The present invention relates to a method and an apparatus for detecting a failure of a sensor device during operation of the sensor device. A test signal is generated in a first frequency band that is above a signal frequency band of the sensor device and fed into a sensor element of the sensor device. A set of samples is obtained, and a magnitude value is derived from said at least two consecutive samples at the first frequency band. The magnitude value is compared to a magnitude threshold value that defines a minimum for the magnitude value and if the magnitude value is below the magnitude threshold value, it is determined that an error has occurred in the sensor device.

A method for operating a capacitive MEMS sensor. The method includes: supplying a defined electrical potential on a deflectably mounted, seismic mass of the MEMS sensor; capacitively inducing a vibrational motion of the seismic mass with the aid of a clocked electrical control voltage; compensating for fluctuations in the supplied electrical potential on the seismic mass caused by the clocked electrical control voltage, by selectively charging and/or discharging an electrical storage element connected to the seismic mass in accordance with the frequency of the clocked electrical control voltage.

A method for operating a capacitive MEMS sensor. The method includes: supplying a defined electrical potential on a deflectably mounted, seismic mass of the MEMS sensor; capacitively inducing a vibrational motion of the seismic mass with the aid of a clocked electrical control voltage; compensating for fluctuations in the supplied electrical potential on the seismic mass caused by the clocked electrical control voltage, by selectively charging and/or discharging an electrical storage element connected to the seismic mass in accordance with the frequency of the clocked electrical control voltage.

An angular rate sensor. The sensor includes a Coriolis vibratory gyroscope (CVG) resonator, configured to oscillate in a first normal mode and in a second normal mode; a frequency reference configured to generate a reference signal; and a first phase control circuit. The first phase control circuit is configured to: measure a first phase difference between: a first phase target, and the difference between: a phase of an oscillation of the first normal mode and a phase of the reference signal. The first phase control circuit is further configured to apply a first phase correction signal to the CVG resonator, to reduce the first phase difference. A second phase control circuit is similarly configured to apply a second phase correction signal to the CVG resonator, to reduce a corresponding, second phase difference.

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.

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.

A signal processing system for a sensor. The system comprises a digital signal processing system configured to set a drive signal frequency for the primary drive transducer, a voltage controlled oscillator configured to receive an input indicative of the resonant frequency and to generate a first periodic signal at a first multiple of the resonant frequency, and a first phase locked loop, configured to receive the first periodic signal, and to generate a second periodic signal at a second multiple of the resonant frequency. The first and second periodic signals are used to control the operation of an analog-to-digital converter (ADC) configured to sample the primary pick off signal and a digital-to-analog converter (DAC) configured to generate a drive signal waveform applied to the primary drive transducer.

A signal processing system for a sensor. The system comprises a digital signal processing system configured to set a drive signal frequency for the primary drive transducer, a voltage controlled oscillator configured to receive an input indicative of the resonant frequency and to generate a first periodic signal at a first multiple of the resonant frequency, and a first phase locked loop, configured to receive the first periodic signal, and to generate a second periodic signal at a second multiple of the resonant frequency. The first and second periodic signals are used to control the operation of an analog-to-digital converter (ADC) configured to sample the primary pick off signal and a digital-to-analog converter (DAC) configured to generate a drive signal waveform applied to the primary drive transducer.

Systems and methods for a tightly coupled end-to-end multi-sensor fusion with integrated compensation are described herein. For example, a system includes an inertial measurement unit that produces inertial measurements. Additionally, the system includes additional sensors that produce additional measurements. Further, the system includes one or more memory units. Moreover, the system includes one or more processors configured to receive the inertial measurements and the additional measurements. Additionally, the one or more processors are configured to compensate the inertial measurements with a compensation model stored on the one or more memory units. Also, the one or more processors are configured to fuse the inertial measurements with the additional measurements using a differential filter that applies filter coefficients stored on the one or more memory units. Further, the compensation model and the filter coefficients are stored on the one or more memory units as produced by execution of a machine learning algorithm.

Systems and methods for a tightly coupled end-to-end multi-sensor fusion with integrated compensation are described herein. For example, a system includes an inertial measurement unit that produces inertial measurements. Additionally, the system includes additional sensors that produce additional measurements. Further, the system includes one or more memory units. Moreover, the system includes one or more processors configured to receive the inertial measurements and the additional measurements. Additionally, the one or more processors are configured to compensate the inertial measurements with a compensation model stored on the one or more memory units. Also, the one or more processors are configured to fuse the inertial measurements with the additional measurements using a differential filter that applies filter coefficients stored on the one or more memory units. Further, the compensation model and the filter coefficients are stored on the one or more memory units as produced by execution of a machine learning algorithm.