Search Results (51)

This paper presents a heading alignment procedure for drone navigation employing a single hover GNSS antenna combined with low-grade MEMs-IMU sensors. The design was motivated by the need for a drone-mounted differential interferometric SAR (DinSAR) application. Still, the methodology proposed here applies to any Unmanned Aerial Vehicle (UAV) application that requires high-precision navigation data for short-flight missions utilizing cost-effective MEMs sensors. The method proposed here involves a Bayesian parameter estimation based on a simultaneous cumulative Mahalanobis metric applied to the innovation process of Kalman-like filters, which are identical except for the initial heading guess. The procedure is then generalized to multidimensional parameters, thus called parametric alignment, referring to the fact that the strategy applies to alignment problems regarding some parameters, such as the heading initial value. The motivation for the multidimensional extension in the scenario is also presented. The method is highly applicable for cases where gyro-compassing is not available. It employs the most straightforward optimization techniques that can be implemented using a real-time parallelism scheme. Experimental results obtained from a real UAV mission demonstrate that the proposed method can provide initial heading alignment when the heading is not directly observable during takeoff, while numerical simulations are used to illustrate the extension to the multidimensional case. Full article

A MEMS gyroscope is a critical sensor in attitude control platforms and inertial navigation systems, which has the advantages of small size, light weight, low energy consumption, high reliability and strong anti-interference capability. This paper presents the design, simulation and fabrication of a Y-axis gyroscope with out-of-plane detection developed using CMOS-MEMS technology. The structural dimensions of the gyroscope were optimized through a multi-objective genetic algorithm, and modal, harmonic response and range simulation analyses were carried out to verify the reasonableness of the design. The chip measured 1.2 mm × 1.3 mm. The simulation results indicate that the driving and detecting frequencies of the gyroscope were 9215.5 Hz and 9243.5 Hz, respectively; the Q-factors were 83,790 and 46,085; the mechanical sensitivity was 4.87 × 10⁻¹¹ m/°/s; and the operational range was ±600°/s. Chip testing shows that the static capacitance was consistent with the preset value. The error between the measured frequency characteristics and the simulation results was 1.9%. This design establishes a foundation for the integration of the gyroscope’s structure and circuitry. Full article

Without a GNSS (global navigation satellite system) signal, the integrated navigation system in vehicles with a fixed trajectory (e.g., railcars) is limited to the use of micro-electromechanical system-inertial navigation system (MEMS-INS) and odometer (ODO). Due to the significant measurement error of the MEMS inertial device and the inability of ODO to output attitude, the positioning error is generally large. To address this problem, this paper presents a new integrated navigation algorithm based on a dynamically constrained Kalman model. By analyzing the dynamics of a railcar, several new observations have been investigated, including errors of up and lateral velocity, centripetal acceleration, centripetal D-value (difference value), and an up-gyro bias. The state transition matrix and observation matrix for the error state model are represented. To improve navigation accuracy, virtual noise technology is applied to correct errors of up and lateral velocity. The vehicle-running experiment conducted within 240 s demonstrates that the positioning error rate of the dead-reckoning method based on MEMS-INS is 83.5%, whereas the proposed method exhibits a rate of 4.9%. Therefore, the accuracy of positioning can be significantly enhanced. Full article

Autonomous Underwater Vehicles (AUVs) are widely used for hydrological monitoring, underwater exploration, and geological surveys. However, AUVs face limitations in underwater navigation due to the high costs associated with Strapdown Inertial Navigation System (SINS) and Doppler Velocity Log (DVL), hindering the development of low-cost vehicles. Micro Electro Mechanical System Inertial Measurement Units (MEMS IMUs) are widely used in industry due to their low cost and can output acceleration and angular velocity, making them suitable as an Attitude Heading Reference System (AHRS) for low-cost vehicles. However, poorly calibrated MEMS IMUs provide an inaccurate angular velocity, leading to rapid drift in orientation. In underwater environments where AUVs cannot use GPS for position correction, this drift can have severe consequences. To address this issue, this paper proposes Underwater Gyros Denoising Net (UGDN), a method based on dilated convolutions and LSTM that learns and extracts the spatiotemporal features of IMU sequences to dynamically compensate for the gyroscope’s angular velocity measurements, reducing attitude and heading errors. In the experimental section of this paper, we deployed this method on a dataset collected from field trials and achieved significant results. The experimental results show that the accuracy of MEMS IMU data denoised by UGDN approaches that of fiber-optic SINS, and when integrated with DVL, it can serve as a low-cost underwater navigation solution. Full article

