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 are 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.
With the increasing popularity of substation inspection robots, the localization and detection of outdoor inspection pictures gradually becomes the focus and difficulty of research. For outdoor substation meters, in different weather conditions, for the current stage of detection algorithm model complexity is high, easy to the detection of the target caused by low recognition rate, is not conducive to the rapid deployment of practical applications and other problems, proposed an improved YOLOV5-based substation detection algorithm, the algorithm uses YOLOv5 + CBAM attention mechanism module to detect meter dials, for different weather conditions The algorithm uses YOLOv5+CBAM attention mechanism module to detect meter dials, which is of great significance to identify meter dials accurately and quickly under different weather conditions and further realize intelligent inspection of substations.
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