Example navigation aids for increasing the accuracy of a navigation system are disclosed herein. An example method disclosed herein identifying, with an aircraft intent description language (AIDL) aid, an AIDL instruction as associated with a first dynamic activity level of a plurality of dynamic activity levels and determining, with the AIDL aid, an aircraft state to be affected by the AIDL instruction. The example method also includes changing, with a navigation filter, a weighting scheme for a measurement of the aircraft state obtained by an inertial navigation system (INS) of the aircraft and estimating, with the navigation filter, a trajectory of the aircraft based on the weighting scheme and the measurement.

There is provided a positioning apparatus, a positioning method, and a program that make it possible to perform positioning with a high degree of accuracy using an acceleration sensor and an angular velocity sensor. A movement vector is estimated using a machine learning model on the basis of acceleration of a device and an angular velocity of the device, the movement vector indicating a movement amount and a movement direction of the device, the acceleration being detected by an acceleration sensor that is used to detect the acceleration, the angular velocity being detected by an angular velocity sensor that is used to detect the angular velocity. Then, the estimated movement vector is integrated, and a relative position of the device is calculated. For example, the present technology is applicable to a positioning apparatus that measures a position of, for example, a pedestrian.

An apparatus, a system and a method indirectly detect the operational state of a machine among a plurality of operational states and track the movement of a material through a plurality of machines.

Method, system and device for obtaining location information of a mobile device coupled to a wireless network. A request for a location of the device at a first set of times is transmitted by the mobile device toward a first function. A location of the device at the set of times is determined. In response to receipt of the request, the first function replies with an indication of location of the device at the specified times. The mobile device then performs a local tracking operation to determine a location of the device at another time. The local tracking operation is based on the location of the device at the specified times and local input indicative of motion of the device between at least one of the set of times and the other time.

Systems and methods for a single-difference based pre-filter of measurements for use in solution separation framework are provided. In certain embodiments, a navigation system includes at least one receiver configured to receive a plurality of signals transmitted from a plurality of transmitters. The navigation system further includes a processing unit operatively coupled to the navigation system, the processor configured to identify a plurality of measurements associated with the plurality of transmitters. Additionally, executable instructions cause the processing unit to calculate an auxiliary navigation solution based on a calculated single difference between the plurality of measurements; calculate one or more single difference residuals for the auxiliary navigation solution; perform statistical tests on the one or more single difference residuals; and to identify a set of measurements in the plurality of measurements for use in a solution separation method based on the statistical tests.

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.

Systems and/or devices for implementing a tracking device for tracking a position/location and orientation of an object are provided herein. The device comprises one or more sides that define a predetermined shape, and a plurality of inertial measurement units (IMU) mounted to the one or more sides of the predetermined shape. Each IMU is configured to detect movement of the object and generate inertial output data representing a position and/or orientation of the object. Each IMU includes a first sub-sensor and a second sub-sensor. Each IMU is positioned at a predetermined distance and orientation relative to a center point of the tracking device. The device also comprises a controller communicatively coupled to the plurality of IMUs, the controller configured to receive output data from each of the plurality of IMUs, and determine position/location and orientation of the object based on the received output data from the plurality of IMUs and known data points for the predetermined shape to eliminate drift from sensor data.

A method for calculating an INS initial heading angle error includes comparing an RTK trajectory with an INS trajectory under a tilt RTK application scenario, which may achieve heading angle initialization with an accuracy of 1 deg within 2 seconds. An INS trajectory estimation method eliminates accelerometers, and a large initial gyro bias is compensated by averaging the stationary gyro measurement at the beginning of the measurement to ensure the accuracy of the estimated INS trajectory. A rather short initialization duration also greatly improves the measurement efficiency. Compared with common heading initialization methods for the tilt RTK, the inventive method eliminates magnetometers and thus prevents interference from magnetic fields, obtaining stronger adaptability in complex environments.

A system and to a method of analyzing and monitoring interfering movements of an inertial unit of an aircraft during a stage of statically aligning the inertial unit. During the static alignment stage, measurements of the velocity of the aircraft relative to the ground are acquired, and states of a mirror process having a model that is close to the model of the process of aligning the inertial unit are estimated. The states of the mirror process are estimated from observations constituted by the measurements of velocity relative to the ground. Finally, the estimates of the states are compared with respective validation thresholds in order to validate or not validate said alignment of the inertial unit.

The present disclosure relates to a positioning apparatus and method and a self-moving device. The positioning apparatus includes a first positioning module (101), a sensor module (102), and a processing module (103). The position of the positioning apparatus is determined according to the positioning result of the first positioning module (101) and the positioning result determined by using the sensor module (102) to measure the acceleration and the angle parameter and based on a pedestrian dead reckoning algorithm, and determine the boundary of the self-moving device. A pedestrian dead reckoning technology independent of an external environment is introduced during boundary positioning, and the pedestrian dead reckoning technology and other positioning technologies are integrated to establish a virtual boundary, so that positioning precision is high, and a precise boundary is established. In addition, it is not necessary to arrange a physical boundary, so that operations of a user are less complex.