An Algorithm of Improving Least-Square Method on Tracing Accuracy of Bistatic Sonar System

2014 ◽  
Vol 568-570 ◽  
pp. 168-171 ◽  
Author(s):  
Yong Sun ◽  
Jun Wei Zhao
Keyword(s):  
Full Rank ◽  
Least Square Method ◽  
Singular Value ◽  
Least Square ◽  
Sonar System ◽  
Rank Matrix ◽  
Measured Information ◽  
Tracing Algorithm ◽  
Simulation Results ◽  
Value Decomposition

The least square algorithms were widely used in the estimation of localizing and tracing situation in military fields. In this paper, we proposed a method of using a full rank matrix instead of using singular value decomposition to solving the non-full rank matrix. Therefore the improving least square (ILS) algorithm was emerged at this situation. The simulation results show that the proposed tracing algorithm exhibits higher accuracy compared with the least square algorithm. This new method can take full application of the measured information to improved the tracing accuracy in the whole controlled area.

An Improved of Extended Kalman Filtering Method on Tracking Accuracy of Bistatic Sonar System

2014 ◽  
Vol 596 ◽  
pp. 494-497
Author(s):  
Yong Sun ◽  
Jun Wei Zhao
Keyword(s):  
Kalman Filtering ◽  
Tracking Accuracy ◽  
Extended Kalman Filtering ◽  
Filtering Method ◽  
Filter Methods ◽  
Sonar System ◽  
Tracking Process ◽  
Measured Information ◽  
Tracing Algorithm ◽  
Simulation Results

The Extended Kalman Filter methods were widely used in the estimation of tracing situation in military fields. In this paper, we proposed a method of using multiple iteration of the observation and covariance matrix in the measuring equations during the tracking process in bistatic sonar system. Therefore the iterating extended kalman filtering (IEKF) algorithm was emerged at this situation. The simulation results show that the proposed tracing algorithm exhibits higher accuracy compared with the EKF algorithm. This new method can take full application of the measured information to improved the tracing accuracy in the whole controlled area. Keywords: bistatic sonar; tracing accuracy; IEKF algorithm; target moving analysis

scholarly journals A Note on Full-Rank Factorization of Matrix

2019 ◽  
Vol 15 (2) ◽  
pp. 152-154
Author(s):  
Gyan Bahadur Thapa ◽  
J. López-Bonilla ◽  
R. López-Vázquez
Keyword(s):  
Singular Value Decomposition ◽  
Full Rank ◽  
Singular Value ◽  
The Matrix ◽  
Rank Factorization ◽  
Value Decomposition

We exhibit that the Singular Value Decomposition of a matrix Anxm implies a natural full-rank factorization of the matrix.

scholarly journals Online Parameter Identification and State of Charge Estimation of Battery Based on Multitimescale Adaptive Double Kalman Filter Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Wenxian Duan ◽  
Chuanxue Song ◽  
Yuan Chen ◽  
Feng Xiao ◽  
Silun Peng ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Unscented Kalman Filter ◽  
Least Square Method ◽  
State Of Charge ◽  
Least Square ◽  
Accurate Estimation ◽  
Battery Life ◽  
Model Parameters ◽  
Simulation Results

An accurate state of charge (SOC) can provide effective judgment for the BMS, which is conducive for prolonging battery life and protecting the working state of the entire battery pack. In this study, the first-order RC battery model is used as the research object and two parameter identification methods based on the least square method (RLS) are analyzed and discussed in detail. The simulation results show that the model parameters identified under the Federal Urban Driving Schedule (HPPC) condition are not suitable for the Federal Urban Driving Schedule (FUDS) condition. The parameters of the model are not universal through the HPPC condition. A multitimescale prediction model is also proposed to estimate the SOC of the battery. That is, the extended Kalman filter (EKF) is adopted to update the model parameters and the adaptive unscented Kalman filter (AUKF) is used to predict the battery SOC. The experimental results at different temperatures show that the EKF-AUKF method is superior to other methods. The algorithm is simulated and verified under different initial SOC errors. In the whole FUDS operating condition, the RSME of the SOC is within 1%, and that of the voltage is within 0.01 V. It indicates that the proposed algorithm can obtain accurate estimation results and has strong robustness. Moreover, the simulation results after adding noise errors to the current and voltage values reveal that the algorithm can eliminate the sensor accuracy effect to a certain extent.

