Abstract: Methods and systems employ at least two motion estimators to form respective estimates of position of a mobile device over time. The estimates of position over time are based on sensor data generated at the mobile device. Each motion estimator is associated with a respective reference frame, and each respective estimate of position includes one or more estimate components. A transformation from the reference frame associated with a second motion estimator to the reference frame associated with a first motion estimator is determined. The transformation is determined based at least in part on at least one estimate component of the one or more estimate components of the estimates of position formed by each of the first and second motion estimators.

Abstract: Methods and systems estimate calibration errors of magnetic sensor measurements collected at mobile devices. Each measurement is associated with a location of one of the mobile devices and has a calibration error. Data from the sensor measurements is partitioned into sets. Each set is associated with a respective calibration error associated with the measurements that generated the data in the set. Pairs of data items are identified, where each pair includes a data item from a first of the sets corresponding to a measurement, associated with a first location, that generated the data in the first set, and a data item from a second of the sets corresponding to a measurement, associated with a second location that is approximately the same as the first location, that generated the data in the first set. The calibration error associated with each of the sets is estimated based in part on the pairs.

Abstract: This invention is directed to a method for mapping and positioning. The method has at least three steps. The first step calls for receiving sensor data based on location and a path of movement of a mobile device. The second step calls for generating, based on the sensor data, a path estimate and a location measurement vector (LMV), wherein the LMV includes one or more measurements at each of the locations from the sensor data from each of the locations. The third step entails generating, based on the sensor data, the path estimate, and the LMV, one or more unique path features.

Abstract: A computer-implemented method for providing a mapping tool with a semi-automatic mode of operation, comprising: retrieving map data relative to an indoor environment from an indoor positioning system (IPS); collecting sensor data from one or more units of a mobile device while the mobile device in transit within the indoor environment and sending the collected sensor data to the IPS; processing the sensor data received by the IPS in order to return to the mobile device one or more paths suggestions; and enabling a user to accept, reject or modify each path suggestion and updating the IPS accordingly.

Abstract: This invention is directed to a method for mapping and positioning. The method has at least three steps. The first step calls for receiving sensor data based on location and a path of movement of a mobile device. The second step calls for generating, based on the sensor data, a path estimate and a location measurement vector (LMV), wherein the LMV includes one or more measurements at each of the locations from the sensor data from each of the locations. The third step entails generating, based on the sensor data, the path estimate, and the LMV, one or more unique path features.

Abstract: A system and method for mapping, tracking, positioning, and navigating in GPS-denied or GPS-inaccurate areas features accurate and automatic generation and update of a pedestrian lane map based on crowd-sourcing unique path features (UPFs) in combination with associated path estimates from a plurality of mobile devices. Searching and matching newly generated UPFs and associated path estimates, to a pedestrian lane map with known UPFs and associated lanes (or lane estimates) provides simultaneous localization and mapping (SLAM) of mobile devices in GPS-denied or GPS-inaccurate areas, including indoors, underground; dense urban streets with high buildings, natural canyons, and similar environments. UPFs are robust to noise and orientation invariant, enabling operation on low cost mobile device sensors and handling of real life human behavior. The innovative UPFs and pedestrian lane map enable route finding, generating guiding instructions, tracking, and analysis of pedestrian traffic.

Abstract: A system and method for mapping, tracking, positioning, and navigating in GPS-denied or GPS-inaccurate areas features accurate and automatic generation and update of a pedestrian lane map based on crowd-sourcing unique path features (UPFs) in combination with associated path estimates from a plurality of mobile devices. Searching and matching newly generated UPFs and associated path estimates, to a pedestrian lane map with known UPFs and associated lanes (or lane estimates) provides simultaneous localization and mapping (SLAM) of mobile devices in GPS-denied or GPS-inaccurate areas, including indoors, underground; dense urban streets with high buildings, natural canyons, and similar environments. UPFs are robust to noise and orientation invariant, enabling operation on low cost mobile device sensors and handling of real life human behavior. The innovative UPFs and pedestrian lane map enable route finding, generating guiding instructions, tracking, and analysis of pedestrian traffic.

Abstract: A Long Term Evolution (LTE) Concentrator and Distributor system and method extends geographical coverage while minimizing Evolved Node B (eNB) deployments. The system and method use a distributed array of Wide Band Receiver Transmitter (WBRT) devices (i.e., RF Heads, RFH, including antennas) connected via wide-band links to a central standard LTE eNB through a novel LTE Concentrator-Distributor (LTE-CD) which is an uplink (smart optimal) concentrator and downlink simulcast distributor. The eNB downlink signal (baseband or modulated RF) is distributed in synchronization (simulcast) through the LTE-CD to all WBRTs for downlink simulcast transmission to all UEs in the coverage area. The WBRTs receive uplink signals from user equipment, UE, devices in a coverage area, send the uplink signals (baseband or modulated RF) to the LTE-CD which optimally combines all received signals into one best uplink signal that is sent (in baseband or modulated RF) to the eNB.

Abstract: Traffic light control using destination information in calendar data of a user device includes storing calendar data including destination information in a plurality of user devices in a community. A next step includes obtaining the calendar data by a community authority coordinator operable to communicate with the user devices through a community network. A next step includes controlling at least one traffic light in the community in response to the destination information. Optionally, the traffic light can be controlled in response to time, date, location, route, and number of vehicles, to alleviate future road congestion.

Abstract: A Long Term Evolution (LTE) Concentrator and Distributor system and method extends geographical coverage while minimizing Evolved Node B (eNB) deployments. The system and method use a distributed array of Wide Band Receiver Transmitter (WBRT) devices (i.e., RF Heads, RFH, including antennas) connected via wide-band links to a central standard LTE eNB through a novel LTE Concentrator-Distributor (LTE-CD) which is an uplink (smart optimal) concentrator and downlink simulcast distributor. The eNB downlink signal (baseband or modulated RF) is distributed in synchronization (simulcast) through the LTE-CD to all WBRTs for downlink simulcast transmission to all UEs in the coverage area. The WBRTs receive uplink signals from user equipment, UE, devices in a coverage area, send the uplink signals (baseband or modulated RF) to the LTE-CD which optimally combines all received signals into one best uplink signal that is sent (in baseband or modulated RF) to the eNB.

Abstract: Traffic light control using destination information in calendar data of a user device includes storing calendar data including destination information in a plurality of user devices in a community. A next step includes obtaining the calendar data by a community authority coordinator operable to communicate with the user devices through a community network. A next step includes controlling at least one traffic light in the community in response to the destination information. Optionally, the traffic light can be controlled in response to time, date, location, route, and number of vehicles, to alleviate future road congestion.