Split-Direction Adaptive Fingerprinting for Public FM Radio Signals Aided by PDR Based on Performance Analysis

The demand for indoor location services is growing rapidly. Various indoor positioning techniques have been proposed. As a supplement, public FM radio signal-based methods can provide a possible solution for infrastructure-free indoor positioning which benefits from its wide coverage. However, existing research mainly focuses on algorithm development, with a notable absence of theoretical performance analysis. To bridge this gap, this article first analyzed the FM signal indoor positioning performance by deriving the Fisher information. Our findings indicate that: FM received signal strength (RSS) theoretically has difficulties in 2-D indoor positioning and exhibits directional differences in 1-D performance. Thus, when both directions are used for localization simultaneously, the direction with blurred features will significantly affect the overall accuracy of 2-D positioning. Besides, there exists a problem of finding effective FM positioning region in large-scale scenes. To address these issues: First, the method for finding positioning region within effective FM signal environmental field with pedestrian dead reckoning (PDR) aided is designed. Second, focusing on the problem of 2-D positioning, a new strategy is suggested that takes the 1-D characteristics of FM RSS by employing the split-direction accuracy estimation and parameter adjustment. To begin with, three improved lightweight accuracy estimation indicators are adopted to assess the FM signal positioning performance. Then, the splitdirection adaptive FM fingerprinting method is proposed, which integrates the FM RSS signal and PDR-constrained space metrics by using the accuracy estimation results in different directions. Practical experiments are conducted in three large-scale scenes, and the results demonstrated an improvement of 86.6% and 33.4% compared to traditional 2-D fingerprinting and fusion methods, respectively.