scholarly journals An Initial Value Estimation Method for the Kalman and Extended Kalman Filters in Underground Metal Detection

2019 ◽  
Vol 9 (19) ◽  
pp. 4113 ◽  
Author(s):  
Yadong Wan ◽  
Zhen Wang ◽  
Peng Wang ◽  
Zhiyang Liu ◽  
Na Li ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Estimation Method ◽  
Detection Methods ◽  
Polarizability Tensor ◽  
Filter Method ◽  
Excellent Performance ◽  
Initial Value ◽  
Initial State ◽  
Metal Detection ◽  
Value Estimation

As an underground metal detection technology, the electromagnetic induction (EMI) method is widely used in many cases. Therefore, the EMI detection algorithms with excellent performance are worth studying. One of the EMI detection methods in the underground metal detection is the filter method, which first obtains the secondary magnetic field data and then uses the Kalman filter (KF) and the extended Kalman filter (EKF) to estimate the parameters of metal targets. However, the traditional KF methods used in the underground metal detection have an unsatisfactory performance of the convergence as the algorithms are given a random or a fixed initial value. Here, an initial state estimation algorithm for the underground metal detection is proposed. The initial state of the target’s horizontal position is estimated by the first order central moments of the secondary field strength map. In addition, the initial state of the target’s depth is estimated by the full width at half maximum (FWHM) method. In addition, the initial state of the magnetic polarizability tensor is estimated by the least squares method. Then, these initial states are used as the initial values for KF and EKF. Finally, the position, posture and polarizability of the target are recursively calculated. A simulation platform for the underground metal detection is built in this paper. The simulation results show that the initial value estimation method proposed for the filtering algorithm has an excellent performance in the underground metal detection.

Interlaced Attitude Estimation for Spacecraft Using Vector Observations

2021 ◽  
Vol 01 (03) ◽  
Author(s):  
Lubin Chang
Keyword(s):  
Prior Information ◽  
State Of The Art ◽  
Estimation Method ◽  
Attitude Estimation ◽  
The Body ◽  
Body Frame ◽  
Initial Value ◽  
Initial State ◽  
Initial Errors ◽  
Initial Attitude

This paper proposes an interlaced attitude estimation method for spacecraft using vector observations, which can simultaneously estimate the constant attitude at the very start and the attitude of the body frame relative to its initial state. The arbitrary initial attitude, described by constant attitude at the very start, is determined using quaternion estimator which requires no prior information. The multiplicative extended Kalman filter (EKF) is competent for estimating the attitude of the body frame relative to its initial state since the initial value of this attitude is exactly known. The simulation results show that the proposed algorithms could achieve better performance compared with the state-of-the-art algorithms even with extreme large initial errors. Meanwhile, the computational burden is also much less than that of the advanced nonlinear attitude estimators.

scholarly journals Iterative Truncated Unscented Particle Filter

Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 214
Author(s):  
Yanbo Wang ◽  
Fasheng Wang ◽  
Jianjun He ◽  
Fuming Sun
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
State Estimation ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Process Models ◽  
Filter Method ◽  
Proposal Distribution ◽  
Truncation Method ◽  
Unscented Particle Filter

The particle filter method is a basic tool for inference on nonlinear partially observed Markov process models. Recently, it has been applied to solve constrained nonlinear filtering problems. Incorporating constraints could improve the state estimation performance compared to unconstrained state estimation. This paper introduces an iterative truncated unscented particle filter, which provides a state estimation method with inequality constraints. In this method, the proposal distribution is generated by an iterative unscented Kalman filter that is supplemented with a designed truncation method to satisfy the constraints. The detailed iterative unscented Kalman filter and truncation method is provided and incorporated into the particle filter framework. Experimental results show that the proposed algorithm is superior to other similar algorithms.

scholarly journals Prediksi Harga Saham pada PT. ABCD menggunakan Ensemble Kalman Filter

