26 results about "Mems imu" patented technology

The invention discloses a wind power generation tower pose monitoring method based on a GNSS / MEMS IMU / magnetometer combination, and the method comprises the steps: carrying out the estimation of a state vector containing the position, speed, attitude error, sensor zero offset and scale factor error through error state Kalman filtering based on the GNSS / MEMS IMU combination, and carrying out the correction and fusion of GNSS and IMU data through a lever arm effect; a complementary filtering algorithm is adopted, an error vector is constructed by combining measured values of an accelerometer and a magnetometer, a gyro angular velocity is corrected through a PI controller, an attitude quaternion is recurred, a course angle is calculated, and the course angle is used as an auxiliary observed quantity to update filtering. According to the invention, by adding course information provided by the MEMS IMU and the magnetometer, the position and the attitude are comprehensively estimated, and high-precision six-degree-of-freedom pose monitoring is realized.

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 invention relates to an attitude resolving method in a high dynamic environment based on a different-plane MEMS IMU array, belongs to the technical field of inertial navigation, and particularly relates to an IMU array data fusion method. According to the invention, the plurality of MEMS IMUs are arranged in an array according to a specific different-plane structure, the geometric characteristics of the sensor layout are ingeniously utilized to offset the influence of zero offset and environmental factors, and the measurement precision is improved. The characteristics of the accelerometer array and the gyroscope array are respectively utilized to fuse the angular velocity for the collected inertial data, so that the system can accurately obtain the angular velocity of the system in a high-dynamic environment and even an environment with a super gyroscope range, thereby effectively improving the attitude calculation precision in the high-dynamic environment. According to the method provided by the invention, the attitude calculation precision in the high-dynamic environment is improved, and a new thought is provided for solving high-precision attitude calculation in the high-dynamic environment.

The application discloses a kind of MEMS device architecture, including cap layer, device layer and substrate layer, cap layer and device layer are bonded, the whole after bonding is bonded with substrate layer, cap layer is manufactured based on first silicon substrate, cavity structure is provided at the bottom of cap layer, high-depth groove structure is provided in cap layer, groove wall of high-depth groove structure is provided with silicon dioxide film, high-depth groove structure is filled with polycrystalline silicon and deposited, the upper surface of first silicon substrate is provided with silicon dioxide layer;Device layer is made based on SOI wafer, including the first silicon structure layer consisting of top layer silicon, buried oxide layer and the second silicon structure layer consisting of back substrate silicon;Substrate layer includes second silicon substrate, bottom cavity is provided on the top surface of second silicon substrate, exhaust hole is opened on the bottom surface of second silicon substrate.The application combines deep groove etching, polycrystalline silicon slot filling and bonding technology, utilizes advanced materials such as SOI wafer and polyimide layer, and realizes excellent signal isolation and flexibility of various MEMS IMU applications.

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.

The utility model provides an automatic north-seeking navigation device. The automatic north-seeking navigation device comprises a north-seeking module which comprises a single-axis fiber-optic gyroscope and two accelerometers and is used for transmitting obtained course angle information to a data processing module; the MEMS inertial navigation unit comprises three MEMS gyroscopes and three MEMS angular velocimeters, and the MEMS inertial navigation unit is used for transmitting the obtained attitude information of the automatic north-seeking navigation device to the data processing module; and the data processing module is used for calculating a north direction included angle and an attitude angle of the automatic north-seeking navigation device according to the received course angle information and attitude information. According to the utility model, the north-seeking module with a simple structure is combined with the MEMS IMU, so that the problem that the MEMS IMU cannot realize north seeking and navigation at any time is solved; in addition, the MEMS in the MEMS IMU is corrected through a fiber-optic gyroscope in the north-seeking module, so that the drift of the MEMS IMU is reduced.

