Details

Autor(en) / Beteiligte

• Liu, Yahui
• Fan, Xiaoqian
• Lv, Chen
• Wu, Jian
• Li, Liang
• Ding, Dawei

Titel

An innovative information fusion method with adaptive Kalman filter for integrated INS/GPS navigation of autonomous vehicles

Ist Teil von

• Mechanical systems and signal processing, 2018-02, Vol.100, p.605-616

Ort / Verlag

Berlin: Elsevier Ltd

Erscheinungsjahr

2018

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• •An information fusion method based on Adaptive Kalman Filter is proposed for integrated INS/GPS navigation.•The proposed Adaptive Kalman Filter with an attenuation factor can restrain the measurement noise and processes noise.•The precision of navigation by proposed method is improved and much more accurate. Information fusion method of INS/GPS navigation system based on filtering technology is a research focus at present. In order to improve the precision of navigation information, a navigation technology based on Adaptive Kalman Filter with attenuation factor is proposed to restrain noise in this paper. The algorithm continuously updates the measurement noise variance and processes noise variance of the system by collecting the estimated and measured values, and this method can suppress white noise. Because a measured value closer to the current time would more accurately reflect the characteristics of the noise, an attenuation factor is introduced to increase the weight of the current value, in order to deal with the noise variance caused by environment disturbance. To validate the effectiveness of the proposed algorithm, a series of road tests are carried out in urban environment. The GPS and IMU data of the experiments were collected and processed by dSPACE and MATLAB/Simulink. Based on the test results, the accuracy of the proposed algorithm is 20% higher than that of a traditional Adaptive Kalman Filter. It also shows that the precision of the integrated navigation can be improved due to the reduction of the influence of environment noise.