12 results about "Mems imu" patented technology

The invention requests to protect an MEMS IMU sensor anti-interference method based on an auto-encoder. The method relates to the technical field of MEMS IMU sensor anti-interference, and comprises the following steps: acquiring normal working data of an MEMS IMU sensor and interference data of different types and different intensities, preprocessing the data to construct a data set, and training an AE self-encoding model fusing feature extraction and classification evaluation functions; in practical application, the model receives sensor output data in real time, judges whether interference exists or not through reconstruction errors, outputs interference types and interference intensity levels through a built-in classification head and an intensity evaluation head, and provides an accurate decision basis for subsequent anti-interference coping strategies. According to the method, the features do not need to be manually designed, rapid identification, accurate classification and intensity quantification of interference are realized by virtue of the deep feature automatic extraction capability of AE self-coding, the method has the characteristics of strong real-time performance, excellent generalization capability and wide adaptability, and the technical problems of weak pertinence and low identification precision of an existing MEMS sensor anti-interference method are effectively solved.

The application discloses a method for realizing filter fast convergence based on improved Sage-Husa filtering when high-precision inertial measurement unit (IMU) is switched to low-precision MEMS IMU in an unmanned aerial vehicle (UAV) combined navigation system, and relates to the field of UAV navigation. The method monitors the state of the high-precision IMU in real time, triggers the switching when the high-precision IMU fails, adjusts the initial value of the state estimation covariance matrix based on the error characteristics of the MEMS IMU at the switching moment, adopts the attenuated memory Sage-Husa filtering combined with the recursive updating mechanism, estimates the process noise variance matrix and the measurement noise variance matrix in real time and adaptively, and ensures the switching result stable through the innovation monitoring and the strong tracking mechanism. The application solves the result jump problem caused by the great change of noise characteristics in the switching process of the high-precision IMU and the MEMS IMU, and improves the stability and accuracy of the UAV navigation system.

The invention discloses a multi-frequency-point interference suppression method of a low-cost array gyroscope. The multi-frequency-point interference suppression method comprises the following steps: establishing an interference noise model; carrying out multi-frequency-point interference suppression by adopting an interference suppression algorithm; establishing a constraint of the interference suppression algorithm; the input of the interference suppression algorithm is a signal with an interference noise frequency point, and the output of the interference suppression algorithm is mean value fused data which passes through the interference suppression algorithm of a plurality of frequency points and temperature compensation. Under the condition that the low-frequency interference noise is effectively suppressed, the expected performance improvement of the MEMS IMU array is realized, and the effectiveness of the interference suppression algorithm in the high-density MEMS IMU array is verified.

This invention claims protection for a system and method for generating adaptive interference strategies driven by a MEMS IMU characteristic spectrum library. The aim is to precisely interfere with the normal operation of a MEMS IMU using external excitation at a specific resonant frequency. This system constructs a mainstream MEMS IMU characteristic spectrum library on the market, combining high-precision frequency sweep detection with a library-driven adaptive interference strategy to quickly lock onto the resonant frequency of the target MEMS IMU and implement effective interference. In multiple experiments, the system successfully interfered with the tested MEMS IMU devices, increasing the output error of the target devices by a hundredfold, demonstrating extremely high interference effectiveness. The core of this invention lies in the combination of a resonant frequency matching interference mechanism driven by a MEMS IMU characteristic spectrum library and a dynamic interference strategy, providing an innovative solution for interference applications in related fields.

The application provides a COA (Coati Optimization Algorithm) attitude solution method based on MEMS IMU auxiliary search, which comprises the following steps: collecting IMU and BDS double-antenna data in a city street where GNSS signals are weak; calculating a pitch angle and a yaw angle through MEMS IMU output data to determine an optimization algorithm search boundary; defining a fitness function to be optimized according to a GNSS carrier phase observation model and an attitude observation model; introducing an adaptive dynamic parameter adjustment mechanism to accelerate the convergence process of the algorithm and effectively avoid falling into a local optimal solution; aiming at the problem of too large jumping or slow convergence speed in the solution space, a step length adaptive adjustment mechanism is proposed to improve the efficiency and precision of the optimization process; and the improved COA is applied to attitude search and optimization within the space range determined by the inertial navigation system to solve the accurate attitude of the system.

The invention provides a UWB / MEMS IMU positioning method for improving XGBoost and GM in a non-line-of-sight environment. The UWB / MEMS IMU positioning method comprises the following steps: 1, generating derivative features by using UWB original data; 2, setting a threshold value, if the NLOS is judged to be NLOS, giving a label '1', and if the NLOS is not judged to be NLOS, giving a label '0'; thirdly, GRA is adopted to calculate the importance of UWB features; fourthly, constructing an optimal classification feature subset through a forward search strategy; 5, performing parameter optimization on the XGBoost by using a PSO-GA algorithm; sixthly, the NLOS is corrected through GM; and 7, carrying out tight combination on the corrected UWB data and the MEMS IMU data. According to the method, on the basis of UWB and MEMS IMU data, an XGBoost optimal classification subset is constructed, a PSO-GA algorithm is used for performing parameter optimization on an XGBoost model, NLOS data is corrected through GM, and the corrected UWB data and MEMS IMU data are combined in a tight combination mode, so that high-precision and high-stability indoor positioning of UWB / MEMS IMU is realized.

