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Abstract: To address the limitations of the Extended Kalman Filter (EKF) in handling strongly nonlinear systems, such as low accuracy, significant errors in robotic motion estimation, weak anti-interference capabilities, and sub-optimal performance in vehicle motion estimation, this paper introduces a novel vehicle motion estimation method based on a Genetic Algorithm-Optimized Higher Order Extended Kalman Filter (GA-HEKF). The HEKF algorithm enhances traditional EKF by incorporating hidden variables to mitigate round-off errors and pseudo-linearizing the vehicle motion model. This approach establishes a linear relationship between system variables and hidden variables, transforming the system state model into a linear form and equivalently rewriting the observation model. As a result, the vehicle motion model is reformulated into a structure compatible with Kalman filtering, enabling more accurate state estimation. To further improve the performance of the HEKF algorithm, an improved genetic algorithm is employed to optimize the covariance matrices of system noise and observation noise during the high-order extended Kalman filtering process. This optimization enhances the precision of vehicle state parameter estimation by dynamically adjusting the noise characteristics to better reflect real-world conditions. Simulation experiments are conducted to validate the effectiveness of the proposed method. The results demonstrate that the GA-HEKF algorithm outperforms traditional EKF in terms of accuracy, robustness, and computational efficiency. Specifically, it exhibits superior performance in scenarios with high nonlinearity and noise interference, making it a promising solution for advanced vehicle motion estimation applications. The proposed method not only addresses the inherent limitations of EKF but also provides a framework for improving state estimation in other complex nonlinear systems.Abstract: To address the limitations of the Extended Kalman Filter (EKF) in handling strongly nonlinear systems, such as low accuracy, significant errors in robotic motion estimation, weak anti-interference capabilities, and sub-optimal performance in vehicle motion estimation, this paper introduces a novel vehicle motion estimation method based on a Genetic...Learn More
Abstract: In view of the current situation that there are many types of civil aviation interference sources and the interference identification algorithm is relatively scarce in the field of interference detection, an improved YOLOv7-ESC interference identification algorithm is proposed. Firstly, four common suppression interferences in civil aviation signals are modeled and an interference data set is constructed; secondly, continuous wavelet transform (CWT) is introduced as a time-frequency domain processing and analysis method to highlight the time-frequency feature information of the signal; then, the ECA attention mechanism, SE attention mechanism, and CBAM attention mechanism are integrated into the YOLOv7 backbone network to enhance the signal feature extraction capability; finally, a YOLOv7-ESC algorithm that integrates three attention mechanisms is studied to accurately classify and identify different interference signals. Experimental results show that compared with the traditional YOLOv7, the recognition accuracy (P) of the YOLOv7-ESC model increased from 0.930 to 0.986, an increase of 6.0%; the mean average precision (mAP) increased from 0.975 to 0.982, an increase of 0.7%; and the recall rate (R) increased from 0.965 to 0.989, an increase of 2.5%. The YOLOv7-ESC model has obvious advantages in interference identification and anti-interference capabilities, and has broad application prospects in the field of accurate investigation and identification of civil aviation interference sources.Abstract: In view of the current situation that there are many types of civil aviation interference sources and the interference identification algorithm is relatively scarce in the field of interference detection, an improved YOLOv7-ESC interference identification algorithm is proposed. Firstly, four common suppression interferences in civil aviation signal...Learn More
