Single‐station radar‐based UAV position estimation in complex urban environments

Bi, Xiaoyan; Bai, Ding; Cui, Yongqiang

doi:10.1049/rsn2.12188

Cited by 2 publications

(3 citation statements)

References 11 publications

(20 reference statements)

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“…Some new techniques are emerging. 50–53,56 Collocation requirements are dealt with moderately and tend to become stronger in 20% of the papers surveyed. This is understandable since systems designers would like to work with a single sensor, if possible.…”

Section: The Survey and Measures For Analysismentioning

confidence: 99%

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A survey and analysis of drone detection systems using a systems approach superposed on scenarios

Kashi,

Prashanth,

Kashi

et al. 2023

Systems Engineering

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The proliferation of drones in civil and military applications has brought about new challenges in the context of safety and security considerations of operational aspects. The use of small‐ and medium‐sized drones in civilian airspaces even with regulatory approval may not provide guarantees of safety and security. Detection of drones is the first step towards providing a mitigating mechanism for challenges posed by mal‐intentioned drones. Sensors and their characteristics affecting the detection process are discussed. A figure of merit is proposed to compare systems employing these sensors. The key focus of the paper is the examination of the spectrum of drone detection systems (DDS) using a systems‐based approach applied to scenarios. In order to make an equitable comparison between systems, two scenarios are utilized to discuss system characteristics. Next, a survey of DDS in each of these scenarios is performed. Artifacts are analyzed, culling information using the systems model. An approach is proposed to perform evaluation of these artifacts using a scoring mechanism. Simple yet novel measures to assess the information content pertaining to DDS are presented. This approach enables researchers, system architects, system designers, solution providers, and system integrators to be aware of various challenges at the system and subsystem level. The proposed method helps to provide pointers to the literature where information about challenges and specific solutions can be obtained.

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Section: The Survey and Measures For Analysismentioning

confidence: 99%

“…Xiaoyan et al 51 . discuss a system and an approach where prior information on urban geography and multiple virtual synchronous radars are constructed from multipath detection enabled by a single radar.…”

Section: The Survey and Measures For Analysismentioning

confidence: 99%

A survey and analysis of drone detection systems using a systems approach superposed on scenarios

Kashi,

Prashanth,

Kashi

et al. 2023

Systems Engineering

View full text Add to dashboard Cite

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“…In 2021, Liu proposed a multi-target location method using NLOS signals reflected by targets based on consistency checking [8]. Bi from South-Central University for Nationalities proposed a single-station radar-based unmanned aerial vehicles position method in an urban canyon with known urban environment information [9]. Another type of NLOS scenario is the corner, where the LOS path does not exist and targets in NLOS regions are located by multipath echoess [10,11].…”

Section: Introductionmentioning

confidence: 99%

A target localisation method with monostatic radar via multi‐observation data association

Ding

Wang

Jiang

et al. 2022

IET Radar Sonar & Navi

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Traditionally, radar-based localisation systems are designed to deal with targets in line-ofsight (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-ofarrivals (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 millimetrewave 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. K E Y W O R D Smulti-observation data association, multipath exploitation, non-line-of-sight (NLOS), target localisation, timeof-arrival (TOA) estimationThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Single‐station radar‐based UAV position estimation in complex urban environments

Cited by 2 publications

References 11 publications

A survey and analysis of drone detection systems using a systems approach superposed on scenarios

A survey and analysis of drone detection systems using a systems approach superposed on scenarios

A target localisation method with monostatic radar via multi‐observation data association

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