An optimization algorithm of baseline density distribution for an ultra-long wave astronomical observation array

Low frequency exploration is becoming a research hotspot. It is possible to explore the dark ages of the universe by obtaining high-resolution sky maps from low-frequency observations. On March 1, 2018, the Ultra Long Wave Astronomical Observation Array Background Model Project was proposed. A linear formation composed of 1 mother satellite and 5~8 daughter satellite, to carry out observations on the ground behind the lunar orbit. Directed line segments formed between satellites and satellites are called baselines. When satellite formations move around the moon, sampling points from trajectories formed by different baselines will obtain a baseline density distribution map. This paper proposes a baseline density distribution optimization algorithm based on particle swarm. The algorithm continuously adjusts the initial deployment position of the satellite formation and optimizes the baseline distribution to conform to the image spectral distribution law. The content of this thesis mainly includes three parts: firstly determine the evaluation index, use dynamic time warping algorithm and other different standards to conduct experimental research; then, based on the particle swarm optimization algorithm, conduct solution search in six-dimensional space, repeatedly adjust parameters, and find Optimal solution; finally check the effect and interpretability of the algorithm, we analyze the peak signal-to-noise ratio of the image, the distribution of brightness temperature particles and the root mean square error of the inverted image and the original sky map. Experiments show that the optimal solution obtained by the algorithm makes the baseline density distribution conform to the image spectral distribution law, and the inversion image quality is better, which is of great significance for the ultra-long wave astronomical observation array plan.