Trusted Positioning | Date: 2014-01-23
A method and apparatus for providing an enhanced navigation solution for cycling applications is described herein. The navigation solution is about a device within a platform, which is a cycling platform such as for example a bicycle, a tricycle, or a unicycle amongst others. The device can be in any orientation with respect to the platform (such as for example in any location or orientation on the body of the cyclist). The device includes a sensor assembly. The sensors in the device may be for example, accelerometers, gyroscopes, magnetometers, barometer among others. The present method and apparatus can work whether in the presence or in the absence of navigational information updates (such as, for example, Global Navigation Satellite System (GNSS) or WiFi positioning).
Trusted Positioning | Date: 2012-03-22
The present disclosure relates to methods of enhancing a navigation solution about a device and a platform, wherein the mobility of the device may be constrained or unconstrained within the platform, and wherein the navigation solution is provided even in the absence of normal navigational information updates (such as, for example, GNSS). More specifically, the present method comprises utilizing measurements from sensors (e.g. accelerometers, gyroscopes, magnetometers etc.) within the device to calculate and resolve the attitude of the device and the platform, and the attitude misalignment between the device and the platform.
Trusted Positioning | Date: 2014-01-30
The present disclosure relates to a method and system for estimating varying step length for on foot motion (such as for example walking or running). The present method and apparatus is able to be used in anyone or both of two different phases. In some embodiments, the first phase is used. In some other embodiments, the second phase is used. In a third group of embodiments, the first phase is used, and then the second phase is used. The first phase is a model-building phase done offline to obtain the nonlinear model for the step length as a function of different parameters that represent human motion dynamics. A nonlinear system identification technique is used for building this model. In the second phase the nonlinear model is used to calculate the step length from the different parameters that represent human motion dynamics used as input to the model. These parameters are obtained from sensors readings from the sensors in the apparatus. This second phase is the more frequent usage of the present method and apparatus for a variety of applications.
Trusted Positioning | Date: 2015-08-20
An apparatus and method are disclosed for enhancing a navigation solution of a portable device and a platform. Motion sensor data may be obtained corresponding to motion of the portable device, such that a first filter may be configured to output a navigation solution and at least one second filter may be configured to use the motion sensor data to generate at least one value. The at least one generated value may then be used with the first filter to enhance the navigation solution output by the first filter.
Trusted Positioning | Date: 2014-06-16
A navigation module and method for providing an INS/GNSS navigation solution for a moving platform, comprising a receiver for receiving absolute navigational information from an external source (e.g., such as a satellite), means for obtaining speed or velocity information and an assembly of self-contained sensors capable of obtaining readings (e.g., such as relative or non-reference based navigational information) about the moving platform, and further comprising at least one processor, coupled to receive the output information from the receiver, sensor assembly and means for obtaining speed or velocity information, and operative to integrate the output information to produce a navigation solution. The at least one processor may operate to provide a navigation solution by using the speed or velocity information to decouple the actual motion of the platform from the readings of the sensor assembly.