Techniques for a pose component that may determine a pose are described herein. A pose may refer to the inertial pose or position of a vehicle which may be updated in real-time or near real-time. For example, the techniques may include receiving a plurality of input signals at a pose component and monitoring the plurality of input signals. The pose component may determine, based on the monitoring of the plurality of input signals, a particular pose update algorithm of a plurality of pose update algorithms for determining the pose and determine, using the particular pose update algorithm, the pose based the plurality of input signals and IMU measurements associated with a primary IMU.

The disclosure relates to a method for measuring the variance in a measurement signal, comprising the following steps: filtering the measurement signal by means of a high-pass filter in order to obtain a filtered measurement signal; determining the variance by using the filtered measurement signal.

The present technology relates to an information processing apparatus and an information processing method that make it possible to properly report the reliability of information that is available with use of an inertial sensor. The information processing apparatus includes a state estimation section that estimates a state of a predetermined object and an output controller that controls, on the basis of the estimated state of the object, output of reliability information indicating the reliability of object information of the object, the object information of the object being available with use of an inertial sensor. The present technology is applicable to a portable information terminal such as a smartphone or a wearable device, for example.

A location-estimating device comprises a processor configured to acquire first locational information representing the location of a moving object for each first information acquisition time and calculate a first estimated location, acquire second locational information representing the location of the moving object for each second information acquisition time and calculate a second estimated location, and when the first estimated location has been determined, calculate the first movement amount and moving direction between the first information acquisition time and current time, and estimate the location at the current time based on the first estimated location, first movement amount and moving direction, or when the first estimated location is not determined, calculate a second movement amount and moving direction between the preceding second information acquisition time and current time, and estimate the location at the current time based on the second estimated location, second movement amount and moving direction.

A method of determining the orientation of a device having disposed therein, in part, an inertia measurement unit, a phased array receiver, and a controller, includes, in part, detecting the difference between phases of an RF signal received by at least a pair of receive elements of the phased array receiver, determining the angle of incidence of the RF signal from the phase difference, using the angle of incidence to determine the projection of a vector on a plane of an array of transmitters transmitting the RF signal, and determining the yaw of the device from the projection of the vector. The vector is a three-dimensional vector representative of the orientation of the plane of the phased array receivers relative to the plane of the array of transmitters transmitting the RF signal.

The subject matter disclosed herein relates to the control and utilization of multiple sensors within a device. For an example, motion of a device may be detected in response to receipt of a signal from a first sensor disposed in the device, and a power state of a second sensor also disposed in the device may be changed in response to detected motion.

Dead reckoning correction utilizing patterned light projection is provided herein. An example method can include navigating a drone along a pattern using dead reckoning, the pattern having a plurality of lines, detecting one of the plurality of lines using an optical sensor of the drone, determining when a line of travel of the drone is not aligned with the one of the plurality of lines, and realigning the line of travel of the drone so as to be aligned with the one of the plurality of lines to compensate for drift that occurs during navigation using dead reckoning.

Disclosed is a self-adaptive horizontal attitude measurement method based on motion state monitoring. Based on a newly established state space model, a horizontal attitude angle is taken as a state variable, an angular velocity increment Δω.sup.b for compensating a random constant zero offset is taken as a control input of a system equation, and a specific force f.sup.b for compensating the random constant zero offset is taken as a measurement quantity. Meanwhile, judgment conditions for a maneuvering state of a carrier are improved, and maneuvering information of the carrier is judged by comprehensively utilizing acceleration information and angular velocity information output by a micro electro mechanical system inertial measurement unit (MEMS-IMU), whereby a measurement noise matrix of a filter can be automatically adjusted, thereby effectively reducing the influence of carrier maneuvering on the calculation of a horizontal attitude. The method has no special requirement on the maneuvering state of the carrier, and can ensure that the system has high attitude measurement precision in different motion states without an external information assistance.

A method, a device and a system for safely and reliably performing real-time speed measurement and continuous positioning are provided. With the method, inertial navigation data from an inertial navigation signal source arranged in a train is detected, and correction data from a correction signal source is detected. In a case that no correction data is detected, a current speed and a current position of the train is determined based on the inertial navigation data, and in a case that the correction data is detected, the inertial navigation data is corrected with the correction data, and a current speed and position of the train are determined based on the corrected inertial navigation data. Therefore, even in the case that no correction data is detected, the real-time speed measurement and continuous positioning can be performed safely and reliably based on the inertial navigation data.

A strict reverse navigation method for optimal estimation of fine alignment is provided. The strict reverse navigation method including: establishing an adaptive control function; performing a forward navigation calculation process; performing a reverse navigation calculation process; and performing the adaptive control for a number of forward and reverse calculations. The strict reverse navigation method shortens an alignment time for the optimal estimation of fine alignment while ensuring an alignment accuracy. The strict reverse navigation method provided effectively solves a problem that an error of an initial value of filtering in an initial stage of the optimal estimation of fine alignment affects convergence speeds of subsequent stages. In the initial stage, a larger number of the forward and reverse navigation calculations are adopted to reduce an error of the initial value as much as possible and increase a convergence speed of the filtering.