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

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.

Download Full-text

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

Key Engineering Materials - Measurement Technology and Intelligent Instruments VI ◽

10.4028/0-87849-977-6.245 ◽

2005 ◽

pp. 245-252

Author(s):

X.R. Chen ◽

P. Cai ◽

W.K. Shi

Keyword(s):

Kalman Filter ◽

Measurement System ◽

Laser Tracking ◽

Tracking Measurement

Download Full-text

Application of Interval Kalman Filtering Algorithm in Integrated Navigation Data Fusion

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.433-440.4059 ◽

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.

Download Full-text

Estimation of Sideslip Angle Based on Extended Kalman Filter

Journal of Electrical and Computer Engineering ◽

10.1155/2017/5301602 ◽

2017 ◽

Vol 2017 ◽

pp. 1-9 ◽

Cited By ~ 3

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.

Download Full-text

Transmission rate conditions for distributed filtering in sensor networks against eavesdropper

Transactions of the Institute of Measurement and Control ◽

10.1177/01423312211005607 ◽

2021 ◽

pp. 014233122110056

Author(s):

Bingya Zhao ◽

Ya Zhang

Keyword(s):

Sensor Networks ◽

Topological Entropy ◽

Kalman Filtering ◽

Transmission Rate ◽

Necessary Condition ◽

Estimation Errors ◽

Positive Role ◽

Filtering Algorithm ◽

Wiretap Channels ◽

Kalman Filtering Algorithm

This paper studies the distributed secure estimation problem of sensor networks (SNs) in the presence of eavesdroppers. In an SN, sensors communicate with each other through digital communication channels, and the eavesdropper overhears the messages transmitted by the sensors over fading wiretap channels. The increasing transmission rate plays a positive role in the detectability of the network while playing a negative role in the secrecy. Two types of SNs under two cooperative filtering algorithms are considered. For networks with collectively observable nodes and the Kalman filtering algorithm, by studying the topological entropy of sensing measurements, a sufficient condition of distributed detectability and secrecy, under which there exists a code–decode strategy such that the sensors’ estimation errors are bounded while the eavesdropper’s error grows unbounded, is given. For collectively observable SNs under the consensus Kalman filtering algorithm, by studying the topological entropy of the sensors’ covariance matrices, a necessary condition of distributed detectability and secrecy is provided. A simulation example is given to illustrate the results.

Download Full-text