Honglong Chang

A Handheld Inertial Pedestrian Navigation System With Accurate Step Modes and Device Poses Recognition

In this paper, a handheld inertial pedestrian navigation system (IPNS) based on low-cost microelectromechanical system sensors is presented. Using the machine learning method of support vector machine, a multiple classifier is developed to recognize human step modes and device poses. The accuracy of the selected classifier is >85%. A novel step detection model is created based on the results of the classifier to eliminate the over-counting and under-counting errors. The accuracy of the presented step detector is >98%. Based on the improvements of the step modes recognition and step detection, the IPNS realized precise tracking using the pedestrian dead reckoning algorithm. The largest location error of the IPNS prototype is ~40 m in an urban area with a 2100-m-long distance.

An Acceleration Sensing Method Based on the Mode Localization of Weakly Coupled Resonators

This paper reports an acceleration sensing method based on two weakly coupled resonators (WCRs) using the phenomenon of mode localization. When acceleration acts on the proof masses, differential electrostatic stiffness perturbations will be applied to the WCRs, leading to mode localization, and thus, mode shape changes. Therefore, acceleration can be sensed by measuring the amplitude ratio shift. The proposed mode localization with the differential perturbation method leads to a sensitivity enhancement of a factor of 2 than the common single perturbation method. The theoretical model of the sensitivity, bandwidth, and linearity of the accelerometer is established and verified. The measured relative shift in amplitude ratio (~312162 ppm/g) is 302 times higher than the shift in resonance frequency (~1035 ppm/g) within the measurement range of ±1 g. The measured resolution based on the amplitude ratio is 0.619 mg and the nonlinearity is ~3.5% in the open-loop measurement operation.

Signal Processing of MEMS Gyroscope Arrays to Improve Accuracy Using a 1st Order Markov for Rate Signal Modeling

This paper presents a signal processing technique to improve angular rate accuracy of the gyroscope by combining the outputs of an array of MEMS gyroscope. A mathematical model for the accuracy improvement was described and a Kalman filter (KF) was designed to obtain optimal rate estimates. Especially, the rate signal was modeled by a first-order Markov process instead of a random walk to improve overall performance. The accuracy of the combined rate signal and affecting factors were analyzed using a steady-state covariance. A system comprising a six-gyroscope array was developed to test the presented KF. Experimental tests proved that the presented model was effective at improving the gyroscope accuracy. The experimental results indicated that six identical gyroscopes with an ARW noise of 6.2 °/√h and a bias drift of 54.14 °/h could be combined into a rate signal with an ARW noise of 1.8 °/√h and a bias drift of 16.3 °/h, while the estimated rate signal by the random walk model has an ARW noise of 2.4 °/√h and a bias drift of 20.6 °/h. It revealed that both models could improve the angular rate accuracy and have a similar performance in static condition. In dynamic condition, the test results showed that the first-order Markov process model could reduce the dynamic errors 20% more than the random walk model.

Combining Numerous Uncorrelated MEMS Gyroscopes for Accuracy Improvement Based on an Optimal Kalman Filter

In this paper, an approach to improve the accuracy of microelectromechanical systems (MEMS) gyroscopes by combining numerous uncorrelated gyroscopes is presented. A Kalman filter (KF) is used to fuse the output signals of several uncorrelated sensors. The relationship between the KF bandwidth and the angular rate input is quantitatively analyzed. A linear model is developed to choose suitable system parameters for a dynamic application of the concept. Simulation and experimental tests of a six-gyroscope array proved that the presented approach was effective to improve the MEMS gyroscope accuracy. The experimental results indicate that six identical gyroscopes with a noise density of 0.11°/s/√Hz and a bias instability of 62°/h can be combined to form a virtual gyroscope with a noise density of 0.03°/s/√Hz and a bias instability of 16.8°/h . The accuracy improvement is better than that of a simple averaging process of the individual sensors.

A Three Degree-of-Freedom Weakly Coupled Resonator Sensor With Enhanced Stiffness Sensitivity

This paper reports a three degree-of-freedom (3DoF) microelectromechanical systems (MEMS) resonant sensing device consisting of three weakly coupled resonators with enhanced sensitivity to stiffness change. If one resonator of the system is perturbed by an external stimulus, mode localization occurs, which can be detected by a change of modal amplitude ratio. The perturbation can be, for example, a change in stiffness of one resonator. A detailed theoretical investigation revealed that a mode aliasing effect, along with the thermal noise floor of the sensor and the associated electrical system ultimately limit the dynamic range of the sensor. The nonlinearity of the 3DoF sensor was also analyzed theoretically. The 3DoF resonator device was fabricated using a silicon on insulator process. Measurement results from a prototype device agreed well with the predictions of the analytical model. A significant, namely 49 times, improvement in sensitivity to stiffness change was evident from the fabricated 3DoF resonator sensor compared with the existing state-of-the-art 2DoF resonator sensors, while the typical nonlinearity was smaller than ±2% for a wide span of stiffness change. In addition, measurements indicate that a dynamic range of at least 39.1 dB is achievable, which could be further extended by decreasing the noise of the device and the interface electronics.

