In response to the close-range large-scale measurement requirements in on-orbit photogrammetry, we propose an optimal projection model identification and calibration method for large field-of-view photogrammetric cameras using starlight constraints. First, a piecewise starlight geometric projection model with an adjustable coefficient is formulated. Subsequently, a general multistation self-calibration bundle adjustment algorithm is developed for the piecewise starlight projection model. By combining the bundle adjustment algorithm with the northern goshawk optimization, we synchronously optimize the projection model adjustment coefficient, camera intrinsic and extrinsic parameters, and lens distortion coefficients. This optimization process continues until the star image points reprojection root mean squared error reaches the global minimum, resulting in the optimal projection model and its parameters. Experimental measurements show that, after calibrating the camera with a large field of view using starlight, the reprojection root mean squared error of star image coordinates is 1 / 9 pixel. In consecutive frame starlight calibration experiments, random errors in camera parameters using the Kalman filter are effectively eliminated. This method can identify the optimal projection model and calibrate all imaging parameters during the camera starlight calibration process, with the ability for consecutive frame calibration and parameter correction.