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针对传统捷联惯导系统静基座初始对准模型的维数较高,导致滤波算法的解算实时性较差的问题,设计出一种基于鱼群优化粒子滤波的两位置初始对准方法。首先,建立了捷联惯导系统的两位置初始对准模型。由于该模型中不存在惯性器件的随机常值影响,因此,在确保初始对准精度的前提下,有效降低了初始对准模型维数;然后,利用鱼群优化算法改善了粒子滤波算法中粒子样本的分布,提高了粒子滤波算法的收敛速度和预测精度。仿真结果验证,采用该初始对准方法,可以有效提高初始对准的精度,且满足系统对实时性的要求。
In order to solve the problem of poor real-time performance of the filtering algorithm, aiming at the problem that the initial alignment model of the traditional SINS static alignment has a high dimensionality, a two-position initial alignment method based on fish-optimized particle filter . Firstly, a two-position initial alignment model of SINS is established. Because there is no stochastic constant value of the inertial device in this model, the dimension of the initial alignment model is effectively reduced under the premise of ensuring the initial alignment accuracy. Then, the particle swarm optimization algorithm is used to improve the particle size The distribution of samples improves the convergence speed and prediction accuracy of the particle filter algorithm. Simulation results verify that the initial alignment method can effectively improve the accuracy of the initial alignment and meet the requirements of real-time system.