A Research of Improving Data Fusing Algorithm Localization in Multistaic Sonar

2014 ◽  
Vol 530-531 ◽  
pp. 240-244
Author(s):  
Yong Sun ◽  
Jun Wei Zhao
Keyword(s):  
New Method ◽  
Localization Algorithm ◽  
Distance Measurements ◽  
Localization Accuracy ◽  
Sonar System ◽  
Measured Information ◽  
Simulation Results ◽  
Data Fusing

For the purpose of improving the localization accuracy of bistatic sonar in baseline districts and side districts, the most effective method is to increase the number of transmitting and receiving stations, which forms a multistatic sonar system. The mature algorithm of multistatic sonar system which contains three distance measurements volume in one subset, calls the multistatic time-only localization (TOL) algorithm. This paper proposes a new algorithm which merges the TOL algorithm and IBOL algorithm. of improving the bearing-only localization algorithm. The simulation results show that the proposed localization algorithm exhibits higher accuracy compared with the TOL algorithm and IBOL algorithm. This new method can take full application of the measured information to improved the localization accuracy in the whole controlled area.

Asymmetric Prandtl-Ishlinskii Hysteresis Model for Giant Magnetostrictive Actuator

2016 ◽  
Vol 20 (2) ◽  
pp. 223-230 ◽  
Author(s):  
Zhuoyun Nie ◽  
◽  
Chanjun Fu ◽  
Ruijuan Liu ◽  
Dongsheng Guo ◽  
...  
Keyword(s):  
Linear Function ◽  
Polynomial Function ◽  
Least Square Method ◽  
Least Square ◽  
Hysteresis Model ◽  
Hysteresis Behavior ◽  
Magnetostrictive Actuator ◽  
Simulation Results

An asymmetric Prandtl–Ishlinskii (API) hysteresis model for a giant magnetostrictive actuator (GMA) is proposed in this paper. The classical Prandtl–Ishlinskii (PI) model is analyzed and divided into two parts: linear function and operator summation. To enhance model asymmetry, a polynomial function is used in the API model as the center curve of the hysteresis instead of the linear function. The remaining curve of the hysteresis is modeled by a new operator that provides some basic asymmetric hysteresis. In this manner, the proposed API model requires relatively less operators and fewer parameters to describe the asymmetric hysteresis behavior of the GMA. All parameters of the API model are identified by a standard least square method. Simulation results show that the API model is very successful in formulating an asymmetric hysteresis of the GMA. In addition, it provides better identification results compared with the classical PI model.

Satellite Fault Detection and Isolation through Decoupling Parity Space

2011 ◽  
Vol 110-116 ◽  
pp. 4406-4414
Author(s):  
Zheng Wang ◽  
Yan Jun Li ◽  
Xiao Wei Sun
Keyword(s):  
Singular Value Decomposition ◽  
Singular Value ◽  
Fault Detection And Isolation ◽  
Actuator Fault ◽  
Actuator Faults ◽  
Eigen Value ◽  
Parity Space ◽  
Simulation Results ◽  
Value Decomposition ◽  
Space Method

In order to detect and isolate orbiting satellite actuator faults, a decoupling parity space method was extended. The decoupling parity vector was obtained using this method by singular value decomposition. Sometimes this vector may not exist, then by singular value substitution or generalized Eigen value method was used for solving the optimal approximation. The vector can easily make satellite actuator fault detected and isolated. The simulation results showed the effectiveness of the proposed algorithm.

