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针对传统自适应粒子滤波算法的计算负荷太大问题,在Fox的K-L距离采样的基础上,给出一种新的求解k值的方法,将k值的计算从采样过程中分离出来,大大降低算法的复杂度,减少计算量,避免死循环发生。最后,将该算法应用到大失准角情况下捷联惯导系统动基座初始对准中,并与扩展卡尔曼滤波算法、标准粒子滤波算法和传统自适应粒子滤波算法进行了比较,仿真结果表明简化的自适应粒子滤波算法在保持高精度的同时,有效地提高了算法的计算速度,因此更适合于捷联惯导系统动基座初始对准。
Aiming at the problem of large computational load of traditional adaptive particle filter algorithm, a new method for solving k value is given based on Fox's KL distance sampling. The k-value calculation is separated from the sampling process and greatly reduced The complexity of the algorithm reduces the amount of computation and avoids the occurrence of dead loops. Finally, the algorithm is applied to the initial alignment of SINS with large misalignment angle, and compared with the extended Kalman filter, the standard particle filter and the traditional adaptive particle filter, and the simulation The results show that the simplified adaptive particle filter algorithm can effectively improve the calculation speed of the algorithm while maintaining high precision, and is therefore more suitable for the initial alignment of the strapdown inertial navigation system.