Because the conventional Temperature Drift Error (TDE) estimation model for Capacitive MEMS Gyros (CMGs) has inadequate Temperature Correlated Quantities (TCQs) and inaccurate parameter identification to improve their bias stability, its novel model based on thermal stress deformation analysis is presented. Firstly, the TDE of the CMG is traced precisely by analyzing its structural deformation under thermal stress, and more key decisive TCQs are explored, including ambient temperature variation ∆T and its square ∆T², as well its square root ∆T^1/2; then, a novel TDE estimation model is established. Secondly, a Radial Basis Function Neural Network (RBFNN) is applied to identify its parameter accurately, which eliminates local optimums of the conventional model based on a Back-Propagation Neural Network (BPNN) to improve bias stability. By analyzing heat conduction between CMGs and the thermal chamber with heat flux analysis, proper temperature control intervals and reasonable temperature control periods are obtained to form a TDE precise test method to avoid time-consuming and expensive experiments. The novel model is implemented with an adequate TCQ and RBFNN, and the Mean Square Deviation (MSD) is introduced to evaluate its performance. Finally, the conventional model and novel model are compared with bias stability. Compared with the conventional model, the novel one improves CMG’s bias stability by 15% evenly. It estimates TDE more precisely to decouple Si-based materials’ temperature dependence effectively, and CMG’s environmental adaptability is enhanced to widen its application under complex conditions. Full article

In this paper, a neural fractional order prescribed performance control is proposed for micro-electromechanical system (MEMS) gyros. Gyros tend to become smaller in size and more precise in structure with the development of micro-manufacturing technology. The operational security for MEMS gyros in cases of disturbances and parameter uncertainties becomes a challenging problem that has attracted much attention. The proposed method incorporates a prescribed performance technique to accomplish a bounded (within 10% of the vibration amplitude) gyro trajectory tracking error dynamic to secure the gyro’s operation. Meanwhile, fractional calculus is integrated into the controller’s design to provide precise adjustments to the gyro’s motion and thus further improve gyro control performance (shortening error convergence time by 20%). Furthermore, to enlarge the application scope and to improve gyro system robustness, a modified neural network estimator with a constrained input mapping mechanism is proposed to approximate unknown time-varying angular-velocity-related gyro dynamics. Notably, the constrained input mapping mechanism can help guide neural parameter initialization to avoid a time-consuming parameter adjustment process. The stability of the closed-loop gyro control system is proved in the framework of Lyapunov stability theory, and comparisons of simulation results are presented to demonstrate the effectiveness of the proposed method. Full article

Long-term corrosion, construction irregularities, road pressure and other reasons lead to various defects in urban sewer pipelines. Closed-circuit television (CCTV) and quick view (QV) are currently the most commonly used techniques to detect the internal state of the pipeline, but CCTV requires a large amount of capital investment and manpower costs, while QV is faced with the use of limitations and inaccurate positioning. The inspection of urban sewerage networks has long been a challenge for the relevant management authorities to overcome. To this end, in this study, an device was assembled using a six-axis MEMS gyroscope sensor as the core component to inspect and locate the breakage point of the pipe. Specifically, a six-axis MEMS gyroscope sensor is used as the core component along with a small lithium battery and a remote control switch assembled in a highly waterproof round box, and dropped into a laboratory to simulate a sewage pipe that has external water infiltration. Then the device is recovered and the SD card on which the data is stored is removed, the data is loaded to perform the coordinate conversion process and restore the trajectory and attitude of the device along its travel. The three axis axial acceleration of the device before and after passing through the infiltration point is analyzed for anomalies, as well as changes in the roll and pitch angle fluctuations of the device. Multiple experiments demonstrated that the six-axis MEMS gyro sensor response is very sensitive, generating data and storing it through the DATALOG module. With the reading and analysis of the data, when the pipeline is broken by external water intrusion, the axial acceleration value, pitch angle and roll angle of the device will change abruptly after flowing through the infiltration point, based on the analysis of these indicators the preliminary judgment of the extent of external water infiltration and locate the location of the infiltration point, potential applications of MEMS gyroscopic sensors in the field of sewerage are believed to be vast. Full article

