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This article develops several centralized and collective neurodynamic approaches for sparse signal reconstruction by solving the $L_{1}$ -minimization problem. First, two centralized neurodynamic approaches are designed based on the augmented Lagrange method and the Lagrange method with derivative feedback and projection operator. Then, the optimality and global convergence of them are derived. In addition, considering that the collective neurodynamic approaches have the function of information protection and distributed information processing, first, under mild conditions, we transform the $L_{1}$ -minimization problem into two network optimization problems. Later, two collective neurodynamic approaches based on the above centralized neurodynamic approaches and multiagent consensus theory are proposed to address the obtained network optimization problems. As far as we know, this is the first attempt to use the collective neurodynamic approaches to deal with the $L_{1}$ -minimization problem in a distributed manner. Finally, several comparative experiments on sparse signal and image reconstruction demonstrate that our proposed centralized and collective neurodynamic approaches are efficient and effective. [ABSTRACT FROM AUTHOR]

A new star map identification algorithm based on approximate string matching is proposed. First the guide star database is built with pattern string, then the improved BPM algorithm is used to find the matches between measured stars and guide stars, and the correctness of the matching results is validated. The simulation results show that the algorithm is simple with small guide star database and has good robustness. [ABSTRACT FROM AUTHOR]