2018 ◽  
Vol 4 (1) ◽  
pp. 24-27
Author(s):  
Puspandam Katias ◽  
Denis Fidita ◽  
Teguh - Herlambang
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Stock Exchange ◽  
Estimation Method ◽  
Optimal Investment ◽  
Share Price ◽  
Filter Method ◽  
Price Estimation ◽  
Future Return ◽  
Kalman Filter Method

Stock Exchange is established as an effort to link both stock / security sellers and buyers. Securities often traded in stock market is share. The intention of an investor in investment is to have the lowest risk and to gain the highest profit. To make decision for optimal investment, calculation on the estimate of future return to be gained is necessarily made. One of estimate calculation methods considered the most objective is by applying  the Ensemble Kalman filter (EnKF) method. Ensemble Kalman filter is a method of estimation of condition variable of discrete linear dynamic system that minimizes covarian error of estimation. So, this study aims to apply share price estimation method for close prices of share of PT. ABCD by Ensemble Kalman Filter method as investor' consideration in investment with an error of  3% - 5%.

scholarly journals PERBAIKAN MODEL SEASONAL ARIMA DENGAN METODE ENSEMBLE KALMAN FILTER PADA HASIL PREDIKSI CURAH HUJAN

2019 ◽  
Vol 19 (1) ◽  
pp. 9
Author(s):  
Dwi Anugrah Wibisono ◽  
Dian Anggraeni ◽  
Alfian Futuhul Hadi
Keyword(s):  
Time Series ◽  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Estimation Method ◽  
Filter Method ◽  
Sarima Model ◽  
Forecast Data ◽  
Kalman Filter Method ◽  
Algorithm Implementation

Forecasting is a time series analytic that used to find out upcoming improvement in the next event using past events as a reference. One of the forecasting models that can be used to predict a time series is Kalman Filter method. The modification of the estimation method of Kalman Filter is Ensemble Kalman Filter (EnKF). This research aims to find the result of EnKF algorithm implementation on SARIMA model. To start with, preticipation forecast data is changed in the form of SARIMA model to obtain some SARIMA model candidates. Next, this best model of SARIMA applied to Kalman Filter models. After Kalman Filter models created, forecasting could be done by applying pass rainfall data to the models. It can be used to predict rainfall intensity for next year. The quality of this forecasting can be assessed by looking at MAPE’s value and RMSE’s value. This research shows that enkf method relative can fix sarima method’s model, proved by mape and rmse values which are smaller and indicate a more accurate prediction. Keywords: Ensemble Kalman Filter, Forecast, SARIMA

scholarly journals Econometric Models in Resident Value of Investment

2013 ◽  
Vol 4 (3) ◽  
pp. 49-63
Author(s):  
Paweł Kliber ◽  
Artur Stefański
Keyword(s):  
Discount Rate ◽  
Estimation Method ◽  
Econometric Models ◽  
Economic Sector ◽  
Initial Value ◽  
Investment Ratio ◽  
Analyze Period ◽  
Value Estimation ◽  
Independent Variable ◽  
The Impact

The aim of the study is to analyze what is the impact of: analyze period, resident value estimation method, discount rate and economic sector of the investor on the level of resident value to initial value of investment ratio. In the article, basing on 43 investments made by investors form MSP sector whose purpose was to purchase truck car of capacity to 3,5t, four econometric models were prepared: logit, probit, tobit, and logit-tobit to explain the dependence described in the aim of the study. All models are statistically important. In all models only one independent variable is always statistically important – analyze period. The longer analyze period is, the smaller resident value to initial value of investment ratio is. In order to compare models: MSE, RMSE, MAE, MAPE ratios were used. The best adaptation to data was observed when logit-tobit model is used

scholarly journals State of Power Estimation of Echelon-Use Battery Based on Adaptive Dual Extended Kalman Filter

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5579
Author(s):  
Enguang Hou ◽  
Yanliang Xu ◽  
Xin Qiao ◽  
Guangmin Liu ◽  
Zhixue Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Estimation Method ◽  
Internal Resistance ◽  
The State ◽  
Extended Kalman Filtering ◽  
Initial Value ◽  
Process Noise ◽  
Noise Covariance ◽  
State Of Power

