Checking unscented information fusion algorithm for autonomous navigation vehicles

For the multisensor systems with unknown noise variances, by the statistics method, the mathematical model and the noise statistics are essential, and this limitation was settled by adaptive algorithm. The adaptive Kalman filter was proposed to solve the filtering problem of the system with unknown mathematical model or noise statistics in information fusion. Based on the probability method and the scalar weighting optimal information fusion criterion in the minimum variance sense, the algorithm can not only optimize the multi-channel data, but also obtain the minimum mean square error (MMSE) by introducing fusion equation, namely the algorithm is optimal under the sense of MMSE, and the error is the least than the original Kalman information fusion algorithm. The test result shows that the algorithm can precede information fusion effectively under the distributed acquisition system.

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Multi-sensor Information Fusion Method Based on BP Neural Network

International Journal of Online Engineering (iJOE) ◽

10.3991/ijoe.v12i05.5738 ◽

2016 ◽

Vol 12 (05) ◽

pp. 53 ◽

Cited By ~ 2

Author(s):

Lin Liandong

Keyword(s):

Neural Network ◽

Bayesian Inference ◽

Information Fusion ◽

System Development ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Fusion Algorithm ◽

Temperature Detection ◽

Posterior Expectation ◽

Sensor Information

This study aims to solve the problem of multi-sensor information fusion, which is a key issue in the multi-sensor system development. The main innovation of this study is to propose a novel multi-sensor information fusion algorithm based on back propagation neural network and Bayesian inference. In the proposed algorithm, a triple is defined to represent a probability space; thereafter, the Bayesian inference is used to estimate the posterior expectation. Finally, we construct a simulation environment to test the performance of the proposed algorithm. Experimental results demonstrate that the proposed algorithm can significantly enhance the accuracy of temperature detection after fusing the data obtained from different sensors.

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Autonomous navigation algorithm based on AUKF filter about fusion of geomagnetic and sunlight directions

International Journal of Intelligent Computing and Cybernetics ◽

10.1108/ijicc-07-2017-0087 ◽

2018 ◽

Vol 11 (4) ◽

pp. 471-485 ◽

Cited By ~ 1

Author(s):

Bing Hua ◽

Zhiwen Zhang ◽

Yunhua Wu ◽

Zhiming Chen

Keyword(s):

Orbit Determination ◽

Geomagnetic Field ◽

Autonomous Navigation ◽

Unscented Kalman Filter ◽

Satellite Orbit ◽

Field Vector ◽

Low Earth Orbit ◽

Fusion Algorithm ◽

Content Type ◽

Navigation Algorithm

Purpose The geomagnetic field vector is a function of the satellite’s position. The position and speed of the satellite can be determined by comparing the geomagnetic field vector measured by on board three-axis magnetometer with the standard value of the international geomagnetic field. The geomagnetic model has the disadvantages of uncertainty, low precision and long-term variability. Therefore, accuracy of autonomous navigation using the magnetometer is low. The purpose of this paper is to use the geomagnetic and sunlight information fusion algorithm to improve the orbit accuracy. Design/methodology/approach In this paper, an autonomous navigation method for low earth orbit satellite is studied by fusing geomagnetic and solar energy information. The algorithm selects the cosine value of the angle between the solar light vector and the geomagnetic vector, and the geomagnetic field intensity as observation. The Adaptive Unscented Kalman Filter (AUKF) filter is used to estimate the speed and position of the satellite, and the simulation research is carried out. This paper also made the same study using the UKF filter for comparison with the AUKF filter. Findings The algorithm of adding the sun direction vector information improves the positioning accuracy compared with the simple geomagnetic navigation, and the convergence and stability of the filter are better. The navigation error does not accumulate with time and has engineering application value. It also can be seen that AUKF filtering accuracy is better than UKF filtering accuracy. Research limitations/implications Geomagnetic navigation is greatly affected by the accuracy of magnetometer. This paper does not consider the spacecraft’s environmental interference with magnetic sensors. Practical implications Magnetometers and solar sensors are common sensors for micro-satellites. Near-Earth satellite orbit has abundant geomagnetic field resources. Therefore, the algorithm will have higher engineering significance in the practical application of low orbit micro-satellites orbit determination. Originality/value This paper introduces a satellite autonomous navigation algorithm. The AUKF geomagnetic filter algorithm using sunlight information can obviously improve the navigation accuracy and meet the basic requirements of low orbit small satellite orbit determination.

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