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 invention discloses an improved IMU (Inertial Measurement Unit) calibration method based on filtering optimization, which comprises the following steps of: S1, respectively placing MEMS IMUs at three predefined positions, and acquiring acceleration data in a static state at each position; s2, identifying the random error of the MEMS IMU through Allan variance, and performing wavelet threshold filtering and denoising on the output signal of the MEMS IMU at each position by referring to the identification result to obtain a denoised signal; s3, discrete Kalman filtering processing is carried out on a result obtained after wavelet denoising; and S4, based on a Kalman filtering result, representing an output value model of the MEMS IMU accelerometer in a static state as a linear model, and based on three-position method calibration and least square modeling, solving a calibration result. According to the method, the noise characteristics are determined through Allan variance analysis, the original data output by the IMU are filtered by using a wavelet denoising technology and Kalman filtering, and the calibration precision and stability of the IMU in a static environment can be remarkably improved by combining with the three-position method for calibration.

The application discloses a wind power tower pose monitoring method based on GNSS / MEMS IMU / magnetometer combination, comprising the following steps: based on GNSS / MEMS IMU combination, using error state Kalman filtering to estimate a state vector containing position, velocity, attitude error and sensor zero offset, proportional factor error, and fusing GNSS and IMU data through a lever arm effect correction; using a complementary filtering algorithm, combining accelerometer and magnetometer measurement values to construct an error vector, correcting gyro angular velocity through a PI controller and recursively calculating attitude quaternions, calculating a heading angle, and taking the heading angle as auxiliary observation to update filtering. The application realizes high-precision six-degree-of-freedom pose monitoring by increasing heading information provided by MEMS IMU and a magnetometer and comprehensively estimating position and attitude.

Disclosed herein is a process flow for forming a MEMS IMU including an accelerometer and a gyroscope each located in a separate sealed cavity maintained at a different pressure. Formation of the MEMS IMU includes the use of a first vHF release to etch a sacrificial layer underneath a structural layer containing the accelerometer and gyroscope and capping the device under formation to set both cavities at a first pressure. The floor of one of the cavities is formed to including a gas permeable layer. Formation further includes forming a chimney underneath the gas permeable layer and then performing a second vHF release to etch through the gas permeable layer and expose the cavity containing the gas permeable layer so that its pressure may be set to be different than that of the other cavity when the chimney is sealed.

The application provides a kind of underwater vehicle navigation positioning method and system based on MEMS IMU and DVL combination, comprising: taking the mean value of the attitude angle of MEMS IMU as the initial heading attitude information of underwater vehicle;Time update of Kalman filtering is carried out, the position and speed of underwater vehicle are calculated;The speed measured by DVL is multiplied by a proportion factor to eliminate the proportion error;Eliminate the arm error of the speed measured by DVL, further correct the DVL measurement value;The difference between the speed of underwater vehicle and the corrected DVL speed is used as the measurement difference of Kalman filter, the measurement update of Kalman filtering is carried out, and the optimal estimation of underwater vehicle position and speed is obtained;The position of underwater vehicle is used to calculate the attitude angle, and the navigation position, speed and attitude angle are output.The application eliminates the proportion error and arm error of DVL speed respectively, and further improves the navigation positioning accuracy of underwater vehicle.

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.

The invention provides an MEMS IMU gyroscope constant value zero offset estimation method and system, and relates to the technical field of inertial navigation positioning, and the method comprises the following steps: collecting three-axis gyroscope data of an MEMS IMU, and calculating a three-axis gyroscope module value according to the three-axis gyroscope data; the method comprises the following steps: establishing a three-axis gyroscope module value buffer area, calculating a variance value according to data in the three-axis gyroscope module value buffer area, comparing the variance value with a preset variance threshold value, and judging that the MEMS IMU is in a static state when the variance value is smaller than the preset variance threshold value; and calculating the constant zero offset of the three-axis gyroscope according to the data of the three-axis gyroscope in the static state.

The invention provides an RTK + TDCP / INS integrated navigation method for accurately controlling the dynamic displacement of a vehicle in an urban complex environment, which comprises the following steps: acquiring GNSS original observation values including a carrier phase observation value and a pseudo-range observation value, and simultaneously performing MEMS IMU data acquisition to extract INS information; vehicle-mounted dynamic measurement displacement detection: establishing a TDCP observation equation based on a carrier phase observation value, and obtaining a three-dimensional displacement amount between adjacent epochs through least square estimation; dynamically adjusting the noise in the filtering process, namely extracting information for the next step of integrated navigation according to a result obtained by displacement detection and a measurement updating matrix of RTK (Real-Time Kinematic); data fusion and filtering: inputting the position, speed and attitude information of the INS and the GNSS into a robust Kalman filter, when the number of visible satellites is greater than a preset value, isolating abnormal data, otherwise, performing data fusion by adopting an innovation-based adaptive Kalman filtering mode; and outputting the dynamic displacement of the vehicle, including realizing accurate control of the dynamic displacement of the vehicle in the urban complex environment through a data filtering result.

