Abstract
The application of the micro-electro-mechanical system inertial measurement unit has become a new research hotspot in the field of inertial navigation. In order to solve the problems of the poor accuracy and stability of micro-electro-mechanical system sensors, redundant design is an effective method under the restriction of current technology. Redundant data processing is the most important part in the micro-electro-mechanical system redundant inertial navigation system, which includes the processing of anomaly data and the fusion estimation of redundant data. To further improve the reliability of the micro-electro-mechanical system redundant inertial measurement unit, an anomaly detection, isolation, and recognition method for data anomalies is proposed. The relationship between the parity space method detection function and the deterioration degree of anomaly data is mathematically deduced. The parity space method detection functions of different anomalies are analyzed, and five indicators are designed to quantitatively analyze the detection function values. According to these indicators, the detection and recognition method are proposed. The new method is tested by a series of simulation experiments.
Highlights
Micro-electro-mechanical system inertial measurement unit (MEMS IMU) has the advantages of being low cost, small volume, and lightweight, but low precision and poor stability are still problems that cannot be ignored
Only a few MEMS gyros can reach the level of tactical grade [1], and these tactical grade gyros are too expensive for common application
The motivation of this paper is to develop the anomaly diagnosis method, and improve the reliability of the MEMS redundant inertial navigation system (RINS)
Summary
Micro-electro-mechanical system inertial measurement unit (MEMS IMU) has the advantages of being low cost, small volume, and lightweight, but low precision and poor stability are still problems that cannot be ignored. The data anomalies are comprised of sensor faults and outliers. The faults refer to the failure and variation of sensor characteristics, which are probably caused by harsh environmental conditions, Appl. The short-duration auto-recovery can be corrected with outlier eliminating methods. The latterThe includes large outlier anomalies can bewell corrected well with outlier eliminating latter includes largepatches, outlier hard faults, permanent failures, etc. Since the deviation of anomaly points caused by sensor faults will be diminished after the outlier eliminating processing, the original statistical characteristics of the faults will change. To solve the problems above, this paper proposed a diagnosis method for data anomalies in MEMS RINS.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