Owing to the degradation of the performance of a retired battery and the unclear initial value of the state of charge (SOC), the estimation of the state of power (SOP) of an echelon-use battery is not accurate. An SOP estimation method based on an adaptive dual extended Kalman filter (ADEKF) is proposed. First, the second-order Thevenin equivalent model of the echelon-use battery is established. Second, the battery parameters are estimated by the ADEKF: (a) the SOC is estimated based on an adaptive extended Kalman filtering algorithm, that uses the process noise covariance and observes the noise covariance , and (b) the ohmic internal resistance and actual capacity are estimated based on the aforementioned algorithm, that uses the process noise covariance and observes the noise covariance . Third, the working voltage and internal resistance are predicted using optimal estimation, and the SOP of the echelon-use battery is estimated. MATLAB simulation results show that, regardless of whether or not the initial value of the SOC is clear, the proposed algorithm can be adjusted to the adaptive algorithm, and if the estimation accuracy error of the echelon-use battery SOP is less than 4.8%, it has high accuracy. This paper provides a valuable reference for the prediction of the SOP of an echelon-use battery, and will be helpful for understanding the behavior of retired batteries for further discharge and use.

scholarly journals Performance evaluation of GPS / BDS combined single-point positioning based on RTKLIB

2020 ◽  
Vol 165 ◽  
pp. 03009
Author(s):  
Li Yan-yi ◽  
Huang Jin ◽  
Tang Ming-xiu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Single Point ◽  
Least Square Method ◽  
Least Square ◽  
Filter Method ◽  
Single System ◽  
Positioning Error ◽  
Dual Mode ◽  
Kalman Filter Method

In order to evaluate the performance of GPS / BDS, RTKLIB, an open-source software of GNSS, is used in this paper. In this paper, the least square method, the weighted least square method and the extended Kalman filter method are respectively applied to BDS / GPS single system for data solution. Then, the BDS system and GPS system are used for fusion positioning and the positioning results of the two systems are compared with that of the single system. Through the comparison of experiments, on the premise of using the extended Kalman filter method for positioning, when the GPS signal is not good, BDS data is introduced for dual-mode positioning, the positioning error in e direction is reduced by 36.97%, the positioning error in U direction is reduced by 22.95%, and the spatial positioning error is reduced by 16.01%, which further reflects the advantages of dual-mode positioning in improving a system robustness and reducing the error.

Weak Transient Electromagnetic Radiation Signal Detection Method Considering the New Watershed Image Segmentation Algorithm

2019 ◽  
Vol 34 (03) ◽  
pp. 2054009
Author(s):  
Wenjun Huo ◽  
Peng Chu ◽  
Kai Wang ◽  
Liangting Fu ◽  
Zhigang Niu ◽  
...  
Keyword(s):  
Electromagnetic Radiation ◽  
Cross Correlation ◽  
Detection Efficiency ◽  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Detection Methods ◽  
Signal To Noise ◽  
Long Distance ◽  
Transient Electromagnetic ◽  
Noise Ratio

In order to study the detection methods of weak transient electromagnetic radiation signals, a detection algorithm integrating generalized cross-correlation and chaotic sequence prediction is proposed in this paper. Based on the dual-antenna test and cross-correlation information estimation method, the detection of aperiodic weak discharge signals under low signal-to-noise ratio is transformed into the estimation of periodic delay parameters, and the noise is reduced at the same time. The feasibility of this method is verified by simulation and experimental analysis. The results show that under the condition of low signal-to-noise ratio, the integrated method can effectively suppress the influence of 10 noise disturbances. It has a high detection probability for weak transient electromagnetic radiation signals, and needs fewer pulse accumulation times, which improves the detection efficiency and is more suitable for long-distance detection of weak electromagnetic radiation sources.

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