Device free localization (DFL) utilizing the received signal strength (RSS) variations on the wireless link caused by an object is the estimation of the object such as a person without carrying any electronic device. However, environment influences, such as temperature or swaying of sensor nodes, also alter RSS variations, thus degrading the positioning accuracy. A novel wavelet denoising algorithm based on subspace decomposition, combined with the fingerprint method, is proposed to reduce the environment impact. The noise characteristics of static environment are researched, then the maximum characteristic value is extracted as threshold, the signal features are adaptively decomposed into different orthogonal subspaces and the object signal is reconstructed in subspace. The feather extraction method is discussed after the mixed denoising analysis. Gaussian radial basis function is utilized to calculate the kernel distance between online measurement received signal strength and the fingerprint data to estimate the target location coordinate. Simulation results indicate that the proposed algorithm can achieve better positioning accuracy than the traditional location algorithm.