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 paper firstly analyzed the FM signal indoor positioning performance by deriving the Fisher information. Our findings indicate that: FM RSS theoretically has difficulties in two-dimensional (2D) indoor positioning and exhibits directional differences in one-dimensional (1D) performance. Thus, when both directions are used for localization simultaneously, the direction with blurred features will significantly affect the overall accuracy of 2D 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 PDR aided is designed. Second, focusing on the problem of 2D positioning, a new strategy is suggested that takes the 1D 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 split-direction 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 2D fingerprinting and fusion methods, respectively.