Initial alignment of compass based on genetic algorithm-particle swarm optimization

The rapidity and accuracy of the initial alignment influence the performance of the strapdown inertial navigation system (SINS), compass alignment is one of the most important methods for initial alignment. The selection of the parameters of the compass alignment loop directly affects the result of alignment. Nevertheless, the optimal parameters of the compass loop of different SINS are also different. Traditionally, the alignment parameters are determined by experience and trial-and-error, thus it cannot ensure that the parameters are optimal. In this paper, the Genetic Algorithm-Particle Swarm Optimization (GA-PSO) algorithm is proposed to optimize the compass alignment parameters so as to improve the performance of the initial alignment of strapdown gyrocompass. The experiment results showed that the GA-PSO algorithm can find out the optimal parameters of the compass alignment circuit quickly and accurately and proved the effectiveness of the proposed method.     

基于相位相关和自适应阈值的图像亚像素配准

针对图像的亚像素配准问题,提出了一种基于相位相关和自适应阈值技术来计算图像间平移参数的方法.先通过传统的相位相关技术得到图像间的整数平移参数,接着引入由整数平移参数自适应确定的阈值来消除相位相关矩阵的主导奇异向量中相应的高频部分,最后使用主导奇异向量剩下的部分来估计非整平移参数.实验结果证实了这一方法具有较高的配准精度和鲁棒性.