Integrated Behavior Simulation and Verification for a MEMS Vibratory Gyroscope Using Parametric Model Order Reduction

In this paper, a parameterized reduced model of a vibratory microelectromechanical systems (MEMS) gyroscope is established using a parametric model order reduction algorithm. In the reduction process, not only the input angular velocity, material density, Youngs modulus, and Rayleigh damping coefficient but also the coefficient of thermal expansion and the change in temperature were all preserved. Based on this model, the integrated behavior simulation of the MEMS gyroscope, including many environmental factors in engineering situations, was performed in an accurate and fast way. Compared with the finite-element method, the relative error of the reduced-order model was less than 4.2%, while the computational efficiency was improved about five times. The cosimulation with a complete interface circuit was successfully performed in a very fast way, which provides a convenient platform for designers to evaluate the performance of sensors. The experimental verification proves that the reduced model can provide a reliable simulation result, although some errors exist.

Theoretical Modeling for a Six-DOF Vortex Inertial Sensor and Experimental Verification

This paper reports on a multi-axis fluidic inertial sensor that can detect three components of angular rate and linear acceleration. The sensor uses a vortex gas flow instead of the traditional linear gas flow as the inertial mass to detect the angular rate and linear acceleration. For this complex multi-axis sensing scheme, the theoretical modeling for the sensitivity and the cross-axis sensitivity of the sensor are discussed in detail. During the verification of the sensors performance, the vortex was created by jetting the air supplied by an external air pump into a detection chamber via two opposing nozzle orifices in opposite directions. A configuration of microfabricated thermistors was constructed to realize multi-axis detection. The measured sensitivities of the gyroscope for the x-axis, y-axis, and z-axis were 0.429, 0.338, and 0.159 mV/°/s, respectively. The measured sensitivities of the accelerometer for the x-axis, y-axis, and z-axis were 0.185, 0.180, and 0.133 V/g, respectively. The results prove that the vortex sensor can effectively detect six-degree-of-freedom spatial motion.

Low noise vacuum MEMS closed-loop accelerometer using sixth-order multi-feedback loops and local resonator sigma delta modulator

This paper reports on the design, implementation of a novel sixth-order sigma-delta modulator (ΣΔM) MEMS closed-loop accelerometer with extended bandwidth in a vacuum environment (~0.5Torr), which can coexist on a single die (or package) with other sensors requiring vacuum packaging. The fully differential accelerometer sensing element with a large proof mass (4×7mm2) was designed and fabricated on a Silicon-on-Insulator (SOI) wafer with 50μm-thick structural layer. Four electronic integrators were cascaded with the sensing element for high-order noise shaping ability. The local feedback paths created a local resonator producing a notch to further suppress the total in-band quantization noise. Measurement results show the overall noise floor achieved was -120dBg/√Hz, which is equivalent to a noise acceleration value of 1.2μg/√Hz in a 500Hz bandwidth; the scale factor was 950mV/g for input accelerations up to ±6g.

A sensor for stiffness change sensing based on three weakly coupled resonators with enhanced sensitivity

This paper reports on a novel MEMS resonant sensing device consisting of three weakly coupled resonators that can achieve an order of magnitude improvement in sensitivity to stiffness change, compared to current state-of-the-art resonator sensors with similar size and resonant frequency. In a 3 degree-of-freedom (DoF) system, if an external stimulus causes change in the spring stiffness of one resonator, mode localization occurs, leading to a drastic change of mode shape, which can be detected by measuring the modal amplitude ratio change. A 49 times improvement in sensitivity compared to a previously reported 2DoF resonator sensor, and 4 orders of magnitude enhancement compared to a 1DoF resonator sensor has been achieved.

On Improving the Performance of a Triaxis Vortex Convective Gyroscope Through Suspended Silicon Thermistors

This paper reports on a triaxis vortex convective gyroscope, in which a suspended arch-shaped silicon thermistor is proposed to improve performance by reducing the thermal-induced stress and heat dissipation to the substrate. The arch structure of the thermistor reduced the thermal-induced stress up to 88.7% compared with the clamped-clamped structure of the thermistor in our previous study. The suspended state reduced the heat dissipation to the substrate up to 32%. The experimental test results indicated that the sensitivities of the sensor for the x-axis, y-axis, and z-axis gyroscope were 0.642, 0.528, and 0.241 mV/°/s, respectively. The sensitivity improvement reached 49.65%, 56.21%, and 51.57% for each axis. The measured nonlinearity for the x-axis, y-axis, and z-axis gyroscope were 2.1%, 3.8%, and 4.5% in the range of ±100 °/s, respectively. An improvement in the linearity of 23.53%, 36.67%, and 6.25% was obtained.