Unlike outdoor environments, indoor spaces exhibit complex electromagnetic conditions that complicate the localization of targets using wireless signals. Based on the Fresnel propagation model, this paper studies the propagation law of Wi-Fi signals in a rich multi-path environment. First, to address the phase offset in channel state information caused by hardware, a correction method based on least squares regression analysis is proposed, and environmental interference signals are eliminated by spectrum analysis. Second, the target position is estimated by using multiple groups of signal links, and the estimated result is matched with the reference position to reconstruct the target motion path. Experimental results show that the theoretical basis of this method is consistent with the actual movement patterns of the target, achieving a positioning error of about 11 cm without data training. At this error level, this method can distinguish adjacent targets in lateral or longitudinal proximity, thereby reducing misidentification. Compared with the existing deep learning-based methods, the proposed method reduces the positioning error by 50.4% with less training data, and by 5% with ample training data.

indoor localization  /  passive sensing  /  motion path reconstruction