In response to the issues of misjudgment of location area attribution and interference from outliers in fingerprint-based positioning of a large number of access points scenes, a multi-scale signal fusion indoor positioning algorithm is proposed, which incorporates dual-source signals. During the fingerprint online positioning phase, the spatiotemporal information of PDR signals is utilized to expand the number of reference points belonging to the location area, thereby alleviating the negative effects caused by misclassification in neighboring areas. Additionally, multiple distances and chi-square distances are used instead of the traditional Euclidean distance, in combination with spatial domain physical distance scales, to implement nearest neighbor selection at multiple scales. In this way, the interference from outliers is overcome effectively. We introduce a dynamic adaptation of the K value. Based on this, the dynamic linked fusion between Wi-Fi and PDR pre-positioning is established, which further enhances the accuracy of the positioning algorithm. Experimental results show that, under the same conditions of introducing dual-source signals, the proposed method exhibits superior overall performance compared to other multi-scale or dynamic K-value algorithms, with an average positioning accuracy surpassing other algorithms by 6. 6% to 23. 1% .