As an effective capacitance signal produced by a micro-hemisphere gyro is usually below the pF level, and the capacitance reading process is susceptible to parasitic capacitance and environmental noise, it is highly difficult to acquire an effective capacitance signal. Reducing and suppressing noise in the gyro capacitance detection circuit is a key means to improve the performance of detecting the weak capacitance generated by MEMS gyros. In this paper, we propose a novel capacitance detection circuit, where three different means are utilized to achieve noise reduction. Firstly, the input common-mode feedback is applied to the circuit to solve the input common-mode voltage drift caused by both parasitic capacitance and gain capacitance. Secondly, a low-noise, high-gain amplifier is used to reduce the equivalent input noise. Thirdly, the modulator–demodulator and filter are introduced to the proposed circuit to effectively mitigate the side effects of noise; thus, the accuracy of capacitance detection can be further improved. The experimental results show that with the input voltage of 6 V, the newly designed circuit produces an output dynamic range of 102 dB and the output voltage noise of 5.69 nV/√Hz, achieving a sensitivity of 12.53 V/pF. Full article

High-sensitivity uniaxial opto-mechanical accelerometers provide very accurate linear acceleration measurements. In addition, an array of at least six accelerometers allows the estimation of linear and angular accelerations and becomes a gyro-free inertial navigation system. In this paper, we analyze the performance of such systems considering opto-mechanical accelerometers with different sensitivities and bandwidths. In the six-accelerometer configuration adopted here, the angular acceleration is estimated using a linear combination of accelerometers’ read-outs. The linear acceleration is estimated similarly but requires a correcting term that includes angular velocities. Accelerometers’ colored noise from experimental data is used to derive, analytically and through simulations, the performance of the inertial sensor. Results for six accelerometers, separated by 0.5 m in a cube configuration show noise levels of 10${}^{-7}$ m s${}^{-2}$ and 10${}^{-5}$ m s${}^{-2}$ (in Allan deviation) for time scales of one second for the low-frequency (Hz) and high-frequency (kHz) opto-mechanical accelerometers, respectively. The Allan deviation for the angular velocity at one second is ${10}^{-5}$ rad s${}^{-1}$ and $5\times {10}^{-4}$ rad s${}^{-1}$. Compared to other technologies such as MEMS-based inertial sensors and optical gyroscopes, the high-frequency opto-mechanical accelerometer exhibits better performance than tactical-grade MEMS for time scales shorter than 10 s. For angular velocity, it is only superior for time scales less than a few seconds. The linear acceleration of the low-frequency accelerometer outperforms the MEMS for time scales up to 300 s and for angular velocity only for a few seconds. Fiber optical gyroscopes are orders of magnitude better than the high- and low-frequency accelerometers in gyro-free configurations. However, when considering the theoretical thermal noise limit of the low-frequency opto-mechanical accelerometer, $5\times {10}^{-11}$ m s${}^{-2}$, linear acceleration noise is orders of magnitude lower than MEMS navigation systems. Angular velocity precision is around ${10}^{-10}$ rad s${}^{-1}$ at one second and $5\times {10}^{-7}$ rad s${}^{-1}$ at one hour, which is comparable to fiber optical gyroscopes. While experimental validation is yet not available, the results shown here indicate the potential of opto-mechanical accelerometers as gyro-free inertial navigation sensors, provided the fundamental noise limit of the accelerometer is reached, and technical limitations such as misalignments and initial conditions errors are well controlled. Full article

