Low-earth-orbit(LEO) satellite can ensure the acquisition of external timing and positioning for UAVs in Global Navigation Satellite System(GNSS) -denied environments. This paper proposes a multi-source information fusion positioning algorithm within the framework of Riemannian Information Geometry, which based on LEO Instantaneous Doppler positioning and the inertial navigation sensors(INS) carried by drones. The algorithm derives the covariance propagation process of LEO Doppler positioning based on the Newton-LS method, completes the modeling of probability distribution functions, and maps the probability distribution functions of heterogeneous sensors to Riemannian space to unify the navigation source information format. Utilizing the more precise information granularity of Riemannian space, the proposed algorithm solved the multi-sensor information fusion positioning problem through convex optimization. The accuracy of LEO positioning in GNSS-denied environments has been effectively improved. Compared with single LEO Instantaneous Doppler positioning and INS, the positioning accuracy has been improved by 41.9% and 32.34%, respectively.