Graph-Based Multi-Bounce Modeling and Channel Parameter Estimation for Indoor Sensing

Abstract

Indoor sensing is challenging because of the multi-bounce effect, spherical wavefront, and spatial nonstationarity (SNS) of the near-field effect. This paper addresses radio-based environment sensing considering these issues. Specifically, graph theory (GT) is used to model the multi-bounce propagation of the near field. In this manner, indoor reflectors/scatterers are modeled as vertices in a propagation graph, the multi-bounce paths are modeled by the edges linking the vertices. Besides, the coupled multipath parameters in the near field, i.e., range and angles, are denoted directly by the coordinates of vertices. Then, the space-alternating generalized expectation-maximization (SAGE) algorithm is adapted to the proposed GT-based dictionary-aided multi-bounce SAGE (GM-SAGE), where the searching parameters including range and angle of departure/arrival (AoD/AoA) are transformed to the coordinates of vertices in the graph. To accelerate the two-bounce sensing, a recursive strategy is proposed. Furthermore, geometric information-based radio-sensing ambiguities are analyzed. The proposed algorithm is validated through a synthetic scattering channel and realistic ray tracing (RT) in a complex indoor office. The results demonstrate that the proposed GM-SAGE can deal with multi-bounce channels, where the one-bounce paths and near-field two-bounce paths are used to reconstruct the scatterers in the environment, the residual higher-bounce paths are identified and hence avoid ghost/mirror detection. Numerical simulations also show the influence of signal-noise ratio (SNR), grid size of vertices, aperture size, and near-far-field effects.