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.

The disclosed subject matter includes a method and system for navigating an unmanned ground vehicle (UGV), that include: generating, based on the scanning output data, a first map comprising a first group of cells and characterized by a first size; generating, based on the scanning output data, a second map representing an area smaller than that of the first map comprising a second group of cells, which are characterized by a second size being smaller than the first size; wherein each cell in the first group of cells and the second group of cells is classified to a class selected from at least two classes, comprising traversable and non-traversable, wherein the second part at least partly overlaps the first part; navigating the UGV based on data deduced from crossing between cells in the first map and second map.

Provided are methods for semantic annotation of sensor data using unreliable map annotation inputs, which can include training a machine learning model to accept inputs including images representing sensor data for a geographic area and unreliable semantic annotations for the geographic area. The machine learning model can be trained against validated semantic annotations for the geographic area, such that subsequent to training, additional images representing sensor data and additional unreliable semantic annotations can be passed through the neural network to provide predicted semantic annotations for the additional images. Systems and computer program products are also provided.

A physical quantity sensor includes a substrate, a movable body that includes a movable drive electrode, a movable detection electrode, and a connection portion for connecting the movable drive electrode and the movable detection electrode and is allowed to vibrate along a first axis with respect to the substrate, a fixed drive electrode that is fixed to the substrate, is disposed to face the movable drive electrode, and vibrates the movable body along the first axis, and a fixed monitor electrode that is fixed to the substrate, is disposed to face the movable detection electrode and detects vibration of the movable body along the first axis.

The invention provides an inertial sensing device the capability to achieve self-alignment (sensor error compensation), by using dual-rotation modulation technique. The self-alignment process is performed based on fully building the sensor's mathematical model and rotating the inertial sensor blocks in a specific order. The advantages of this technology are fast calibration time, high accuracy, and the ability to separate independent movements on the axes of the inertial sensor. The inertial sensor based on a dual-rotation modulation platform is applied to marine and aeronautical fields.

A heavy goods vehicle includes a displacement calculator that calculates a displacement by multiplying an arc length per unit rotation angle of the outer circumference of a specified tire by the first physical quantity, a vehicle position estimator that estimates a vehicle position using the displacement, and a memory that stores a correlation between a second physical quantity corresponding to a loading weight and an arc length per predetermined rotation angle at the outer circumference of the specified tire. The displacement calculator refers to the correlation to calculate a current arc length per unit rotation angle at the outer circumference of the specified tire from the second physical quantity corresponding to the loading weight, and calculates the displacement by multiplying the first physical quantity detected by the rotation amount detector by the current arc length per unit rotation angle.

This provides methods and systems for the global navigation satellite system (GNSS) combined with the dead-reckoning (DR) technique, which is expected to provide a vehicle positioning solution, but it may contain an unacceptable amount of error due to multiple causes, e.g., atmospheric effects, clock timing, and multipath effect. Particularly, the multipath effect is a major issue in the urban canyons. This invention overcomes these and other issues in the DR solution by a geofencing framework based on road geometry information and multiple supplemental kinematic filters. It guarantees a road-level accuracy and enables certain V2X applications which does not require sub-meter accuracy, e.g., signal phase timing, intersection movement assist, curve speed warning, reduced speed zone warning, and red-light violation warning. Automated vehicle is another use case. This is used for autonomous cars and vehicle safety, shown with various examples/variations.

A positioning method includes: acquiring the credibility of each positioning subsystem in different states and generating a credibility data table; acquiring the real-time credibility from the corresponding credibility data table according to real-time positioning data of each positioning subsystem; calculating a first information distribution weight coefficient of each positioning subsystem involving a fusion operation of an filter according to the real-time credibility of each positioning subsystem; respectively feeding back, by a main filter, a second information distribution weight coefficient of each positioning subsystem involving the fusion operation to each sub-filter according to global data; determining a final information distribution weight coefficient of each positioning subsystem involving the fusion operation according to the first information distribution weight coefficient and the second information distribution weight coefficient; and performing, by the filter, the fusion operation according to the final information distribution weight coefficient of each positioning subsystem and outputting a final positioning result.

A method for determining a current estimated gyroscope bias of a gyroscope, the gyroscope being configured to output rotation rate data, the method comprising: receiving first rotation rate data associated with a first time from the gyroscope, the first rotation rate data comprising a first rotation rate reading that indicates a rotation rate of the gyroscope about a first axis; calculating a rotation rate moving average associated with the first time based on the first rotation rate data and a rotation rate moving average associated with a second time earlier than the first time; calculating a moving standard deviation associated with the first time based on the first rotation rate data, the rotation rate moving average associated with the first time, and a moving standard deviation associated with the second time; determining if the moving standard deviation associated with the first time is less a threshold moving standard deviation; and in response the moving standard deviation being less than the threshold moving standard deviation, using the first rotation rate reading to update the current estimated gyroscope bias.

Methods, apparatus and systems for wireless tracking with graph-based particle filtering are described. A described wireless monitoring system comprises a transmitter transmitting a series of probe signals, a receiver, and a processor. The receiver is configured for: receiving the series of probe signals modulated by the wireless multipath channel and an object moving in a venue, and obtaining a time series of channel information (TSCI) of the wireless multipath channel from the series of probe signals. The processor is configured for: monitoring a motion of the object relative to a map based on the TSCI, determining an incremental distance travelled by the object in an incremental time period based on the TSCI, and computing a next location of the object at a next time in the map based on at least one of: a current location of the object at a current time, the incremental distance, and a direction of the motion during the incremental time period.