The application discloses a multi-MEMS IMU chip module and a performance optimization method, performance of each IMU is evaluated in different temperature intervals, and respective weights are calculated in each temperature interval, whether the current IMU is in a stationary state is judged by using acceleration and angular velocity information of the IMU, the current acceleration quality is judged according to the acceleration module length of the stationary state, and the weight proportion is adjusted in real time, in the IMU motion state, high-frequency dynamic information of a gyroscope of the IMU chip and low-frequency steady-state information of an accelerometer are fused by using an extended Kalman filter model, weights of abnormal IMUs are screened out and reduced, and data fusion processing is applied in the final output result. The application has the advantages that the precision problem caused by consistency difference in the manufacturing process of a single IMU is solved, and the overall measurement precision and reliability of the IMU module are improved.

The present application relates to a kind of unmanned ship combination navigation method based on IMU / DVL / magnetometer / LiDAR, for the hardware condition of MEMS IMU, Doppler velocity meter and magnetometer and other sensors.Point cloud distortion correction refers to the point cloud in the same data frame in different coordinate systems is transferred to the coordinate system of the starting point cloud in the frame.MEMS IMU, DVL, magnetometer all have high data update frequency, before point cloud data update, the motion of carrier is predicted and estimated.MEMS IMU outputs angular velocity and acceleration in carrier coordinate system, using the above information to update state;Magnetometer outputs magnetic field intensity in b system, DVL outputs velocity in the coordinate system of DVL, using the above information to update measurement, deduce simplified 9-dimensional state Kalman filter, estimate the motion of carrier, and then effectively correct point cloud distortion.Realize the point cloud in the same data frame is transferred to the coordinate system of the starting point cloud in the frame, complete the goal of point cloud distortion correction, improve the precision of LiDAR navigation.

RTK-GPS (realtime kinematic GPS) is one of the most precise positioning technology, with which users can obtain cm-level accuracy of the position in realtime by processing carrier-phase measurements of GPS signals. RTK-GPS has been already utilized for many applications like surveying, construction, agriculture and transportation. Recently, some researchers have tried to apply RTK-GPS to precise navigation of a mobile vehicle. On the condition under the clear sky, RTK-GPS provides fair performance even in the mobile environment. The performances includes availability of solutions, accuracy, TTF (time to fix), and reliability of integer ambiguity resolutions. However, in urban area like in a downtown street, RTK-GPS shows poor performance. One reason of the performance degradation on such condition is cycle slips. Cycle slips are defined as the discontinuities in the measured carrier-phase caused by loss-of-lock of signal tracking in GPS receivers. A sudden jump of the carrier-phase measurement forces the integer ambiguity discontinuity. The Main sources of the cycle slips are obstructions of GPS signals due to buildings, trees or bridges. So the cycle slips much more occur in mobile environment than in static conditions. In the most of the RTK-GPS receivers, once a cycle slip detected, the estimated integer ambiguity is reset to the initial state. This reinitialization process often takes a few minutes, so the precise solution is not provided in the period. With frequent cycle slips, the availability of the RTK-GPS solutions becomes very low. Additionally, it is not easy to recognize a small cycle slip in the mobile environment. The miss-detection of the cycle slip much degrades the RTK-GPS accuracy. Reliable cycle slip detection is more important on this situation. One of the major technical issues for the mobile environment RTK-GPS is to overcome the cycle slips.For mobile vehicle navigation with GPS, a low-cost MEMS (micro-electro-mechanical systems) IMU (inertial measurement unit) is sometimes integrated to improve the performance. In such GPS / IMU integrated system, the IMU is used to obtain user positions and velocities during the GPS signal outages. For mobile environment RTK-GPS, this IMU integration is also effective to improve the navigation conditions. In this study, we employed tightly coupled GPS / IMU integration in order to detect and repair the cycle slips for RTK-GPS. As for the tightly coupled GPS / IMU integration, the user position, velocity and attitude are estimated by EKF (extended Kalman Filter) by using both of GPS and IMU measurements. The sensor biases of the gyros and accelerometers of the IMU are also incorporated in the filter, because sensor biases of a MEMS IMU are relatively large and time-varied. In addition to these unknown states, the double-differenced integer ambiguities are estimated in the case of RTK-GPS. The estimated float ambiguities by EKF are resolved into integer value by a usual integer ambiguity resolution way for RTK-GPS.In this study, in the measurement update of EKF, pre-fit residual of the double-differenced carrier-phase measurement, that is the difference between the predicted and observed state, is compared to the pre-defined threshold for the cycle slip detection. The measurement with larger residual than the threshold is marked as a candidate of the phase discontinuity by a cycle slip. After all the checks, the algorithm enters to the cycle slip repair process. The mount of the each cycle slip has usually the integer value of cycle. So the cycle slip amount as the unknown parameters, the integer vector search process runs to obtain the best estimated integer vector to minimize the least square residuals according to the covariance matrix of the predicted receiver position. To reduce computational cost to solve this ILS (integer least square) problem, the well known efficient strategy LAMBDA is employed to search proper integer vector in this study. Once best solution of the integer vector obtained, it is validated by simple ratio-test with the best and the second best solutions of ILS. If this discrimination test is passed, the states of the carrier phase integer ambiguities are updated by adding the integer vector to repair the cycle slips. If the test failed, the integer ambiguities are reinitialized and restart estimation process from guess estimation values. This proposed cycle slip detection and repair process can be applied to usual RTK-GPS algorithm without the IMU. However, it is hard to provide good performance without an IMU because of the low accuracy of the predicted user position.We made some field tests to demonstrate and evaluate proposed GPS / IMU integration scheme to detect and repair cycle slip for mobile environment RTK-GPS, NovAtel OEM-V dual-frequency GPS receivers were used for both of the mobile receiver (rover) and the reference station. The rover GPS antenna is mounted to the rooftop of a car. Analog Devices AD16354 6-DOF MEMS IMU device are also fixed on the car body. The rover receiver and IMU were connected to a laptop PC to record raw measurement data. At first we drove the car around the area with clear sky and few obstructions to acquire the raw measurement data of GPS and IMU. We processed the raw measurements of only GPS in the simulated RTK-GPS mode of RTKLIB. RTKLIB is a GPS post processing analysis software developed by the authors but has no function of the IMU data handling originally. Under such good condition, few cycle slips occurred and the RTK-GPS solutions showed the smoothed ground track. Next, we added the artificial integer cycle jumps to the raw carrier phase measurement of GPS to simulate the cycle slips. In this case, the availability of precise solution much decreased and some miss-detections of the cycle slips caused the sudden jumps of the car track. Furthermore, we implemented the proposed tightly coupled GPS / IMU integration and cycle slip detection and repair functions described above. The cycle slip detection and repair function with the IMU could fairly detect and repair the simulated cycle slips and made the ground track of the solutions smooth. The evaluation results are also demonstrated in more realistic situations like at the downtown streets with many cycle slips.

