To improve the localization accuracy of the subspace data fusion （SDF） applied in distributed multi-array under the influence of unknown mutual coupling， we propose a reduced mutual coupling dimension subspace data fusion （RMCD-SDF） approach in this paper. Firstly， we introduce the mutual coupling error model into the SDF approach to make it adapt to the scenario where the antenna array is affected by the unknown mutual coupling error. Furthermore， in order to reduce the ultra-high computational complexity caused by searching all unknown parameters simultaneously， we introduce the reduced-dimension idea and construct the spectral function of RMCD-SDF. Simulation results show that the RMCD-SDF approach has advantageous localization performance when arrays are affected by unknown mutual coupling. The RMCD-SDF approach has similar computational complexity but higher localization accuracy than existing algorithms. When the signal-to-noise ratio （SNR） is 10 dB， the root means square error of the proposed approach is 8.67 dB lower than the classical SDF algorithm.