To solve the problem that dynamic Allan variance (DAVAR) with fixed length of window cannot meet the identification accuracy requirement of fiber optic gyro (FOG) signal over all time domains, a dynamic Allan variance analysis method with time-variant window length based on fuzzy control is proposed. According to the characteristic of FOG signal, a fuzzy controller with the inputs of the first and second derivatives of FOG signal is designed to estimate the window length of the DAVAR. Then the Allan variances of the signals during the time-variant window are simulated to obtain the DAVAR of the FOG signal to describe the dynamic characteristic of the time-varying FOG signal. Additionally, a performance evaluation index of the algorithm based on radar chart is proposed. Experiment results show that, compared with different fixed window lengths DAVAR methods, the change of FOG signal with time can be identified effectively and the evaluation index of performance can be enhanced by 30% at least by the DAVAR method with time-variant window length based on fuzzy control.

No abstract

No abstract

Fingerprint recognition is the most widely used identification method at present. However, it still falls short in terms of cross-platform and algorithmic complexity, which exerts a certain effect on the migration of fingerprint data and the development of the system. The conventional image recognition methods require offline standard databases constructed in advance for image access efficiency. The database can provide a pre-processed image via a specific method that probably is compatible merely with the specific recognition algorithm. Then, the specific recognition algorithm starts the process of retrieving these specific pre-proessing images for recognition and inevitably will be blocked from other datasets. The proposed method in this research designed an embedded image processing algorithm based on a Siamese neural network in the recognition method that allows the proposed method to recognize images from any source without constructing a database for image storage in advance. In this research, the proposed method was applied to fingerprint recognition and evaluation of the proposed method was evaluated. The results showed that the accuracy of the proposed algorithm was up to 92%, and its F1 score was up to 0.87. Compared with the conventional fingerprint matching methods, its significant advantage in the FRR, FAR, and CR jointly indicated the remarkable correct recognition rate of the proposed method.

It is difficult to analyze the nonstationary gyro signal in detail for the Allan variance (AV) analysis method. A novel approach in the time-frequency domain for gyro signal characteristics analysis is proposed based on the empirical mode decomposition and Allan variance (EMDAV). The output signal of gyro is decomposed by empirical mode decomposition (EMD) first, and then the decomposed signal is analyzed by AV algorithm. Consequently, the gyro noise characteristics are demonstrated in the time-frequency domain with a three-dimensional (3D) manner. Practical data of fiber optic gyro (FOG) and MEMS gyro are processed by the AV method and the EMDAV algorithm separately. The results indicate that the details of gyro signal characteristics in different frequency bands can be described with the help of EMDAV, and the analysis dimensions are extended compared with the common AV. The proposed EMDAV, as a complementary tool of the AV, which provides a theoretical reference for the gyro signal preprocessing, is a general approach for the analysis and evaluation of gyro performance.