弹道学报2025，Vol.37Issue(3)：16-24,9.DOI:10.12115/ddxb.2024.06003
基于ULCA融合模型的旋转弹姿态估计算法研究
Research on Attitude Estimation Algorithm for Spinning Projectile Based on ULCA Fusion Model
摘要
Abstract
To address the critical challenges of insufficient accuracy,noise sensitivity and poor environmental adaptability in low-cost micro inertial-measurement-unit(MIMU)for spinning projectile attitude-estimation,a novel fusion model named UKF-LSTM-CNN-Attention(ULCA)was proposed.The ULCA model integrates unscented Kalman filtering(UKF),long short-term memory networks,convolutional neural networks(CNN)and attention mechanisms to achieve multimodal information fusion,significantly improving attitude estimation accuracy and robustness under complex conditions.In the methodology,UKF was first employed to handle the nonlinear system dynamics,ensuring baseline estimation precision.LSTM was then introduced to capture temporal dependencies in attitude variations,while CNN extracted spatial-local features from sensor data.Finally,the attention mechanism adaptively weighted critical information to suppress noise interference effectively.To comprehensively evaluate the algorithm,systematic simulations were conducted under various operational conditions,comparing ULCA with traditional UKF and extended Kalman filter(EKF).Results demonstrate that the ULCA model reduces the average estimation error by 59.89%in roll,pitch and yaw angle estimation compared to traditional algorithms,exhibiting superior robustness in high-noise environments.Theoretical analysis and experimental validation show that the ULCA fusion model effectively addresses modeling biases of conventional filters in complex scenarios,achieving remarkable progress in precision and adaptability for attitude estimation.关键词
旋转弹丸/姿态角估计/无迹卡尔曼滤波/长短期记忆网络/融合模型Key words
spinning projectile/attitude angle estimation/unscented Kalman filter(UKF)/long short-term memory network/fusion model分类
军事科技引用本文复制引用
阿怀伟,傅健,王良明,冯德伟..基于ULCA融合模型的旋转弹姿态估计算法研究[J].弹道学报,2025,37(3):16-24,9.基金项目
国家自然科学基金(61903241) （61903241）
