An approach is provided for calibrating vehicle motion data using a rotation matrix calculated based on mobile device sensor data, thereby determining vehicle events (e.g., forward acceleration, stoppages, etc.). The approach, for example, involves determining a road segment that meets one or more criteria for straightness, inclination, or a combination thereof. The approach also involves collecting sensor data from at least one sensor of a mobile device associated with a vehicle in motion on the road segment based on the determination. The sensor data indicates one or more acceleration vectors in a mobile device frame of reference. The approach further involves calibrating the one or more acceleration vectors from the mobile device frame of reference to a vehicle frame of reference based on the sensor data. The approach further involves providing the one or more calibrated acceleration vectors as an output.

Various embodiments are directed to improvements to sensor calibration systems, methods, and configurations. Subject system improvements and configurations facilitate the manufacturing process of such sensors, and of devices containing such sensors, to be dramatically simplified, reducing or eliminating the need for costly, dedicated calibration steps directly in the manufacturing process. Such configurations have application and relevance in the design and manufacture of such sensors requiring calibration, as well as in the design and manufacture of larger devices containing such sensors requiring calibration as subcomponent(s), with direct impacts to many market segments, including, without limitation, visualization systems of various types, autonomous vehicles, and security systems and the like.

Various embodiments are directed to improvements to sensor calibration systems, methods, and configurations. Subject system improvements and configurations facilitate the manufacturing process of such sensors, and of devices containing such sensors, to be dramatically simplified, reducing or eliminating the need for costly, dedicated calibration steps directly in the manufacturing process. Such configurations have application and relevance in the design and manufacture of such sensors requiring calibration, as well as in the design and manufacture of larger devices containing such sensors requiring calibration as subcomponent(s), with direct impacts to many market segments, including, without limitation, visualization systems of various types, autonomous vehicles, and security systems and the like.

A piezoelectric transducer for measuring a force includes a base element; a pre-loading element; at least one effective main seismic mass aggregation of pre-loaded parts capable of producing the force when being accelerated; a main piezoelectric ceramic element including a first piezoelectric ceramic; at least one compensation seismic mass aggregation of pre-loaded parts capable of producing a compensation force when being accelerated; a compensation piezoelectric ceramic element including a second piezoelectric ceramic. The first piezoelectric ceramic has a thermal sensitivity shift smaller than the second piezoelectric ceramic. The main piezoelectric ceramic element is oriented with respect to the force to be measured and the compensation piezoelectric ceramic element is oriented with respect to the compensation force such that the main electric charge and the compensation electric charge are opposite in polarity.

A sensor system includes a sensor element, a signal processing circuit, and a pseudo-signal correction circuit. The sensor element outputs an electric signal corresponding to an external force. The signal processing circuit converts the electric signal coming from the sensor element into a signal having a certain signal format and then outputs the signal thus converted. The pseudo-signal correction circuit corrects a pseudo-signal outputted by the sensor element. When receiving a test signal, the sensor element performs a self-diagnosis based on the test signal and then outputs the pseudo-signal, which represents a result of the self-diagnosis. The pseudo-signal correction circuit corrects the pseudo-signal based on environment information about an environment where at least one of the sensor element or the signal processing circuit is located.

A sensor system includes a sensor element, a signal processing circuit, and a pseudo-signal correction circuit. The sensor element outputs an electric signal corresponding to an external force. The signal processing circuit converts the electric signal coming from the sensor element into a signal having a certain signal format and then outputs the signal thus converted. The pseudo-signal correction circuit corrects a pseudo-signal outputted by the sensor element. When receiving a test signal, the sensor element performs a self-diagnosis based on the test signal and then outputs the pseudo-signal, which represents a result of the self-diagnosis. The pseudo-signal correction circuit corrects the pseudo-signal based on environment information about an environment where at least one of the sensor element or the signal processing circuit is located.

An optomechanical gravimeter includes: a first and second accelerometer; and a spacer member interposed between the first accelerometer and the second accelerometer such that the first accelerometer and the second accelerometer independently include: a basal member; a test mass disposed on the basal member; a flexural member interposed between the basal member and the test mass such that the test mass is moveably disposed on the basal member via flexing of the flexural member; an armature disposed on the basal member and opposing the test mass and the flexural member such that: the armature is spaced apart from the test mass; a cavity including: a first mirror disposed on the test mass; a second mirror disposed on the armature, the spacer member providing a substantially constant distance of separation between a first measurement point of the first accelerometer and a second measurement point of the second accelerometer.

Methods, apparatuses, and systems for collecting the installation error of the wind vane are provided. The image of the blades of the wind turbine and the outer rotor of the generator is obtained. It is determined whether the wind vane is aligned with the center line of the wind turbine, according to a relationship between the center line of the wind turbine and the orienting plane of the wind vane in the image. In a case that the wind vane is not aligned with the center line of the wind turbine, the deviation angle between the wind vane and the center line of the wind turbine is calculated, and a direction of the wind vane is corrected according to the deviation angle. Therefore, installation errors of the wind vane are accurately determined and corrected, and accuracy is improved for installation of the wind vane.

Methods, apparatuses, and systems for collecting the installation error of the wind vane are provided. The image of the blades of the wind turbine and the outer rotor of the generator is obtained. It is determined whether the wind vane is aligned with the center line of the wind turbine, according to a relationship between the center line of the wind turbine and the orienting plane of the wind vane in the image. In a case that the wind vane is not aligned with the center line of the wind turbine, the deviation angle between the wind vane and the center line of the wind turbine is calculated, and a direction of the wind vane is corrected according to the deviation angle. Therefore, installation errors of the wind vane are accurately determined and corrected, and accuracy is improved for installation of the wind vane.

A posture estimation method includes calculating a posture change amount of an object based on an output of an angular velocity sensor, predicting posture information of the object by using the posture change amount, limiting a bias error in a manner of limiting a bias error component of an angular velocity around a reference vector in error information, and correcting the predicted posture information of the object based on the error information, the reference vector, and an output of a reference observation sensor.