Kalman Filter Slope Measurement Method Based on Improved Genetic Algorithm- Back Propagation 2020-01-0897
How to improve the measurement accuracy of road grade is the key content of the research on the speed warning of commercial vehicles in mountainous roads. If there is a large measurement error, the obtained speed threshold will be biased, posing a safety hazard. Conventional measuring instruments such as accelerometers and gyroscopes generally have noise fluctuation interference or time accumulation error, resulting in large measurement errors. In response to this situation, the Kalman filter method is often used for filtering to reduce the interference of unwanted signals, thereby improving the accuracy of the slope measurement. However, the Kalman filtering method is limited by the estimation error of various parameters, and the filtering effect is difficult to meet the project research requirements. In this paper, the acceleration of vehicle gravity, running speed and acceleration of parallel slope are used as auxiliary measurement parameters to improve the measurement method of mountain road slope. Based on the Kalman model, genetic algorithm (GA) and BP neural network are used to carry out the innovation , covariance matrix and the previous Kalman numerical value optimization, improve the filtering effect, and use Matlab simulation and real vehicle test to carry out the plausibility test, the average absolute error can be within 0.2 degrees, to meet the accuracy requirements of the slope required for the speed warning of commercial vehicles in mountainous roads. It confirms the accuracy and reliability of the method. In addition, the improved filtering method also has great application value in the fields of navigation, detection, target tracking and other fields.
Haoyu Wang, Donghua Guo, Gangfeng Tan, Zhenyu Wang, Ming Li, Yifeng Jiang, Meng Ye, Kailang Chen