Channel State Information Based Indoor Localization Error Bound Leveraging Pedestrian Random Motion

Indoor pedestrian motion detection based on the Wi-Fi Received Signal Strength (RSS) has been commonly deployed in recent years. However, the Channel State Information (CSI) based indoor localization methods can be selected to achieve higher localization accuracy since it contains the finer-grained physical-layer information of the signal. Lack of theoretical analysis of the CSI-based error bound that leverages the pedestrian motion posses a challenge to investigate the ideal performance. In this circumstance, this paper proposes the Cramer-Rao Lower Bound (CRLB) concept to derive out the indoor localization error bound leveraging the pedestrian motion that depends on the constructed signal propagation model by considering the relationship between the localization accuracy and the path loss, shadow fading, and multipath effect. Through the experimental comparison, this paper analyzes the difference between the actual localization error and the derived localization error bound, and the impact of different experimental parameters on the localization performance is analyzed, as well as discusses the influence of the asynchronous effect between the transmitter and the receiver on the performance of the proposed localization error bound. The experimental results show that the derived error bound has the same trend as the actual error, which validate our theoretical analysis.