Abstract: A wearable device, such as a head-wearable display (HWD), identifies a device pose based on a combination of angle-of-arrival (AOA) data generated by, for example, an ultra-wideband (UWB) positioning module, and inertial data generated by an inertial measurement unit (IMU). The HWD fuses the AOA data with the inertial data using data integration techniques such as one or more of stochastic estimation (e.g., a Kalman filter), a machine learning model, and the like, or any combination thereof. A computer device associated with the HWD can employ the fused data to identify a pose of the HWD (e.g., a six degree of freedom (6 DoF) pose) and can use the identified pose to modify virtual reality or augmented reality content implemented by the computer device, thereby providing an immersive and enjoyable experience for a user.

Abstract: Embodiments relate to a MEMS IMU having an automatic gain control. The dynamic measurement range of the MEMS IMU is controlled by controlling the gain of a signal amplifier before the analog to digital converter (ADC) to make full use of the ADC range. In one embodiment, two or more MEMS inertial sensor sets are installed in the IMU. One of the sensor sets is for high accuracy with low dynamic range, and the other set or sets is for higher dynamic range with less resolution or accuracy. In one implementation, a digital processor determines which of the sensor sets to be used according to the system dynamic estimation. In another implementation, the system weights the sensor outputs from the sensor sets according to the system dynamics.

Abstract: A spatially distributed MEMS inertial sensor array is designed to reduce or cancel measurement errors and to increase the signal detection accuracy. By combining the measurements from a pair of sensors having sensing axes in two different, non-orthogonal directions, the correlated error due to the environmental changes are cancelled or reduced and the uncorrelated random noise is also reduced by the square root of two. By sequentially sampling the sensor array in the time domain, the number of the ADC circuits can be largely reduced. A two dimensional signal processing method is used to process the sensor array output data, in order to further reduce the remaining errors. Namely, one dimension uses the outputs from multiple spatially distributed sensor sets and the other dimension is the time domain. The signal to noise ratio is increased by using the signal correlation in both the spatial domain and the time domain.

Abstract: A spatially distributed MEMS inertial sensor array is designed to reduce or cancel measurement errors and to increase the signal detection accuracy. By combining the measurements from a pair of sensors having sensing axes in two different, non-orthogonal directions, the correlated error due to the environmental changes are cancelled or reduced and the uncorrelated random noise is also reduced by the square root of two. By sequentially sampling the sensor array in the time domain, the number of the ADC circuits can be largely reduced. A two dimensional signal processing method is used to process the sensor array output data, in order to further reduce the remaining errors. Namely, one dimension uses the outputs from multiple spatially distributed sensor sets and the other dimension is the time domain. The signal to noise ratio is increased by using the signal correlation in both the spatial domain and the time domain.

Abstract: Embodiments relate to a MEMS IMU having an automatic gain control. The dynamic measurement range of the MEMS IMU is controlled by controlling the gain of a signal amplifier before the analog to digital converter (ADC) to make full use of the ADC range. In one embodiment, two or more MEMS inertial sensor sets are installed in the IMU. One of the sensor sets is for high accuracy with low dynamic range, and the other set or sets is for higher dynamic range with less resolution or accuracy. In one implementation, a digital processor determines which of the sensor sets to be used according to the system dynamic estimation. In another implementation, the system weights the sensor outputs from the sensor sets according to the system dynamics.

Abstract: A shuttle message that is passed between GPS enabled devices that is processed at either a bit level or word level with data that the GPS enable device has already gathered and augmenting the positioning data stored at GPS enabled devices by retrieving data contained in the shuttle message. The shuttle message is then passed with any updated information from a GPS enabled device to one or more other devices such as a mobile station.

Abstract: Method and apparatus for estimating a line bias for a signal received from a common signal source at each of two or more GPS signal receivers. Single difference solutions are obtained for angular orientation of an antenna array and for antenna location, using a two-thread or two-procedure process for determining and updating line bias variation within each of a sequence of time intervals.

Abstract: Methods for estimation of the relative locations (baseline vectors) of two or more GPS signal antennas relative to each other and for estimation of line biases associated with each signal processing channel associated with each GPS signal antenna. Double difference carrier phase signal measurements are formed, using floating integer and fixed integer solutions for the phase integer ambiguities, and baseline vectors between the GPS signal antennas are estimated. Single difference phase integer ambiguities are obtained and approximations for line biases are computed. Single difference carrier phase measurements are used to estimate baseline vectors and line biases, using fixed phase integer ambiguities with the estimated baseline vectors and line biases as initial conditions, and are processed by Kalman filtering or other suitable signal processing. Angular orientation or attitude of a vehicle body on which the GPS signal antennas are placed is determined from the baseline vectors.