reports across the multiple packets to identify a target object, a location of the target object, or a movement of the target object. The initiating device may perform an action based at least in part on the target object, the location of the target object, or the movement of the target object. Numerous other aspects are described.

Abstract: 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.

Abstract: Disclosed are systems, methods, and non-transitory media for performing passive radio frequency (RF) location detection operations. In some aspects, RF data, such as RF signals including channel state information (CSI), can be received from a wireless device. The RF data can be provided to a self-supervised machine-learning architecture that is configured to perform object location estimation.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, an initiator network node may transmit a first sensing signal for sensing an object using ultra-wideband sensing. The initiator network node may receive, from a responder network node, a second sensing signal, that is based at least in part on the first sensing signal, for sensing the object using the ultra-wideband sensing. The initiator network node may obtain a location of the object relative to the initiator network node and the responder network node based at least in part on the first sensing signal and the second sensing signal. Numerous other aspects are described.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a responding device may receive a signal from an initiating device. The responding device may estimate, from the signal, a channel impulse response (CIR) that represents signal reflections from one or more objects as multiple taps. The responding device may select one or more taps, from the multiple taps, that are within a first time window that starts at a first offset from a reference point and that has a first specified time duration. The responding device may transmit, to the initiating device, a CIR report that indicates the one or more taps. Numerous other aspects are described.

Abstract: Disclosed are systems, methods, and non-transitory media for sensing radio frequency signals. For instance, radio frequency data can be received by an apparatus and from at least one wireless device in an environment. Based on the radio frequency data received from the at least one wireless device, the apparatus can determine sensing coverage of the at least one wireless device. The apparatus can further provide the determined sensing coverage and a position of at least one device to a user device.

Abstract: Certain aspects of the present disclosure provide techniques for training and inferencing with machine learning localization models. In one aspect, a method, includes training a machine learning model based on input data for performing localization of an object in a target space, including: determining parameters of a neural network configured to map samples in an input space based on the input data to samples in an intrinsic space; and determining parameters of a coupling matrix configured to transport the samples in the intrinsic space to the target space.

Abstract: Disclosed are systems, methods, and non-transitory media for sensing radio frequency signals. For instance, radio frequency data can be received by an apparatus and from at least one wireless device in an environment. Based on the radio frequency data received from the at least one wireless device, the apparatus can determine sensing coverage of the at least one wireless device. The apparatus can further provide the determined sensing coverage and a position of at least one device to a user device.

Abstract: Disclosed are systems, methods, and non-transitory media for performing passive radio frequency (RF) location detection operations. In some aspects, RF data, such as RF signals including channel state information (CSI), can be received from a wireless device. The RF data can be provided to a self-supervised machine-learning architecture that is configured to perform object location estimation.