Algorithm And Experimental Study For Signal Reconstruction Of Multifunctional Sensor

In recent years, multifunctional sensing techniques have become pervasive and essential in particular engineering and science fields, which draw much more attention than traditional sensor. Since multifunctional sensors ensure the merits of small package, low consumption and smart function, they coherently improve the precision and stability of estimation and judgment for measurement system, moreover increase the exist probability in antagonistic environment. Consequently, the rapid development of multifunctional sensing technique brings many new requests to signal processing theories and methods. Since traditional signal reconstruction technologies can not solve the existing problem such as nonlinearity, regressing precision and gross error detection, this thesis systemically studies nonlinear signal reconstruction of multifunctional sensor and presents several practical strategies according to these problems inorder to prompt the further development and application of multifunctional sensors.In terms of actual situation that the training sample set of multifunctional sensor inevitably comprises gross error, our research presents robust estimation and gross error rejection strategies separately, which aim at achieving better robustness and higher efficiency in signal reconstruction. As one of robust estimation studies, M-estimator bases on maximum likelihood estimation theory. By calculating 1-morm of residual, this method can efficiently restrain the effect to whole errors caused by outliers, furthermore make up for the deficiency of Least Squares in case of experimental data comprising gross error. On the other hand, based on equivalent weight thought, Robust Least Squares algorithm combines Weighted Least Squares method and robust estimation to resist the effect of gross error and maintain the merits of traditional Least Squares. Emulation results show that both M-estimator and Robust Least Squares are provided with excellent robustness and convergency.In gross error detection and rejection study, Cross Validation and F-S test are brought forward. Therein, Cross Validation algorithm proceeds repeated random sampling and regresses the experimental data with Radial Basis Function neural network. Then optimal training data and systemic model are finally determined through optimizing above calculations. Additionally, for considering that traditional methods of gross error detection are easy to misjudge potential case and gross error, F-S test is founded upon the studentized residual and externally studentized to be capable of distinguishing these two cases efficiently. Thereafter, gross errors will be located and replaced with the estimations. Simulation results verify the wonderful validities of Cross Validation and F-S test algorithm in gross error detection and recovery aspect.A novel numerical solution method, Moving Least Squares is employed to solve nonlinear reconstruction of multi-functional sensor with a view that Least Squares is restricted in global regression. On the basis of construction method and characters of interpolated function, Moving Least Squares reasonably chooses basis and weight function, and then acquires the reconstructed value of input signals precisely. However, according to each single point, Moving Least Squares will degenerate to Least Squares method. In order to avoid the singular solution, this thesis proposes a modified algorithm, namely Improved Moving Least Squares. In terms of calculating the equivalent orthogonal basis function, this improved method prevents solving the singular functions and simplifies the reconstruction procedure simultaneously.For the purpose of verifying effectiveness of nonlinear signal reconstruction methods in practical model, the research proceeds a concentration measurement experiment of ternary solution. The multifunctional sensor integrated with four-electrode and ultrasonic sensitive material can obtain the information of mixed solution like oil content, salt content and temperature in different cases, and output corresponding signal as conductivity and transit time. Finally, Improved Moving Least Squares method is applied to regress the obtained data, and accomplishes the input signal reconstruction through establishing the inverse model of sensor. Experimental results are satisfying and definitely prove the feasibility of the proposed signal reconstruction method in practical application.