Optimal State Fusion of Linear Systems with Two Channel Observations

2011 ◽  
Vol 467-469 ◽  
pp. 823-828
Author(s):  
Peng Cui ◽  
Hong Guo Zhao ◽  
Mei Zhang
Keyword(s):  
Real Time ◽  
Linear Systems ◽  
Kalman Filtering ◽  
Recursive Algorithm ◽  
Projection Formula ◽  
Optimal State ◽  
Weighted Fusion ◽  
Innovation Analysis ◽  
Good For ◽  
State Fusion

State fusion problem of linear systems with two channel observations is discussed. A globally optimal recursive algorithm is proposed based on projection formula and innovation analysis. Different linear weighted fusion, the algorithm presented is globally optimal, which is equivalent to centralized Kalman filtering. Moreover, the algorithm is good for real-time demand for innovations from different channels are orthogonal.

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.

scholarly journals WFMHT Method of Orbit Maneuver Detection Based on Space-Based Bearing-Only Measurement

2018 ◽  
Vol 36 (6) ◽  
pp. 1185-1192
Author(s):  
Lei Liu ◽  
Jianfeng Cao ◽  
Ye Liu
Keyword(s):  
Real Time ◽  
Situation Awareness ◽  
Detection System ◽  
Hypothesis Test ◽  
Test Methods ◽  
Time Motion ◽  
Weighted Fusion ◽  
Innovation Characteristics ◽  
Detection Window ◽  
Time Awareness

The method of orbit maneuver detection of space targets is investigated using the space-based bearing-only measurement, which aims to acquire a real-time or nearly real-time awareness of orbit maneuver in the space situation awareness. First, the model for estimating real-time motion of a space target is presented, which only uses the space-based bearing-only measurements. The innovation characteristics of the normal orbit and orbit maneuver are analyzed and compared. Second, based on the hypothesis test methods of the distribution characteristic of the stochastic sequence, the WFMHT (i.e., weighted fusion of multi hypothesis tests) method with the innovation is put forward to detect the orbit maneuver. Furthermore, the criterion of determining the weight coefficients is studied. Finally, the method is validated by numeric simulations. The results show that the highest gained success rate is up to 36% with the WFMHT method than the prevalent Chi2 method. With the WFMHT method, the detection system achieves a strengthened robustness with greatly shortened detection window. The research will be beneficial to construction of our space situation awareness system.

Error correction for phase detection by recursive algorithm real time DFT

2002 ◽  
Vol 141 (1) ◽  
pp. 8-17 ◽  
Author(s):  
Tsuyoshi Funaki ◽  
Kenji Matsuura ◽  
Shunsuke Tanaka
Keyword(s):  
Error Correction ◽  
Real Time ◽  
Recursive Algorithm ◽  
Phase Detection

scholarly journals Kalman filtering for general discrete-time linear systems

1999 ◽  
Vol 44 (10) ◽  
pp. 1829-1839 ◽  
Author(s):  
R. Nikoukhah ◽  
S.L. Campbell ◽  
F. Delebecque
Keyword(s):  
Linear Systems ◽  
Discrete Time ◽  
Kalman Filtering ◽  
Time Linear

scholarly journals An Improved Multisensor Self-Adaptive Weighted Fusion Algorithm Based on Discrete Kalman Filtering

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Shifen Shao ◽  
Kaisheng Zhang
Keyword(s):  
Kalman Filtering ◽  
Negative Impact ◽  
Data Sources ◽  
Fusion Algorithm ◽  
Precision Data ◽  
Weighted Fusion ◽  
Fusion Data ◽  
Algorithm Stability ◽  
Self Adaptive ◽  
Improved Algorithm

When the multisensor self-adaptive weighted fusion algorithm fuses the data sources that were severely interfered by noise, its fusion precision, data smoothness, and algorithm stability will be reduced. To overcome this drawback, the idea was proposed with respect to an improved algorithm which optimized acquisition of fusion data sources with discrete Kalman filtering technique, thus reducing the negative impact on the fusion performance from noise. To verify the effectiveness of the improved algorithm, this paper simulated the fusion process of soil moisture data with fusion samples. The result proved that, under the same circumstance, the improved algorithm has a stronger restrain ability to noise and a better performance in fusion precision, data smoothness, and algorithm stability compared with the general algorithm.

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