Abstract: A method and system of resilient UWB target localization for a vehicle are provided. The system comprises a UWB tag arranged to be mobile and trackable by way of a sensor signal and at least three UWB anchors. Each anchor is in communication with the tag. The system further comprises a gateway in communication with the anchors. The gateway comprises an ECU arranged to receive sensor signals from UWB anchors. The ECU comprises a preprocessing module, a clustering module, and a Bayesian module. The preprocessing module is arranged to align sensor signals at an aligned timestamp to define aligned data. The clustering module is arranged to cluster points of intersections, defining a sensed location for each cluster. The Bayesian module is arranged to determine a real-time location of the tag based on a Bayesian probability function to match the sensed location with a predicted location of the tag.

Abstract: Systems and methods for diagnosing sensor performance of a UWB sensor localization are provided. The system comprises a UWB tag, at least four UWB anchors, and a gateway. The gateway comprises an ECU arranged to receive sensor signals from the UWB anchors. The ECU comprises a preprocessing module arranged to align the sensor signals defining aligned data and is arranged to determine intersections of the aligned data defining points of intersections. The system further comprises a clustering module arranged to cluster the points of intersections defining at least one cluster of points of the UWB anchors to calculate a clustering quality and a clustering variance of each of the at least one cluster. The ECU is arranged to find a clustering contribution of each anchor defining a first contribution low of one of the anchors and is arranged to determine an erratic anchor based the first contribution low.

Abstract: A method and system of resilient UWB target localization for a vehicle are provided. The system comprises a UWB tag arranged to be mobile and trackable by way of a sensor signal and at least three UWB anchors. Each anchor is in communication with the tag. The system further comprises a gateway in communication with the anchors. The gateway comprises an ECU arranged to receive sensor signals from UWB anchors. The ECU comprises a preprocessing module, a clustering module, and a Bayesian module. The preprocessing module is arranged to align sensor signals at an aligned timestamp to define aligned data. The clustering module is arranged to cluster points of intersections, defining a sensed location for each cluster. The Bayesian module is arranged to determine a real-time location of the tag based on a Bayesian probability function to match the sensed location with a predicted location of the tag.

Abstract: Systems and methods of diagnosing sensor performance of an ultra wide band (UWB) sensor localization for a vehicle are provided. The method comprises receiving sensor signals from at least four UWB anchors and a UWB tag for a time period. The sensor signals represent anchor coordinates and real-time distances between the tag and each anchor. The method comprises aligning the sensor signals to define aligned data. The method comprises calculating a predicted location of the UWB tag based on the aligned data and a least square of error to define a first constructed matrix and a second constructed matrix. The method comprises determining a local error of each of the at least four UWB anchors based on the least square of error and comparing each local error with an error threshold to define a first threshold high. The method comprises determining an erratic anchor based on the first threshold high.

Abstract: Embodiment of the present disclosure discloses a method and an apparatus for generating human pose images based on Wi-Fi signals. The method for generating human pose images based on Wi-Fi signals comprises: receiving Wi-Fi signals through one or more antennas; obtaining channel state information from the Wi-Fi signals; extracting channel state information components related to human pose changes from the channel state information; constructing a mapping model between the Wi-Fi signals and the human poses to map the channel state information components related to the human pose changes to a human pose image sequence.