For the practical application requirements of location-based services, a sparse fingerprint localization method based on spatial position constraint is proposed, after fully analyzing the limitations of the existing indoor location technologies. The positioning information from inertial navigation system (INS) and wireless local area network (WLAN) are deeply integrated on the data level, to coordinate the positioning task. Based on the received signal strength (RSS) data provided by WLAN, the spatial-location-fingerprint database is constructed, together with the sparse fingerprint representation and location model. In view of the RSS variability due to environmental interferences, the displacement state can be preliminarily estimated by INS, which will be as a constraint condition to construct the sparse fingerprint location model based on spatial position constraint. The simulation experimental results show that the positioning accuracy of this method is improved by 58% and 33% respectively, compared with the INS and sparse fingerprint methods. It is demonstrated that the proposed model can appropriately compensate the accumulative error of INS, and the motion prediction by INS also can restrict the jumping and distortion effects of RSS signals to a certain extent.