Automatic dependent surveillance-broadcast (ADS-B) is a very important communication and surveillance technology in air traffic control (ATC). In the future, more and more satellites will carry out ADS-B technology to perform a global coverage. In order to make full use of the resources in the satellite, this paper proposes a solution for satellite three-axis attitude determination using the ADS-B receiver. The principle of ADS-B-based attitude determination is presented first. On this basis, ADS-B-based methods are employed to solve the problem. To achieve a higher attitude determination precision, gyro is combined with the ADS-B receiver using a multiplicative extended Kalman filter (MEKF). Finally, a simulation is carried out and the result is presented. Full article

A micro-inertial measurement unit (MIMU) is usually used to sense the angular rate and acceleration of the flight carrier. In this study, multiple MEMS gyroscopes were used to form a spatial non-orthogonal array to construct a redundant MIMU system, and an optimal Kalman filter (KF) algorithm was established by a steady-state KF gain to combine array signals to improve the MIMU’s accuracy. The noise correlation was used to optimize the geometric layout of the non-orthogonal array and reveal the mechanisms of influence of correlation and geometric layout on MIMU’s performance improvement. Additionally, two different conical configuration structures of a non-orthogonal array for 4,5,6,8-gyro were designed and analyzed. Finally, a redundant 4-MIMU system was designed to verify the proposed structure and KF algorithm. The results demonstrate that the input signal rate can be accurately estimated and that the gyro’s error can also be effectively reduced through fusion of non-orthogonal array. The results for the 4-MIMU system illustrate that the gyro’s ARW and RRW noise can be decreased by factors of about 3.5 and 2.5, respectively. In particular, the estimated errors (1σ) on the axes of X_b, Y_b and Z_b were 4.9, 4.6 and 2.9 times lower than that of the single gyroscope. Full article

This paper introduces a digital interface application-specific integrated circuit (ASIC) for a micro-electromechanical systems (MEMS) vibratory gyroscope. The driving circuit of the interface ASIC uses an automatic gain circuit (AGC) module instead of a phase-locked loop to realize a self-excited vibration, which gives the gyroscope system good robustness. In order to realize the co-simulation of the mechanically sensitive structure and interface circuit of the gyroscope, the equivalent electrical model analysis and modeling of the mechanically sensitive structure of the gyro are carried out by Verilog-A. According to the design scheme of the MEMS gyroscope interface circuit, a system-level simulation model including mechanically sensitive structure and measurement and control circuit is established by SIMULINK. A digital-to-analog converter (ADC) is designed for the digital processing and temperature compensation of the angular velocity in the MEMS gyroscope digital circuit system. Using the positive and negative diode temperature characteristics, the function of the on-chip temperature sensor is realized, and the temperature compensation and zero bias correction are carried out simultaneously. The MEMS interface ASIC is designed using a standard 0.18 μM CMOS BCD process. The experimental results show that the signal-to-noise ratio (SNR) of sigma-delta (ΣΔ) ADC is 111.56 dB. The nonlinearity of the MEMS gyroscope system is 0.03% over the full-scale range. Full article

This paper examines how tactical-grade Inertial Navigation Systems (INS), aided by Global Navigation Satellite System (GNSS) modules, vary from a survey-grade system in the bathymetric mapping in depths less than 20 m. The motivation stems from the advancements in sensor developments, measurement processing algorithms, and the proliferation of autonomous and uncrewed surface vehicles often seeking to use tactical-grade systems for high-quality bathymetric products. While the performance of survey-grade GNSS + INS is well-known to the hydrographic and marine science community, the performance and limitations of the tactical-grade micro-electro-mechanical system (MEMS) and tactical-grade fiber-optic-gyro (FOG) INS aided with GNSS require some study to answer the following questions: (1) How close or far is the tactical-grade GNSS + INS performance from the survey-grade systems? (2) For what survey order (IHO S-44 6th ed.) can a user deploy them? (3) Can we use them for navigation chart production? We attempt to answer these questions by deploying two tactical-grade GNSS + INS units (MEMS and FOG) and a survey-grade GNSS + INS on a survey boat. All systems collected data while operating a multibeam system with the lever-arm offsets accurately determined using a total station. The tactical-grade GNSS + INSs shared one pair of antennas for heading, while the survey-grade system used an independent antenna pair. We analyze the GNSS + INS results in sequence, examine the patch-test results, and the sensor-specific SBET-integrated bathymetric surfaces as metrics for determining the tactical-grade GNSS + INSs’ reliability. In addition, we evaluate the multibeam’s sounding uncertainties at different beam angles. The bathymetric surfaces using the tactical-grade navigation solutions are within 15 cm of the surface generated with the survey-grade solutions. Full article

