Multi-Channel ARMA Signal Covariance Intersection Fusion Kalman Smoother

2013 ◽  
Vol 655-657 ◽  
pp. 701-704
Author(s):  
Peng Zhang
Keyword(s):  
Steady State ◽  
Kalman Filtering ◽  
Geometric Interpretation ◽  
Fusion Method ◽  
Filtering Method ◽  
Kalman Smoother ◽  
Cross Covariance ◽  
Covariance Intersection ◽  
Optimal Signal

For the multi-channel ARMA signal with two sensors, by the classical Kalman filtering method and the covariance intersection (CI) fusion method, a covariance intersection fusion steady-state Kalman signal smoother is presented, which is independent of the unknown cross-covariance. It is proved that its accuracy is higher than that of each local Kalman signal smoother, and is lower than that of the optimal signal fuser weighted by matrices. The geometric interpretation of the above accuracy relations are presented based on the covariance ellipses. A simulation example result shows its effectiveness and correctness.

scholarly journals A Hybrid Method to Improve the BLE-Based Indoor Positioning in a Dense Bluetooth Environment

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 424 ◽  
Author(s):  
Ke Huang ◽  
Ke He ◽  
Xuecheng Du
Keyword(s):  
Kalman Filtering ◽  
Hybrid Method ◽  
Dead Reckoning ◽  
Sliding Window ◽  
Signal Strength ◽  
Indoor Positioning ◽  
Fusion Method ◽  
Filtering Method ◽  
Calculation Results ◽  
Received Signal Strength Indication

Indoor positioning using Bluetooth Low Energy (BLE) beacons has attracted considerable attention after the release of the BLE protocol. A number of efforts have been exerted to improve the performance of BLE-based indoor positioning. However, few studies pay attention to the BLE-based indoor positioning in a dense Bluetooth environment, where the propagation of BLE signals become more complex and more fluctuant. In this paper, we draw attention to the problems resulting from the dense Bluetooth environment, and it turns out that the dense Bluetooth environment would result in a high received signal strength indication (RSSI) variation and a longtime interval collection of BLE. Hence, to mitigate the effects of the dense Bluetooth environment, we propose a hybrid method fusing sliding-window filtering, trilateration, dead reckoning and the Kalman filtering method to improve the performance of the BLE indoor positioning. The Kalman filter is exploited to merge the trilateration and dead reckoning. Extensive experiments in a real implementation are conducted to examine the performance of three approaches: trilateration, dead reckoning and the fusion method. The implementation results proved that the fusion method was the most effective method to improve the positioning accuracy and timeliness in a dense Bluetooth environment. The positioning root-mean-square error (RMSE) calculation results have showed that the hybrid method can achieve a real-time positioning and reduce error of indoor positioning.

Covariance Intersection Fusion Robust Steady-State Kalman Smoother for Multisensor System with Uncertain Noise Variances

2013 ◽  
Vol 475-476 ◽  
pp. 476-481
Author(s):  
Wen Juan Qi ◽  
Peng Zhang ◽  
Zi Li Deng
Keyword(s):  
Monte Carlo Simulation ◽  
Steady State ◽  
Tracking System ◽  
Lyapunov Equation ◽  
Conservative System ◽  
Upper Bounds ◽  
Worst Case ◽  
Multisensor System ◽  
Kalman Smoother ◽  
Covariance Intersection

This paper deals with the problem of designing covariance intersection fusion robust steady-state Kalman smoother for multisensor system with uncertain noise variances. Using the minimax robust estimation principle, the local and covariance intersection (CI) fusion robust steady-state Kalman smoothers are presented based on the worst-case conservative system with the conservative upper bounds of noise variances. Their robustness is proved based on the proposed Lyapunov equation, and the robust accuracy of CI fuser is higher than that of each local robust Kalman smoother. A Monte-Carlo simulation of three sensors tracking system verifies their robustness and robust accuracy relations.

Study of Steady-State Kalman Filtering Based on Thresholding Optimal Gain

2014 ◽  
Vol 599-601 ◽  
pp. 1724-1728
Author(s):  
Tong Xu Guo ◽  
Ying Wang
Keyword(s):  
Steady State ◽  
Kalman Filtering ◽  
A Priori ◽  
Filtering Method ◽  
Filtering Algorithm ◽  
The Core ◽  
Dynamic Tracking ◽  
Kalman Gain ◽  
A Priori Estimation ◽  
Computing Capacity

For the devices with limited computing capacity and processing time, this paper proposes a new steady-state kalman filtering method based on thresholding optimal gain. The core algorithm is about improving the updating stage of the kalman filtering with a double-threshold optimal gain, which improves the dynamic tracking performance of the system steady-state while reducing the filtering algorithm computation. The results of the simulation prove that the algorithm has a better steady-state convergence effect than traditional kalman filtering algorithm. The method proposed in this paper can be widely used in many occasions, as it simplifies the computation of the optimal kalman gain and the a priori estimation.

Steady-state Kalman filtering with nonstationary noise

2003 ◽  
Vol 24 (2) ◽  
pp. 57-71 ◽  
Author(s):  
P. M. Mäkilä ◽  
J. Paattilammi
Keyword(s):  
Steady State ◽  
Kalman Filtering

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.

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