NLOS Building Layout and Target Estimation in an L-Shaped Corner With Complex Geometries

Nonline-of-sight (NLOS) detection endows radar with the capability to sense visually obscured targets. However, existing algorithms rely on prior knowledge of the scene and assume that the layout is simple and regular, which restricts their applicability in practice. To address the above problems, this article proposes a method for target localization and building layout reconstruction in L-shaped corners with complex geometries. Specifically, the time of arrivals (ToAs) of multipath, containing information about the NLOS target and scenario, are extracted using the data preprocessing method. Next, the nearest-neighbor (NN) algorithm is utilized to track and correlate the extracted ToAs. Then, based on the least-squares (LS) method, the ToAs corresponding to diffraction, first-order reflection, and second-order reflection paths are identified or complemented. Among them, the first two serve for the target localization, while the ToAs of second-order reflection are applied to accomplish the building layout reconstruction. Eventually, simulations and experiments are carried out to corroborate that the proposed algorithm can effectively locate the NLOS target and reconstruct a portion of the shape of the wall just with a portable single-input single-output (SISO) radar.

I. Introduction

Radar is widely used in both civilian and defense applications as a sensor capable of acquiring information about the distances and speeds of targets [1], [2]. Typically, radar achieves target detection with the assistance of directly reflected echo. However, when the target is obstructed by obstacles, the traditional detection method based on line-of-sight (LOS) paths faces limitations or even ineffective. Remarkably, the multipath propagation of electromagnetic (EM) waves, such as diffraction and reflection, enables the radar to receive non- LOS (NLOS) regions. Therefore, NLOS detection breaks through the limitations of traditional LOS detection and holds important research and application value in the fields of intelligent driving and urban stability maintenance [3], [4], [5], [6], [7]