The applications of Micro-Electro-Mechanical-System (MEMS) gyros in inertial navigation system is gradually increasing. However, the random drift of gyro deteriorates the system performance which restricting the applications of high precision. We propose a bias drift compensation model based on two-fold Interpolated Complementary Ensemble Local Mean Decomposition (ICELMD) and autoregressive moving average-Kalman filtering (ARMA-KF). We modify CELMD into ICELMD, which is less complicated and overcomes the endpoint effect. Further, the ICELMD is combined with ARMA-KF to separate and simplify the preprocessed signal, resulting improved denoising performance. In the model, the abnormal noise is removed in preprocess by 2$\sigma$ criterion with ICELMD. Then, continuous mean square error (CMSE) and Permutation Entropy (PE) are both applied to categorize the preprocessed signal into noise, mixed and useful components. After abandon the noise components and denoise the mixed components by ARMA-KF, we rebuild the noise suppression signal of MEMS gyro. Experiments are carried out to validate the proposed algorithm. The angle random walk of gyro decreases from 2.4156${}^{\circ }$/$\sqrt{h}$ to 0.0487${}^{\circ }$/$\sqrt{h}$, the zero bias instability lowered from 0.3753${}^{\circ }$/h to 0.0509${}^{\circ }$/h. Further, the standard deviation and the variance are greatly reduced, indicating that the proposed method has better suppression effect, stability and adaptability. Full article

The micro-electro-mechanical system (MEMS) gyroscope is a micro-mechanical gyroscope with low cost, small volume, and good reliability. The working principle of the MEMS gyroscope, which is achieved through Coriolis, is different from traditional gyroscopes. The MEMS gyroscope has been widely used in the fields of micro-inertia navigation systems, military, automotive, consumer electronics, mobile applications, robots, industrial, medical, and other fields in micro-inertia navigation systems because of its advantages of small volume, good performance, and low price. The material characteristics of the MEMS gyroscope is very significant for its data output, and the temperature determines its accuracy and limits its further application. In order to eliminate the effect of temperature, the MEMS gyroscope needs to be compensated to improve its accuracy. This study proposed an improved variational modal decomposition—extreme learning machine (VMD-ELM) algorithm based on convolutional neural networks—long short-term memory (CNN-LSTM) and particle swarm optimization—support vector machines (PSO-SVM). By establishing a temperature compensation model, the gyro temperature output signal is optimized and reconstructed, and the gyro output signal with better accuracy is obtained. The VMD algorithm separates the gyro output signal and divides the gyro output signal into low-frequency signals, mid-frequency signals, and high-frequency signals according to the different signal frequencies. Once again, the PSO-SVM model is constructed by the mid-frequency temperature signal to find the temperature error. Finally, the signal is reconstructed through the ELM neural network algorithm, and then, the gyro output signal after noise is obtained. Experimental results show that, by using the improved method, the output of the MEMS gyroscope ranging from −40 to 60 °C reduced, and the temperature drift dramatically declined. For example, the factor of quantization noise (Q) reduced from 1.2419 × 10⁻⁴ to 1.0533 × 10⁻⁶, the factor of bias instability (B) reduced from 0.0087 to 1.8772 × 10⁻⁴, and the factor of random walk of angular velocity (N) reduced from 2.0978 × 10⁻⁵ to 1.4985 × 10⁻⁶. Furthermore, the output of the MEMS gyroscope ranging from 60 to −40 °C reduced. The factor of Q reduced from 2.9808 × 10⁻⁴ to 2.4430 × 10⁻⁶, the factor of B reduced from 0.0145 to 7.2426 × 10⁻⁴, and the factor of N reduced from 4.5072 × 10⁻⁵ to 1.0523 × 10⁻⁵. The improved algorithm can be adopted to denoise the output signal of the MEMS gyroscope to improve its accuracy. Full article