The invention discloses a multi-MEMS IMU chip module and a performance optimization method, and the method comprises the steps: carrying out the performance evaluation of each IMU in different temperature intervals, calculating the weight of each IMU in each temperature interval, judging whether the current IMU is in a static state or not through the acceleration and angular velocity information of the IMU, judging the current acceleration quality according to the acceleration modulus length of the static state, and carrying out the calculation of the current acceleration quality. The weight ratio is adjusted in real time; and in an IMU motion state, fusing high-frequency dynamic information of an IMU chip gyroscope and low-frequency steady-state information of an accelerometer by using an extended Kalman filter model, screening out and reducing the weight of an abnormal IMU, and applying data fusion processing in a final output result. The method 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.

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 present invention discloses a MEMS IMU initial alignment method based on nonlinear optimized magnetic heading, comprising the following steps: S1, determining the true magnetic vector and magnetic declination of the current position through a world geomagnetic model; S2, calculating the initial magnetic heading, pitch angle, and roll angle through the MEMS IMU; S3, constructing a nonlinear optimized magnetic heading model; S4, solving the nonlinear optimized magnetic heading model to obtain an optimal rotation matrix calculated based on an optimal rotation matrix increment, thereby converting the optimal initial alignment heading angle ψ b es t; This initial alignment method solves the problem of insufficient alignment accuracy of the existing MEMS IMU initial alignment method based on magnetometer. It has high reliability, strong versatility, high algorithm operation efficiency, simple operation, high accuracy and good practicality.

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 provides a UWB / MEMS IMU four-wheel robot indoor positioning method based on XGBoost, and the method comprises the following steps: 1, collecting UWB signal features and MEMS IMU data, taking the UWB signal features and MEMS IMU data as training samples, employing XGBoost to carry out the training of UWB and MEMS IMU features, and obtaining a UWB NLOS classification model and a straight movement state classification model; step 2, classifying UWB and MEMS IMU data by using the model obtained in the step 1; step 3, after MEMS IMU data of which the category is straight is obtained, restraining divergence of an MEMS IMU error by adopting an NHC algorithm; and a fourth step, for the UWB data without the NLOS and a result after MEMS IMU constraint, performing tight combination resolving by using improved Kalman filtering to obtain a dynamic positioning result. According to the method, the NLOS data of the UWB is identified and rejected through the XGBoost, the error divergence of the MEMS IMU is inhibited by adding the NHC combined with the motion state, the indoor positioning of the four-wheel robot is realized by adopting the improved UWB / MEMS IMU tight combination algorithm, and the precision and stability of the indoor dynamic positioning of the four-wheel robot are improved.

The invention provides a mine disaster relief robot navigation system and method based on an MEMS IMU, and belongs to the field of robot navigation and control, the mine disaster relief robot navigation system comprises a sensing unit, a data processing unit and a fault-tolerant execution circuit, the sensing unit comprises the MEMS IMU and an auxiliary sensor which are synchronous, and the data processing unit comprises an online calibration compensation module and a fusion filtering module. The on-line calibration compensation module is used for calibrating the MEMS IMU by using the auxiliary sensor data and compensating the zero offset of the MEMS IMU, and the fusion filtering module is used for performing error correction on the navigation information of the MEMS IMU when the auxiliary sensor data is credible, and continuously providing the navigation information by relying on the MEMS IMU when the auxiliary sensor data is not credible. The fault-tolerant execution circuit comprises at least three redundant channels and a voter. According to the invention, under the extreme condition that all external auxiliary sensors fail, the system can work independently for a long time by means of the MEMS IMU.

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.