基于双旋迫击炮弹平台的改进型Sage-Husa自适应滤波滚转角测量算法

针对传统扩展卡尔曼滤波算法无法消除量测噪声、导致二维弹道修正引信滚转角测量误差偏大问题，利用轴向陀螺的实测转速作为系统噪声补偿，提出一种基于双旋迫击炮弹平台的改进型Sage-Husa自适应卡尔曼滤波滚转角测量算法。相对于传统扩展卡尔曼滤波方法，新算法显著降低了滚转角测量误差，提高了滤波器对于弹道升弧段和降弧段滚转角新息改变的自适应性；以高精度三轴转台作为硬件实验验证平台，该算法始终能够保持较高的测量精度和自适应性。研究结果表明：改进后的Sage-Husa自适应卡尔曼滤波滚转角测量算法在仿真验证中的滚转角测量误差均值为0.26°、标准差为0.35°；硬件实验验证中的滚转角测量绝对误差不超过4.2°.

Roll Angle Estimation Algorithm with Improved Sage-Husa Adaptive Kalman Filtering Based on Dual-spin Mortar Shell

Traditional extended Kalman filter (EKF) algorithm is unable to eliminate the bigger measured roll angle error caused by irresistible measuring noise. A roll angle estimation algorithm with improved Sage-Husa adaptive Kalman filtering (SHAKF) based on dual-spin mortar shell is proposed, in which the measured rotation speed of gyro is used as compensation for system noise. Compared with traditional EKF algorithm, the proposed algorithm is used to reduce the measuring error of roll angle significantly, and improve the adaptivity of filter to the innovation changing of roll angles in the upward and downward legs of ballistic trajectory. The new proposed algorithm is verified by in-lab testing with MEMS three-axis turntable. The research results indicate that the mean value and standard deviation of measuring errors are 0.26 degs and 0.35 degs, respectively, in simulating verification, and the in-lab testing results show that the estimation error is always less than 4.2 degs in dynamic range changing from 30 r/s to 1 r/s.