Advanced wireless communication systems combined with wireless sensing are being developed as a key technology toward Beyond 5G and 6G networks. Such future communication networks are expected to offer additional capabilities that enable new applications, such as object detection and localization using radio signals. The basic concept of object detection using radio signals is to track the fluctuations in the radio channel which are influenced by the movements and presence of target objects, e.g., channel state information (CSI) is useful to estimate the target's behavior and presence. As described in this paper, we present our recently developed wireless local area network (WLAN)-based device-free indoor localization scheme with distributed antennas and experimentally assess its achievable performance in indoor scenarios. For this approach, feedback beamforming weights in WLAN systems are used as feature information for machine-learning-based algorithms. Experiment results show that our proposed algorithm, implemented in an IEEE 802.11ac-based WLAN, works well in indoor environments. We also discuss how much performance improvement can be expected when the CSI is given properly. Based on these outcomes, we explore the applicability and effective range of the proposed systems in an indoor environment.