Abstract:In this paper, an integrated MEMS gyroscope array method composed of two levels of optimal filtering was designed to improve the accuracy of gyroscopes. In the firstlevel filtering, several identical gyroscopes were combined through Kalman filtering into a single effective device, whose performance could surpass that of any individual sensor. The key of the performance improving lies in the optimal estimation of the random noise sources such as rate random walk and angular random walk for compensating the measurement values. Especially, the cross correlation between the noises from different gyroscopes of the same type was used to establish the system noise covariance matrix and the measurement noise covariance matrix for Kalman filtering to improve the performance further. Secondly, an integrated Kalman filter with six states was designed to further improve the accuracy with the aid of external sensors such as magnetometers and accelerometers in attitude determination. Experiments showed that three gyroscopes with a bias drift of 35 degree per hour could be combined into a virtual gyroscope with a drift of 1.07 degree per hour through the first-level filter, and the bias drift was reduced to 0.53 degree per hour after the second-level filtering. It proved that the proposed integrated MEMS gyroscope array is capable of improving the accuracy of the MEMS gyroscopes, which provides the possibility of using these low cost MEMS sensors in high-accuracy application areas.

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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.

Abstract:In this paper, a novel approach for processing the outputs signal of the microelectromechanical systems (MEMS) gyroscopes was presented to reduce the bias drift and noise. The principle for the noise reduction was presented, and an optimal Kalman filter (KF) was designed by a steady-state filter gain obtained from the analysis of KF observability. In particular, the true angular rate signal was directly modeled to obtain an optimal estimate and make a self-compensation for the gyroscope without needing other sensor's information, whether in static or dynamic condition. A linear fit equation that describes the relationship between the KF bandwidth and modeling parameter of true angular rate was derived from the analysis of KF frequency response. The test results indicated that the MEMS gyroscope having an ARW noise of 4.87°/h 0.5 and a bias instability of 44.41°/h were reduced to 0.4°/h 0.5 and 4.13°/h by the KF under a given bandwidth (10 Hz), respectively. The 1σ estimated error was reduced from 1.9°/s to 0.14°/s and 1.7°/s to 0.5°/s in the constant rate test and swing rate test, respectively. It also showed that the filtered angular rate signal could well reflect the dynamic characteristic of the input rate signal in dynamic conditions. The presented algorithm is proved to be effective at improving the measurement precision of the MEMS gyroscope. OPEN ACCESSMicromachines 2015, 6 267

Abstract:In this paper, the dynamic performance of a Kalman filter (KF) was analyzed, which is used to combine multiple measurements of a gyroscopes array to reduce the noise and improve the accuracy of the individual sensors. A principle for accuracy improvement by the KF was briefly presented to obtain an optimal estimate of input rate signal. In particular, the influences of some crucial factors on the KF dynamic performance were analyzed by simulations such as the factors input signal frequency, signal sampling, and KF filtering rate. Finally, a system that was comprised of a six-gyroscope array was designed and implemented to test the dynamic performance. Experimental results indicated that the 1σ error for the combined rate signal was reduced to about 0.2°/s in the constant rate test, which was a reduction by a factor of more than eight compared to the single gyroscope. The 1σ error was also reduced from 1.6°/s to 0.48°/s in the swing test. It showed that the estimated angular rate signal could well reflect the dynamic characteristic of the input signal in dynamic conditions.

As considerable progress has been made in wireless sensor networks (WSNs), we can expect that sensor nodes will be applied in industrial applications. Most available techniques for WSNs can be transplanted to industrial wireless sensor networks (IWSNs). However, there are new requirements of quality of service (QoS), that is, real-time routing, energy efficiency, and transmission reliability, which are three main performance indices of routing design for IWSNs. As one-hop neighborhood information is often inadequate to data routing in IWSNs, it is difficult to use the conventional routing methods. In the paper, we propose the routing strategy by taking the real-time routing performance, transmission reliability, and energy efficiency (TREE, triple R and double E) into considerations. For that, each sensor node should improve the capability of search range in the phase of data route discovery. Because of the increase of available information in the enlarged search range, sensor node can select more suitable relay node per hop. The real-time data routes with lower energy cost and better transmission reliability will be used in our proposed routing guideline. By comparing with other routing methods through extensive experimental results, our distributed routing proposal can guarantee the diversified QoS requirements in industrial applications. Copyright Different from the common methods used in the industrial data communication such as Fieldbus and Industrial Ethernet, there is a pressing need to reconsider the requirements of quality of service (QoS) in industrial wireless sensor networks (IWSNs), that is, energy awareness [6, 7], real-time data transmission [8,9], and reliable data transmission [10,11], because the actuators interpret and execute the correct control instructions from the central controller. The real-time performance for such networks means that the accumulated time delay of monitoring data from the source node to the sink should be less than the required time bound. For instance, in the oil leak monitoring system, the ineffective monitoring data will cause a great loss to oil transport. From another perspective, wireless sensor nodes are usually powered with limited energy resources, and it is difficult to recharge or replace their batteries. Therefore, the energy efficiency is another important issue needed to be further studied, and the communication loads among sensor nodes should be evenly distributed in order to maintain link connectivity and prolong network lifetimes in IWSNs.Generally speaking, the guarantee of QoS to IWSNs should consider multiple factors, for example, electromagnetic interferences, accidental death of sensor node, dynamic network topology, low processing capability, and small memory capacity [12]. Several works have studied these issues from the perspective of different network layers. Herein, routing protocols regulate and specify how the routers communicate with each other, and they also instruct that how the information can be disseminated to enable sensor nodes to sel...

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.