A 5G-signal-based DOA fingerprint-based positioning method includes the following steps: dividing an initial area into a number of micro-cells, and estimating angle information of reference points in the divided micro-cells; storing the angle information of the reference point of each micro-cell and position information of the each micro-cell in a fingerprint database, and updating the angle information in the fingerprint database at regular intervals; wherein when there is a target in the initial area, estimating angle information of the target; matching the angle information of the target with the angle information in the fingerprint database to determine a micro-cell where the target is located to obtain position information of the target, so as to locate the target.

Systems and methods for indoor tracking via Wi-Fi fingerprinting and electromagnetic fingerprinting are provided and can include a gateway receiver device measuring a RSSI value of a signal transmitted by a Wi-Fi transmitter device, the gateway receiver device measuring an EMF value of an interference in an electromagnetic field created by the gateway receiver device that is caused by the Wi-Fi transmitter device, the gateway receiver device determining whether the RSSI value matches any of a plurality of Wi-Fi fingerprints associated with a monitored region and whether the EMF value matches any of a plurality of electromagnetic fingerprints associated with the monitored region, and responsive thereto, the gateway receiver device identifying that a location of the Wi-Fi transmitter device is within the monitored region.

A wireless communication device includes: a memory; a transceiver; and a processor communicatively coupled to the memory and to the transceiver and configured to: obtain a first device identity for each of a plurality of first radio-frequency (RF) devices each configured to transmit a wireless RF signal; obtain a mobility status for each of the plurality of first RF devices, the mobility status indicative of whether the respective first RF device is expected to be mobile or static; obtain an RF signal measurement for each of the plurality of first RF devices; and produce a profile of the plurality of first RF devices using the first device identity for each of the plurality of first RF devices, the mobility status for each of the plurality of first RF devices, and the RF signal measurement for each of the plurality of first RF devices.

Systems and methods for determining user equipment (UE) locations within a wireless network using reference signals of the wireless network are described. The disclosed systems and methods utilize a plurality of in-phase and quadrature (I/Q) samples generated from signals provided by receive channels associated with two or more antennas of the wireless system. Based on received reference signal parameters the reference signal within the signals from each receive channel among the receive channels is identified. Based on the identified reference signal from each receive channel, an angle of arrival between a baseline of the two or more antennas and incident energy from the UE to the two or more antennas is determined. That angle of arrival is then used to calculate the location of the UE. The angle of arrival may be a horizontal angle of arrival and/or a vertical angle of arrival.

The disclosure relates to a detection method and a detection apparatus, the method including: calculating, when a location base station in an ultra-wideband location system receives a pulse response, values of a plurality of specified pulse response characteristics using the received pulse response, and using the calculated values as values of the plurality of specified pulse response characteristics of the location base station; calculating differences between the values of the plurality of specified pulse response characteristics of the location base station and values of the plurality of specified pulse response characteristics of the location base station at a previous time, and using the calculated differences as variations of the plurality of specified pulse response characteristics of the location base station; determining, based on at least the variations of the plurality of specified pulse response characteristics of the location base station and by means of a trained classifier, whether signal propagation in which the location base station participates is non-line-of-sight propagation.

Disclosed are systems and methods for multi-point destination arrival time analysis. In one aspect, the system may include a memory storing instructions and at least one processor configured to execute the instructions to. The processor performs operations including receiving a request for an order, receiving an acceptance of an order associated with the first external system, determining, upon receiving the acceptance, a first arrival estimate, determining, upon assigning a delivery worker to fulfill the order, a second arrival estimate, and determining, upon receiving confirmation that the delivery worker has retrieved the order from the merchant, a third arrival estimate. Additionally, the operations may include and forwarding, upon their determination, the first, second, and third arrival estimates to the customer.

A system and methods for estimating the location of a mobile device are disclosed. In accordance with one embodiment, a mobile device located within a first corridor of a building receives (1) a first wireless electromagnetic signal and a first ultrasound signal from a first beacon located in the first corridor, (2) a second wireless electromagnetic signal and a second ultrasound signal from a second beacon located in the first corridor, and (3) a third wireless electromagnetic signal from a third beacon located in a second corridor of the building. The first wireless electromagnetic signal, the first ultrasound signal, the second wireless electromagnetic signal, and the second ultrasound signal are used to estimate a location of the mobile device.

A method for determining location of a premises is disclosed. The method includes measuring a signal strength of a plurality of communication signals received at the premises, obtaining data associated with a source of the signals, estimating a propagation loss for the received signal, determining a distance between a source of each of the signals and the premises based on the estimated propagation loss, and triangulating the location of the premises.

In one embodiment, a service receives signal characteristic data indicative of characteristics of wireless signals received by one or more antennas located in a particular area. The service uses the received signal characteristic data as input to a Bayesian inference model to predict physical states of an object located in the particular area. A physical state of the object is indicative of at least one of: a mass, a velocity, an acceleration, a surface area, or a location of the object. The service updates the Bayesian inference model based in part on the predicted state of the object and a change in the received signal characteristic data and based in part by enforcing Newtonian motion dynamics on the predicted physical states.

A method of location determination with a WiFi transceiver and an AI model includes jointly training, based on various losses: a feature extractor, a location classifier, and a domain classifier. The domain classifier may include a first domain classifier and a second domain classifier. The losses used for training tend to cause feature data from the feature extractor to cluster even if a physical object in an environment has moved after training is completed. Then, the location classifier is able to accurately estimate the position of, for example, a person in a room, even if a door or window has changed from open to close or close to open between the time of training and the time of estimating the person's position.