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.

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.

This application discloses an initialization method, device, medium and electronic equipment of an integrated navigation system. The method comprises: acquiring the current motion state estimation of the aircraft and the sensor measurement values; acquiring the motion state estimation values by filtering according to the current motion state estimation and the attributes of the sensor measurement values, and obtaining the motion state estimation prediction results by performing the prediction based on the motion state estimation values; inputting the motion state estimation values into Kalman linear filter to initialize the Kalman linear filter if the motion state estimation prediction results converge; and correcting the integrated navigation system according to the initialized Kalman linear filter.

Systems, device configurations, and processes are provided for tracking and navigation using low-earth orbit satellite (LEO) signals. Embodiments are provided to track LEO satellites in the absence or during interrupted service by global position sources (e.g., GNSS). Operations and a framework are provided to use low-earth orbit (LEO) downlink transmissions as a source of positioning data. Operations can include performing a Doppler frequency measurement on received satellite downlink transmissions to determine a pseudorange rate measurement for a vehicle relative to at least one LEO satellite. Pseudorange rate measurements may be used to correct vehicle position data of a vehicles inertial navigation system (INS) and for control/navigation of the vehicle. Embodiments allow for simultaneous tracking of LEO satellites and navigation of a vehicle, such as an unmanned aerial vehicle. Embodiments are also directed to employing a propagation model for LEO position and velocity within a simultaneous tracking and navigation (STAN) framework.

A method for calibrating a yaw rate sensor of a vehicle, comprises detecting a yaw rate of the vehicle from measurement data from the yaw rate sensor. A change in yaw angle is ascertained from sensor data from at least one optical surroundings sensor unit, wherein an offset of the yaw rate sensor is ascertained, the offset being ascertained by fusion of the detected yaw rate and the ascertained change in yaw angle. The yaw rate sensor is calibrated according to the ascertained offset.

A vehicle traveling control method for causing a vehicle to travel along a traveling road where magnetic markers are arrayed is a control method including an azimuth measuring process of performing a process on angular velocity, which is an output of a gyro sensor, and measuring a measured azimuth indicating an orientation of the vehicle, a control process of controlling the vehicle so that the measured azimuth is matched with a target azimuth corresponding to a direction of the traveling road, and a correction process of correcting a degree of control by the control process, in order to bring a lateral shift amount of the vehicle with reference to each of the magnetic markers closer to zero, in accordance with the lateral shift amount.

A tight-integrated navigation method assisted by Elman neural network when GNSS signals are blocked based on the tight-integrated navigation system model of the INS and GNSS, where the dynamic Elman neural network prediction model is used to train the inertial navigation error model and the GNSS compensation model, so as to solve the problem of tight-integrated navigation when the GNSS signals are blocked. When the GNSS signals are blocked, the trained neural network is used to predict the output error of GNSS and compensate the output of inertial navigation, so that the error will not diverge sharply, and the system can continue to work in the integrated navigation mode. The low-cost tight-integrated navigation module is used, and the collected information is preprocessed to form the sample data for training the neural network to train the Elman neural network model.

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.

Training at the proper level of effort is important for athletes whose objective is to achieve the best results in the least time. In running, for example, pace is often monitored. However, pace alone does not reveal specific issues with regard to running form, efficiency, or technique, much less inform how training should be modified to improve performance or fitness. A sensing system and wearable sensor platform described herein provide real-time feedback to a user/wearer of his power expenditure during an activity. In one example, the system includes an inertial measurement unit (IMU) for acquiring multi-axis motion data at a first sampling rate, and an orientation sensor to acquire orientation data at a second sampling rate that is varied based on the multi-axis motion data.

In a locator device, a dead reckoning part calculates a position of a subject vehicle by dead reckoning. A pseudorange smoothing part smooths a pseudorange between a GNSS satellite and a position of the subject vehicle using a carrier wave phase of the GNSS satellite. A GNSS receiver positioning error evaluation part evaluates reliability of the position of the subject vehicle calculated by the multi-GNSS receiver. A GNSS positioning part (Kalman Filter (KF) method) calculates a position of the subject vehicle from a smoothed value of the pseudorange, a positioning augmentation signal, and an orbit of the GNSS satellite. A complex positioning part (KF method) calculates an error in the dead reckoning from the position of the subject vehicle calculated by the GNSS positioning part (KF method), and corrects the position of the subject vehicle calculated by the dead reckoning part based on the error in the dead reckoning.