Stochastic resonance for a bistable system with multiplicative noise and additive recycled noise

Jiang, Shiqi; Qiu, Qian; Yuan, Shihui; Shi, Xiaojiang; Li, Liang; Zhang, Xueyuan; Fu, Kechang; Qin, Dongxing; Guo, Feng; Wang, Zhanxue; Yan, Jiajia; Wang, Lin; Wang, Yuxin

doi:10.1007/s12648-022-02301-7

Cited by 6 publications

(1 citation statement)

References 28 publications

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“…Therefore, it is necessary to fuse the flight parameters estimated by INS and FADS and that the two complement each other to meet the accurate estimation of flight parameters in various flight states. At present, there are two main fusion algorithms for FADS and INS [9][10][11][12][13][14][15][16][17][18]. One is a data fusion algorithm based on complementary filtering, the other is a data fusion algorithm based on Kalman filtering.…”

Section: Introductionmentioning

confidence: 99%

A Variable Gain Complementary Filtering Fusion Algorithm Based on Distributed Inertial Network and Flush Air Data Sensing

Shao,

Zang,

Zhao

et al. 2023

Applied Sciences

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Aiming at the problem that the accuracy of flight parameters calculated by the traditional flush air data sensing (FADS) and inertial navigation system (INS) complementary filter cannot meet the fine real-time control of the aircraft, a data fusion algorithm based on distributed inertial network and FADS is proposed. This method converts the inertial navigation parameters calculated by the distributed inertial network into flight parameters, and using the least squares fitting theory, the flight parameters are obtained from the pressure data measured by the FADS pressure hole. Then, by adjusting the filter constant of the complementary filtering algorithm, the flight parameters calculated by the inertial network are fused with the flight parameters calculated by the air data sensing to obtain high-precision flight parameters. Finally, the simulation results show that the proposed filtering algorithm can keep the flight parameter estimation error less than 0.01 degrees throughout the flight phase. Compared with the traditional complementary filter, the estimation error of the proposed algorithm is smaller.

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