The application relates to the field of MEMS inertial technology, and provides an array MEMS IMU data compression real-time storage method based on low storage and calculation amount. Compared with a single MEMS IMU, the array MEMS IMU needs to collect more data, and a master control chip needs to not only perform combined navigation processing on a large amount of original data, but also needs to store and compress historical data, which can be used to accurately judge the motion state of a carrier and perform fault diagnosis. For the array MEMS IMU with a single-chip microcomputer as the master control chip, the single-chip microcomputer needs to occupy more storage space and calculation amount to process the data of the array MEMS IMU, further compresses the combined navigation calculation resource, and restricts the development of the low-cost array MEMS IMU. Therefore, the application studies the array MEMS IMU data compression real-time storage method based on low storage and calculation amount. By applying the method, the use of the storage amount of the single-chip microcomputer is greatly reduced, and the single-chip microcomputer resource is saved.

The invention discloses a dynamic heart rate variability detection system and method based on an MEMS IMU pectoral girdle. The system comprises a flexible pectoral girdle used for being worn on the chest of a user; the inertial measurement module is used for collecting a micro-vibration angular rate GCG signal and a real-time stride frequency of a user; the control and transmission module is used for executing a detection algorithm and wirelessly transmitting data; the power supply module is used for providing stable power; wherein the inertial measurement module, the control and transmission module and the power supply module are all integrated in the flexible chest strap. The method is high in detection accuracy and strong in cross-state adaptability.

The application provides a mine disaster rescue robot navigation system and method based on a MEMS IMU, belongs to the field of robot navigation and control, and comprises a sensing unit, a data processing unit and a fault-tolerant execution circuit, the sensing unit comprises a synchronous MEMS IMU and an auxiliary sensor, the data processing unit comprises an online calibration compensation module and a fusion filtering module, the online calibration compensation module calibrates the MEMS IMU by using auxiliary sensor data and compensates the zero offset of the MEMS IMU, the fusion filtering module is used for correcting errors of navigation information of the MEMS IMU when the auxiliary sensor data is reliable, and the MEMS IMU continues to provide navigation information when the auxiliary sensor data is unreliable, and the fault-tolerant execution circuit comprises at least three redundant channels and a voter. In the extreme case that all external auxiliary sensors fail, the MEMS IMU can also work independently for a long time.