A target localisation method with monostatic radar via multi‐observation data association. Issue 1 (22nd September 2022)

• Record Type: Journal Article
• Authors: Ding, Rui; Wang, Zhuang; Jiang, Libing; Liu, Zhang; Zheng, Shuyu
• Is Part Of: IET radar, sonar & navigation. Volume 17:Issue 1(2023)
• Page Start: 99
• Page End: 116
• Publication Date: 2022-09-22
• Subjects: multi‐observation data association -- multipath exploitation -- non‐line‐of‐sight (NLOS) -- target localisation -- time‐of‐arrival (TOA) estimation; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/rsn2.12327
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract: Traditionally, radar‐based localisation systems are designed to deal with targets in line‐of‐sight (LOS) scenarios. However, the performance of those radar systems is limited by multipath echoes reflected from non‐line‐of‐sight (NLOS) targets and walls in the urban areas. In recent years, there has been an increasing interest in multipath exploitation methods aided by urban environmental knowledge. Based on the exploitation of space diversity provided by the multipath effect, this article proposes a novel two‐stage localisation method using a single monostatic radar via association of multipath time‐of‐arrivals (TOAs). In the offline stage, reference TOA data at different locations is generated by a ray‐tracing method to avoid the heterogeneity of multipath propagation at different locations, assuming that the environment information is completely known prior. In the online stage, the TOA set of multipath echoes from the target is estimated by a sparse reconstruction method to achieve super‐resolution rather than the matched filter. Then based on the finite‐set statistics (FISST) theory, a grid‐based multi‐observation data association (GMODA) algorithm is present as the likelihood function of the multipath measurement given a candidate location, which is followed by a maximum likelihood estimation to derive the location of the target. Furthermore, the global nearest neighbour approximation of GMODA is introduced to avoid combinatorial explosion in the association algorithm. Simulation and experiment results on a single antenna millimetre‐wave radar in various scenarios show that the proposed method achieves high localisation accuracy in both LOS and NLOS conditions while maintaining low computational cost at the same time. The presented results suggest that our method can be applied to target localisation applications in the complex urban environment.