Abstract: Infrared camera array utilizes synthetic aperture technology to significantly enhance the detection capability of spatial weak targets, which holds significant application value for the identification of infrared weak small targets. However, the key to realizing multiview synthetic aperture technology lies in obtaining high-precision camera calibration parameters. The currently widely used camera calibration techniques suffer from insufficient accuracy, which limits the application range of the infrared camera array. To address this issue, this paper proposes an improved sparrow search algorithm based on Tent chaotic mapping and differential variation strategy for optimizing the internal parameters of infrared array cameras. The method first uses Zhang’ s camera calibration method to obtain the initial values of the internal parameters of the infrared camera array and then aims to minimize the average reprojection error using the improved sparrow search algorithm to further optimize the internal parameters. Experimental results indicate that applying Tent chaotic mapping to population initialization, combined with differential variation strategy, can significantly improve the search efficiency of the sparrow search algorithm, enabling it to maintain global search capability while performing local fine search when optimizing internal parameters, thereby accelerating convergence. Furthermore, the optimized internal parameters, compared to traditional calibration methods, exhibit higher precision and better repeatability.