An improved Kalman filtering method for indoor location

2016 ◽  
Author(s):  
Tingting Guo ◽  
Feng Qiao ◽  
Mingzhe Liu ◽  
Aidong Xu ◽  
Qinning Liu ◽  
...  
Keyword(s):  
Kalman Filtering ◽  
Indoor Location ◽  
Filtering Method

Reducing Quantitative Fluctuation of Laser-Induced Breakdown Spectroscopy by Kalman Filtering

2013 ◽  
Vol 333-335 ◽  
pp. 243-247 ◽  
Author(s):  
Lan Xiang Sun ◽  
Zhi Bo Cong ◽  
Yong Xin ◽  
Li Feng Qi ◽  
Yang Li
Keyword(s):  
Kalman Filtering ◽  
Elemental Concentration ◽  
Estimation Error ◽  
Laser Induced Breakdown Spectroscopy ◽  
System State ◽  
Large Signal ◽  
Filtering Method ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Quantitative Results

Laser-induced breakdown spectroscopy (LIBS) is excellent for its potential of online compositional analysis. Large signal fluctuation is the major obstacle of LIBS for quantitative analysis application. A kalman filtering method is proposed to estimate the elemental concentration and smooth the quantitative results. The system state model and the measurement model are deduced. The relation matrix between the measured values and system state is estimated based on calibration curve built on some standard samples, and the measurement noise matrix is estimated by the variance of multiple measurements of the spectral intensity. In order to make Kalman filter follow the changes of elemental concentration, the initial value of the covariance matrix of estimation error is reset as a certain rule. The experimental results show that the Kalman filtering method can greatly reduce the fluctuation of quantitative results and improve the measurement accuracy.

scholarly journals Three Revised Kalman Filtering Models for Short-Term Rail Transit Passenger Flow Prediction

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Pengpeng Jiao ◽  
Ruimin Li ◽  
Tuo Sun ◽  
Zenghao Hou ◽  
Amir Ibrahim
Keyword(s):  
Real Time ◽  
Nonparametric Regression ◽  
Kalman Filtering ◽  
Rail Transit ◽  
Correction Coefficient ◽  
Short Term ◽  
Transit System ◽  
Filtering Method ◽  
Passenger Flow ◽  
Model Based

Short-term prediction of passenger flow is very important for the operation and management of a rail transit system. Based on the traditional Kalman filtering method, this paper puts forward three revised models for real-time passenger flow forecasting. First, the paper introduces the historical prediction error into the measurement equation and formulates a revised Kalman filtering model based on error correction coefficient (KF-ECC). Second, this paper employs the deviation between real-time passenger flow and corresponding historical data as state variable and presents a revised Kalman filtering model based on Historical Deviation (KF-HD). Third, the paper integrates nonparametric regression forecast into the traditional Kalman filtering method using a Bayesian combined technique and puts forward a revised Kalman filtering model based on Bayesian combination and nonparametric regression (KF-BCNR). A case study is implemented using statistical passenger flow data of rail transit line 13 in Beijing during a one-month period. The reported prediction results show that KF-ECC improves the applicability to historical trend, KF-HD achieves excellent accuracy and stability, and KF-BCNR yields the best performances. Comparisons among different periods further indicate that results during peak periods outperform those during nonpeak periods. All three revised models are accurate and stable enough for on-line predictions, especially during the peak periods.

Application of Federated Filter to AUV Based on Terrain-Aided SINS

2014 ◽  
Vol 711 ◽  
pp. 338-341 ◽  
Author(s):  
Qi Wang ◽  
Cheng Shan Qian ◽  
Zi Jia Zhang ◽  
Chang Song Yang
Keyword(s):  
Kalman Filtering ◽  
Navigation System ◽  
Autonomous Underwater Vehicles ◽  
Inertial Navigation ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Filtering Method ◽  
Long Time ◽  
Federated Filtering ◽  
Strapdown Inertial Navigation

To improve the navigation precision and reliability of autonomous underwater vehicles, a terrain-aided strapdown inertial navigation based on Federated Filter (FF) is proposed in this paper. The characteristics of strapdown inertial navigation system and terrain-aided navigation system are described in this paper, and Federated Filtering method is applied to the information fusion. Simulation experiments of novel integrated navigation system proposed in the paper were carried out comparing to the traditional Kalman filtering methods. The experiment results suggest that the Federated Filtering method is able to improve the long-time navigation precision and reliability, relative to the traditional Kalman Filtering method.

Evaluation of spectral modulated interferograms by the extended Kalman filtering method

2005 ◽  
Author(s):  
Igor Gurov ◽  
Petr Hlubina ◽  
Mikhail Taratin ◽  
Alexey Zakharov
Keyword(s):  
Kalman Filtering ◽  
Extended Kalman Filtering ◽  
Filtering Method

scholarly journals Sea Surface Temperature Analysis for Fengyun-3C Data Using Oriented Elliptic Correlation Scales

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8067
Author(s):  
Zhihong Liao ◽  
Bin Xu ◽  
Junxia Gu ◽  
Chunxiang Shi
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Kalman Filtering ◽  
Sea Surface ◽  
Quality Analysis ◽  
Observation Error ◽  
Change Analysis ◽  
Background Error ◽  
Filtering Method ◽  
Correlation Scale

Sea surface temperature (SST) is critical for global climate change analysis and research. In this study, we used visible and infrared scanning radiometer (VIRR) sea surface temperature (SST) data from the Fengyun-3C (FY-3C) satellite for SST analysis, and applied the Kalman filtering methods with oriented elliptic correlation scales to construct SST fields. Firstly, the model for the oriented elliptic correlation scale was established for SST analysis. Secondly, observation errors from each type of SST data source were estimated using the optimal matched datasets, and background field errors were calculated using the model of oriented elliptic correlation scale. Finally, the blended SST analysis product was obtained using the Kalman filtering method, then the SST fields using the optimum interpolation (OI) method were chosen for comparison to validate results. The quality analysis for 2016 revealed that the Kalman analysis with a root-mean-square error (RMSE) of 0.3243 °C had better performance than did the OI analysis with a RMSE of 0.3911 °C, which was closer to the OISST product RMSE of 0.2897 °C. The results demonstrated that the Kalman filtering method with dynamic observation error and background error estimation was significantly superior to the OI method in SST analysis for FY-3C SST data.

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