Application of Interval Kalman Filtering Algorithm in Integrated Navigation Data Fusion

2012 ◽  
Vol 433-440 ◽  
pp. 4059-4064
Author(s):  
Yun Feng Ma
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
Interval Matrix ◽  
Uncertain Parameters ◽  
Integrated Navigation ◽  
System Matrix ◽  
Inversion Algorithm ◽  
Filtering Algorithm ◽  
Kalman Filtering Algorithm ◽  
Time Accuracy

The traditional Kalman filter cannot be used directly when some system parameters such as certain elements of the system matrix are not precisely known or gradually change with time. Some uncertain parameters can be described as an interval model. An interval Kalman filtering algorithm is studied in this paper, which can be used to process a system with uncertain parameters. A simple inversion algorithm of interval matrix has been applied and its statistic performances and iterative form are similar to those of traditional Kalman filter. Simulation results show that such filtering algorithm can provide the real time accuracy error estimation and can be applied to such kind of low-cost integrated navigation system.

An Application of the Kalman Filter in a Laser Tracking Measurement System

2005 ◽  
Vol 295-296 ◽  
pp. 245-252
Author(s):  
X.R. Chen ◽  
P. Cai ◽  
Wen Ku Shi
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
Measurement System ◽  
Tracking System ◽  
Measuring System ◽  
Filter Method ◽  
Laser Tracking ◽  
Filtering Algorithm ◽  
Kalman Filtering Algorithm ◽  
Tracking Measurement

Flexible coordinate measuring system based on laser tracking measurement system (LTS) is an effective method to detect 3D coordinates of moving target. However, the system suffers from various interferences resulting in low accuracy. This paper extends the application of a Kalman filtering algorithm in LTS to solve the problem. The laser tracking system is introduced. The state model of the laser tracking measurement system is developed and the linearization method of the model is analyzed. A Kalman filtering algorithm is used for the system. The result of the simulation shows that the proposed Kalman filter method works well for the improvement of accuracy of LTS.

scholarly journals Estimation of Sideslip Angle Based on Extended Kalman Filter

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Yupeng Huang ◽  
Chunjiang Bao ◽  
Jian Wu ◽  
Yan Ma
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
Test Field ◽  
Extended Kalman Filtering ◽  
Sideslip Angle ◽  
Filtering Algorithm ◽  
Limit Test ◽  
Kalman Filtering Algorithm

The sideslip angle plays an extremely important role in vehicle stability control, but the sideslip angle in production car cannot be obtained from sensor directly in consideration of the cost of the sensor; it is essential to estimate the sideslip angle indirectly by means of other vehicle motion parameters; therefore, an estimation algorithm with real-time performance and accuracy is critical. Traditional estimation method based on Kalman filter algorithm is correct in vehicle linear control area; however, on low adhesion road, vehicles have obvious nonlinear characteristics. In this paper, extended Kalman filtering algorithm had been put forward in consideration of the nonlinear characteristic of the tire and was verified by the Carsim and Simulink joint simulation, such as the simulation on the wet cement road and the ice and snow road with double lane change. To test and verify the effect of extended Kalman filtering estimation algorithm, the real vehicle test was carried out on the limit test field. The experimental results show that the accuracy of vehicle sideslip angle acquired by extended Kalman filtering algorithm is obviously higher than that acquired by Kalman filtering in the area of the nonlinearity.

Simplified Federated Filtering Algorithm With Different States in Local Filters

2010 ◽  
Vol 133 (1) ◽  
Author(s):  
Guoliang Liu ◽  
Jian Xie ◽  
Shizuo Yan ◽  
Wenyi Qiang
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Transition Matrices ◽  
Filtering Algorithm ◽  
Federated Kalman Filter ◽  
Federated Filtering ◽  
Local Filters ◽  
Computation Load

In this paper, to reduce the computation load of federated Kalman filters, a simplified federated filtering algorithm for integrated navigation systems is presented. It has been known that the per-cycle computation load grows roughly in proportion to the number of states and measurements for a single centralized Kalman filter. Hence, the states that have poor estimation accuracies are removed from local filters, so that the per-cycle computation load is reduced accordingly. Local filters and master filter of the federated Kalman filter may have different states, so the transition matrices are required to combine the outputs from the local filters and the master filter properly and to reset the global solution into the local filters and the master filter correctly. An experiment demonstrates that the proposed algorithm effectively reduces the computation load, compared with the standard federated Kalman filtering algorithm.

A Measurement-Based Robust Adaptive Kalman Filtering Algorithm

2012 ◽  
Vol 433-440 ◽  
pp. 3773-3779 ◽  
Author(s):  
Yan Hong Chang ◽  
Hai Zhang ◽  
Qi Fan Zhou
Keyword(s):  
Kalman Filtering ◽  
Measurement Noise ◽  
Statistical Characteristics ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Measurement Systems ◽  
Filtering Algorithm ◽  
Gps Measurement ◽  
Kalman Filtering Algorithm ◽  
Robust Adaptive

In the case that the accuracy of standard kalman filter (SKF) declines when the noise statistical characteristics are unknown or changing, a measurement-based adaptive kalman filtering algorithm (MAKF) is presented. Based on the contrastive analysis of measurement characteristics of different measurement systems, MAKF is put forward to estimate adaptively the measurement noise variance R by co-difference measurement sequences. Simulation is performed by applying this algorithm to the GPS/INS integrated navigation system, the results show that MAKF can track the GPS measurement noise in real time on condition that the GPS measurement noise is unknown or changing, and the filtering accuracy and robustness are superior to those of SKF and an improved Sage-Husa adaptive kalman filtering algorithm.

Sign in / Sign up

Export Citation Format

Share Document