NLOS-Identified RSS Localization via Enzyme Action Optimization

With the continuous advancement of the 6th generation (6G) communication technology, the demand for locationbased services (LBS) has become critical. As a mainstream technique in wireless localization, received signal strength (RSS) localization has received significant attention. However, most existing RSS-based methods cannot obtain effective positioning in non-line-of-sight (NLOS) environments due to the multipath effect. In this paper, a new NLOS-identified RSS positioning method via Enzyme Action Optimization (EAO) is proposed. We first derive a signal detection theory-based NLOS identification criterion by leveraging multiple RSS measurements. Depending on the prior probabilities information, the Generalized likelihood ratio test (GLRT) and Neyman-Pearson (NP) criterion are respectively adopted to derive the decision criterion. Then, we propose an EAO-based RSS (EAO-RSS) positioning algorithm. The algorithm mimics the binding and catalytic processes of enzymes with substrates to dynamically adjust candidate estimates in search of the global optimum. As a result, accurate RSS-based localization can be achieved under NLOS condition. Simulation results show that the proposed method can improve positioning accuracy by more than 3.85% compared with the state-of-the-art.