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针对陀螺稳定平台的漂移问题,建立了陀螺稳定跟踪装置在不同工作模式下陀螺漂移的数学模型,指出稳定模式下包含常值漂移和相关漂移的陀螺低频噪声是影响稳定精度的主要原因。提出一种自适应实时估计算法,采用卡尔曼滤波框架和滤波器收敛判据,结合Sage-Husa滤波和加权Sage-Husa滤波算法,利用跟踪器跟踪静止目标时输出的脱靶量信号对陀螺常值漂移和相关漂移进行估计。实验结果表明:该算法能够在系统模型和噪声特性均不准确的情况下使用,收敛时间小于3s,估计均方差小于0.02(°)/s,具有良好的鲁棒性和自适应能力。
Aiming at the problem of drift of gyro stabilized platform, a mathematical model of gyro drift of gyro stabilized tracking device under different working modes is established. It is pointed out that the gyro low frequency noise with steady-state drift and related drift in steady mode is the main reason that affects the stability accuracy. An adaptive real-time estimation algorithm is proposed. The Kalman filtering framework and the convergence criterion of filter are combined with the Sage-Husa filtering and weighted Sage-Husa filtering algorithm. When the tracking error signal is used to track the stationary target, Drift and related drift are estimated. Experimental results show that the proposed algorithm can be used with inaccurate system models and noise characteristics. The convergence time is less than 3s and the estimated mean square deviation is less than 0.02 (°) / s. It has good robustness and self-adaptability.