To address the problems of fingerprint information redundancy, difficulty in spatial boundary division, and lack of accuracy in acquiring RP sets in indoor WiFi localization, we propose an indoor localization method based on dual-metric coordination of signal fingerprint measurement. The low-dimensional fingerprint information is simplified and formed through the fusion of fingerprint matrix under S-metric and European-metric. The correlation degree between fingerprints is considered through the “point-class” correlation degree and “class-class” similarity, taking into account the controllability of the number of new fingerprints on the subregion boundary, and the adjustment mechanism of the fuzzy depth of the subregion boundary is established to form the boundary ambiguity generalization ability. Expansion of the fingerprint database is accomplished by the interpolation method of regional sparsity determination, so as to construct a high-density offline fingerprint database. In the preferred subregion, combining the signal space and the location space, the difference degree of the two kinds of measurements is compared to realize the targeted screening of high-value fingerprint points, and reduce the error influence of online fingerprint matching set. In the global experimental scene, the partition results are regular and orderly, which accords with the actual space structure. The construction effect of fingerprint database is improved by at least 11% compared with other schemes, and the positioning accuracy is improved by more than 12% compared with the same type of algorithm. The proposed scheme has significant positioning accuracy advantages, and has better scene adaptability in complex indoor environments with high disturbance characteristics.