Practical Indoor Localization Based on Soft Information Cross-Correlation in Wireless Sensor Networks

Accurate indoor target localization in Wireless Sen-sor Networks (WSNs) is challenging due to the difficulty in detecting weak direct path (DP) of channel impulse response (CIR). The current hard decision (HD) method only considers the energy of a single path, so its performance deteriorates in scenarios where weak DP is prevalent. Although the existing soft information decision (SID) method takes into account the physical structure of the DP, it relies on an accurate statistical model. To detect weak DP more accurately in practical scenarios, we propose a SID method based on scoring models (SIDSM). This method first models the signal-present and noise-only scores. The soft information (SI) of CIR is then extracted using the rule of thirds. The process of DP detection entails finding the maximum value of SI. To improve the accuracy of indoor localization, a localization method based on SI cross-correlation function (SICC) is developed. It establishes the cost function through the cross-correlation function of SI and then solves it through the exhaustive search method. The effectiveness of the methods has been verified by utilizing experimental data collected and processed via indoor WSNs. The results demonstrate that the DP detection performance of the SIDSM surpasses that of the HD method. Furthermore, the SICC method shows improved localization accuracy compared to conventional HD-based local-ization methods.