How We Solve the Weights in Our Surrogate Models Matters

2019 ◽  
Vol 141 (7) ◽  
Author(s):  
Daniel Correia ◽  
Daniel N. Wilke
Keyword(s):  
Linear System ◽  
Surrogate Model ◽  
Cross Validation ◽  
Full Rank ◽  
Surrogate Models ◽  
Rank Matrix ◽  
Cross Validation Error ◽  
Monotonically Decreasing Function ◽  
Value Decomposition ◽  
Initial Starting

The construction of surrogate models, such as radial basis function (RBF) and Kriging-based surrogates, requires an invertible (square and full rank matrix) or pseudoinvertible (overdetermined) linear system to be solved. This study demonstrates that the method used to solve this linear system may result in up to five orders of magnitude difference in the accuracy of the constructed surrogate model using exactly the same information. Hence, this paper makes the canonic and important point toward reproducible science: the details of solving the linear system when constructing a surrogate model must be communicated. This point is clearly illustrated on a single function, namely the Styblinski–Tang test function by constructing over 200 RBF surrogate models from 128 Latin Hypercubed sampled points. The linear system in the construction of each surrogate model was solved using LU, QR, Cholesky, Singular-Value Decomposition, and the Moore–Penrose pseudoinverse. As we show, the decomposition method influences the utility of the surrogate model, which depends on the application, i.e., whether an accurate approximation of a surrogate is required or whether the ability to optimize the surrogate and capture the optimal design is pertinent. Evidently the selection of the optimal hyperparameters based on the cross validation error also significantly impacts the utility of the constructed surrogate. For our problem, it turns out that selecting the hyperparameters at the lowest cross validation error favors function approximation but adversely affects the ability to optimize the surrogate model. This is demonstrated by optimizing each constructed surrogate model from 16 fixed initial starting points and recording the optimal designs. For our problem, selecting the optimal hyperparameter that coincides with the lowest monotonically decreasing function value significantly improves the ability to optimize the surrogate for most solution strategies.

A FRT - SVD Based Blind Medical Watermarking Technique for Telemedicine Applications

2021 ◽  
pp. 632-653
Author(s):  
Surekah Borra ◽  
Rohit Thanki
Keyword(s):  
Singular Value Decomposition ◽  
Medical Image ◽  
Medical Images ◽  
Image Watermarking ◽  
Region Of Interest ◽  
Singular Value ◽  
Ridgelet Transform ◽  
Simulation Results ◽  
The Individual ◽  
Value Decomposition

In this article, a blind and robust medical image watermarking technique based on Finite Ridgelet Transform (FRT) and Singular Value Decomposition (SVD) is proposed. A host medical image is first transformed into 16 × 16 non-overlapping blocks and then ridgelet transform is applied on the individual blocks to obtain sets of ridgelet coefficients. SVD is then applied on these sets, to obtain the corresponding U, S and V matrix. The watermark information is embedded into the host medical image by modification of the value of the significant elements of U matrix. This proposed technique is tested on various types of medical images such as X-ray and CT scan. The simulation results revealed that this technique provides better imperceptibility, with an average PSNR being 42.95 dB for all test medical images. This technique also overcomes the limitation of the existing technique which is applicable on only the Region of Interest (ROI) of the medical image.

Design of Compensator for Input Dead Zone of Actuator Nonlinearities

2013 ◽  
Vol 17 (6) ◽  
pp. 805-812 ◽  
Author(s):  
Weijie Chen ◽  
◽  
Jundong Wu ◽  
Jinhua She ◽  
◽  
...  
Keyword(s):  
Design Method ◽  
Dead Zone ◽  
Singular Value ◽  
Periodic Signal ◽  
Control Performance ◽  
System Configuration ◽  
Actuator Nonlinearities ◽  
Simulation Results ◽  
The Stability ◽  
Value Decomposition

This paper considers the problem of tracking a periodic signal for a plant with an input dead zone in the actuator. The nonlinearity greatly degrades control performance. To solve this problem, we incorporate an equivalent-input-disturbance (EID) compensator into a repetitive control system (RCS), resulting in a new system configuration. We combine the linear-matrixinequality technique with singular-value decomposition to analyze the stability of the system and to devise a design method. Unlike other methods, this one does not require any information about the dead zone. Simulation results demonstrate its effectiveness.

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