Benchmark Dataset of Ultra-Wideband Radio Based UAV Positioning

Arjmandi, Zahra; Kang, Jungwon; Park, Kunwoo; Sohn, Gunho

doi:10.1109/itsc45102.2020.9294440

Cited by 6 publications

(2 citation statements)

References 16 publications

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“…The need for a unified dataset for evaluating different algorithms is also indicated in [15]. The authors have made a subset of drone-collected datasets available for UWB designers to test their algorithms on and to be able to validate the algorithms in detail.…”

Section: Related Work On Standardized Uwb Benchmarkingmentioning

confidence: 99%

“…By using an indoor location system for asset tracking, supply chains can be optimized and labor costs can be reduced. Other use cases include athlete tracking in sports [11]- [13], radar [14], unmanned aerial vehicles (UAVs) [15], [16] and many more. Recent scientific research papers have proposed techniques to improve UWB indoor localization accuracy further, using techniques such as bias correction, machine learning based error correction, outlier removal, adaptive physical layer (PHY) settings, path filtering, etc.…”

Section: Introductionmentioning

confidence: 99%

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Selecting and Combining UWB Localization Algorithms: Insights and Recommendations From a Multi-Metric Benchmark

Van Herbruggen,

Gerwen,

Luchie

et al. 2024

IEEE Access

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Ultra-wideband (UWB) localization has emerged in GPS-denied environments as a crucial facilitator for diverse industries, including logistics, healthcare applications, and societal domains. Despite notable progress, UWB algorithms from the scientific literature are often evaluated in isolation in very specific conditions and hence difficult to compare. This paper introduces a novel benchmark platform designed to assess the performance of 11 prominent UWB accuracy-enhancing algorithms, both independently and in combination. A key feature of the platform is its incorporation of multiple diverse evaluation metrics, including mean average error, latency, and spatial error. We showcase the significance of adopting alternative metrics such as spatial error, which often, depending on the use case, offers greater relevance than the prevalently used mean average error. Furthermore, we show that ''more is better'' does not hold true when combining multiple accuracy-improving algorithms for UWB systems: combining multiple accuracyimproving algorithms reveals instances of diminishing returns and can even result in overall performance decline. Additionally, we caution against blind reliance on accuracy outcomes reported in the scientific literature when designing UWB systems for use cases that are different in terms of requirements or environment. Finally, we also provide algorithmic recommendations for distinct surroundings, exemplary applications, and usage contexts, assisting in driving efficient design in future UWB research and adoption.

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Section: Related Work On Standardized Uwb Benchmarkingmentioning

confidence: 99%