English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Drones have become essential tools in a wide range of industries, includingagriculture, surveying, and transportation. However, tracking unmanned aerialvehicles (UAVs) in challenging environments, such cluttered or GNSS-deniedenvironments, remains a critical issue. Additionally, UAVs are being deployedas part of multi-robot systems, where tracking their position can be essentialfor relative state estimation. In this paper, we evaluate the performance of amulti-scan integration method for tracking UAVs in GNSS-denied environmentsusing a solid-state LiDAR and a Kalman Filter (KF). We evaluate the algorithm'sability to track a UAV in a large open area at various distances and speeds.Our quantitative analysis shows that while "tracking by detection" using aconstant velocity model is the only method that consistently tracks the target,integrating multiple scan frequencies using a KF achieves lower position errorsand represents a viable option for tracking UAVs in similar scenarios.

Evaluating the Performance of Multi-Scan Integration for UAV LiDAR-based Tracking