Spatially recursive estimation and Gaussian process dynamic models of bat flapping flight

2018 ◽  
Vol 95 (1) ◽  
pp. 217-237 ◽  
Author(s):  
Matt Bender ◽  
Li Tian ◽  
Xiaozhou Fan ◽  
Andrew Kurdila ◽  
Rolf Müller
Author(s):  
Matthew Bender ◽  
Xu Yang ◽  
Hui Chen ◽  
Andrew Kurdila ◽  
Rolf Muller

2014 ◽  
Vol 67 (4) ◽  
pp. 603-615 ◽  
Author(s):  
Hongmei Chen ◽  
Xianghong Cheng ◽  
Haipeng Wang ◽  
Xu Han

Gaussian process regression (GPR) is used in a Spare-grid Quadrature Kalman filter (SGQKF) for Strap-down Inertial Navigation System (SINS)/odometer integrated navigation to bridge uncertain observation outages and maintain an estimate of the evolving SINS biases. The SGQKF uses nonlinearized dynamic models with complex stochastic nonlinearities so the performance degrades significantly during observation outages owing to the uncertainties and noise. The GPR calculates the residual output after factoring in the contributions of the parametric model that is used as a nonlinear SINS error predictor integrated into the SGQKF. The sensor measurements and SINS output deviations from the odometer are collected in a data set during observation availability. The GPR is then applied to predict SINS deviations from the odometer and then the predicted SINS deviations are fed to the SGQKF as an actual update to estimate all SINS biases during observation outages. We demonstrate our method's effectiveness in bridging uncertain observation outages in simulations and in real road tests. The results agree with the theoretical analysis, which demonstrate that SGQKF using GPR can maintain an estimate of the evolving SINS biases during signal outages.


2021 ◽  
pp. 20-25
Author(s):  
VIKTOR I. BALABANOV ◽  

The working equipment used for deep loosening of heavy mechanical soils during the restoration of temporarily unclaimed agricultural lands includes a basic machine unit with attachments infl uenced by signifi cant oscillatory loads during its operation. The variability of the physical and mechanical properties of the treated soil aff ects the uneven depth of loosening and the machine operation. This is one of the main technological indicators evaluating the performance features of rippers. The research purpose is to analyze the ripper as a dynamic system and estimate the magnitude of the fl uctuations in the loosening depth depending on the surface irregularities of the machine’s path along the fi eld. The research was carried out according to generally accepted methods using modeling. The authors considered the functioning model of the reclamation ripper in the form of an “environment – machine – technological process” dynamic system, which converts input disturbances and control actions into output ones. The study results of reclamation ripper dynamic models have shown that average terrain irregularities of 5…10 cm result in the amplitude of cutting edge vibrations of a pneumatic-wheeled ripper equaling 8…15 cm and 6…12 cm for the ripper coupled with a caterpillar tractor. According to agrotechnical requirements, a loosening depth of 0.8 m results in the permissible deviations of 8 cm.


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