Provided are methods and devices for determining a stop period of a mobile device in a location, according to geolocation data available on the mobile device. A predicted position of the mobile device and an associated level of uncertainty related to the predicted position is determined according to either the current position of the mobile device if it is available, or, if not, according to an artificial position obtained from a previously known position of the mobile device. The mobile device is considered to be in a stop period if the normalized prediction error between the predicted position and either the current position or the artificial position, depending on the situation, is smaller than a prediction error threshold. Upon detection of a stop period, an action on the mobile device is triggered.

An information processing device according to the present application includes an acquisition unit and a determination unit. The acquisition unit acquires time-series position information acquired by a terminal device. The determination unit determines whether relation between one or more pieces of interest position information included in the time-series position information acquired by the acquisition unit and prior or posterior position information in time series satisfies a predetermined criterion, and determines whether to hold the interest position information based on a determination result.

Methods and apparatus for processing and using signals transmitted by a device, e.g., a low cost beacon transmitter device, to facilitate making location determinations with regard to the transmitting device and/or making a decision of when or how to use location information generated based on received signals are described. In accordance with some features the processing performed on the received signal strength measurements is based on whether or not the device from which the signals are received is in motion. The size of a sample period used as a processing window when determining device location is based, in some embodiments, on the rate of motion. When and/or how to use location determinations are performed is also based on motion in some embodiments. Machine learning updates of location determination parameters, based on received signals, are disabled when the signals are from devices determined to be in motion.

A method can include receiving sensor data, receiving satellite observations, determining a positioning solution (e.g., PVT solution, PVA solution, kinematic parameters, etc.) based on the sensor data and the satellite observations. A system can include a sensor, a GNSS receiver, and a processor configured to determine a positioning solution based on readings from the sensor and the GNSS receiver.

Systems, apparatuses, and methods are provided herein for updating directions to a container. In one embodiment an apparatus for updating directions to a container comprises a container housing, a movement sensor attached to the container housing, a wireless transceiver; and a control circuit coupled to the movement sensor and the wireless transceiver. The control circuit being configured to: detect a relocation of the container housing via the movement sensor, determine an estimated new location of the container, and send, via the wireless transceiver, the estimated new location to a computing device to initiate an update of a direction to the container.

A method and system for combining data obtained by sensors, having particular application in the field of navigation systems, are disclosed. The techniques provide significant improvement over state-of-the-art Markovian methods that use statistical noise filters such as Kalman filters to filter data by comparing instantaneous data with the corresponding instantaneous estimates from a model. In contrast, the techniques disclosed herein use multiple time periods of various lengths to process multiple sensor data streams, in order to combine sensor measurements with motion models at a given time epoch with greater confidence and accuracy than is possible with traditional single epoch methods. The techniques provide particular benefit when the first and/or second sensors are low-cost sensors (for example as seen in smart phones) which are typically of low quality and have large inherent biases.

Disclosed are devices, systems and methods for combining observations obtained at two different mobile devices attached to a human user for performing a navigation operation. For example, observations of a signal acquired at a first mobile device may be selected for computing a position fix based, at least in part, on a utility indicator associated with the observations.

In one embodiment, an apparatus is presented that detects wireless signals from external devices that uniquely identify each of the external devices, records, in memory, information about the external devices without access to an external database, and compares information from the external devices to determine a relative location of the wearable device without using additional, power-hungry position location functionality if there is a threshold match in the compared information. In some embodiments, the invention uses the determined relative location to trigger an action at another device. The invention, using self-contained functionality, enables improvements in same location or home location determination accuracy, memory conservation, and power consumption.

Disclosed are devices, systems and methods for combining observations obtained at two different mobile devices attached to a human user for performing a navigation operation. For example, observations of a signal acquired at a first mobile device may be selected for computing a position fix based, at least in part, on a utility indicator associated with the observations.

A positioning device includes a first storage device having stored therein received signal strength data in time series according to mobile device data, a second storage device having stored therein relative movement data of the mobile device in time series within a relative coordinate system, and a processor configured to calculate a first neighboring time at which the mobile device becomes closest to a first base station in the plurality of base stations based on the received signal strength data and a second neighboring time at which the mobile device becomes closest to a second base station of the plurality of base stations, and convert the relative movement data in the second storage device into position coordinates data specifying an absolute position of the mobile device in an absolute coordinate system using the first base station as a reference point from the first neighboring time to the second neighboring time.