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VRPF: A Fine-Grained 3D Radar Power-Density Computation Framework Based on Photogrammetric City Models for Urban Observation.

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Title: VRPF: A Fine-Grained 3D Radar Power-Density Computation Framework Based on Photogrammetric City Models for Urban Observation.
Authors: Jiao, Linhui1 (AUTHOR), Yang, Anran1 (AUTHOR) yanganran@nudt.edu.cn, Jia, Qingren1 (AUTHOR), Ma, Mengyu1 (AUTHOR), Zhang, Yifan1 (AUTHOR), Wang, Linyue1 (AUTHOR), Li, Jun1 (AUTHOR)
Source: Remote Sensing. Jun2026, Vol. 18 Issue 12, p1936. 33p.
Subjects: Ray tracing, Spatial data structures, Drone aircraft, Geographic spatial analysis, Sensor placement
Abstract: Highlights: What are the main findings? VRPF couples 3D mesh visibility with radar parameters to compute direct-path power density under urban blockage. It uses reusable spatial indexing, AABB pruning, ray-triangle tests, and multi-baseline validation to improve accuracy and efficiency. What are the implications of the main findings? VRPF enables fine-grained assessment of direct-path radar power-density distributions in complex urban environments. The method supports sensor-deployment assessment for the low-altitude economy and contributes to urban observation and counter-UAV planning. Radar is critical for urban security against Unmanned Aerial Vehicles (UAVs), yet signal occlusion caused by dense buildings and complex urban structures remains a major challenge for coverage assessment. Existing approaches commonly rely on 2D maps or 2.5D Digital Surface Models (DSMs), which have difficulty representing vertical facades, vegetation, bridges, overhanging structures, and void spaces. These geometric limitations can introduce errors in radar occlusion determination and direct-path power-density estimation. Full 3D ray-tracing methods offer high fidelity, but their multi-path modeling and material-parameter requirements can be costly for large oblique photogrammetric city meshes. To address this problem, this paper proposes the Visible Radar Power-Density Field (VRPF), a 3D radar power-density field computation framework based on high-resolution oblique photogrammetric models. The method constructs a reusable spatial index for large numbers of triangular facets and performs two-stage occlusion queries: rapid Axis-Aligned Bounding Box (AABB) pruning followed by ray-triangle intersection tests. Together, these components enable efficient direct-path power-density calculation while accounting for line-of-sight occlusion in complex urban scenes. Qualitative and quantitative experiments show that VRPF better preserves occlusion boundaries around building edges, vegetation, and elevated structures than DSM-based baselines. VRPF also requires less time for repeated occlusion queries than a conventional 3D BVH ray-casting baseline while maintaining highly consistent radar-signal occlusion determinations. With 32 threads, VRPF computes power density for 10 8 target points in 5.92 s, about 2.66 × faster than the 1 m DSM method. These results indicate that VRPF provides a practical balance between geometric fidelity and computational efficiency for direct-path radar power-density assessment with urban geometric occlusion. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: VRPF: A Fine-Grained 3D Radar Power-Density Computation Framework Based on Photogrammetric City Models for Urban Observation.
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jun2026, Vol. 18 Issue 12, p1936. 33p.
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  Data: <searchLink fieldCode="DE" term="%22Ray+tracing%22">Ray tracing</searchLink><br /><searchLink fieldCode="DE" term="%22Spatial+data+structures%22">Spatial data structures</searchLink><br /><searchLink fieldCode="DE" term="%22Drone+aircraft%22">Drone aircraft</searchLink><br /><searchLink fieldCode="DE" term="%22Geographic+spatial+analysis%22">Geographic spatial analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Sensor+placement%22">Sensor placement</searchLink>
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  Label: Abstract
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  Data: Highlights: What are the main findings? VRPF couples 3D mesh visibility with radar parameters to compute direct-path power density under urban blockage. It uses reusable spatial indexing, AABB pruning, ray-triangle tests, and multi-baseline validation to improve accuracy and efficiency. What are the implications of the main findings? VRPF enables fine-grained assessment of direct-path radar power-density distributions in complex urban environments. The method supports sensor-deployment assessment for the low-altitude economy and contributes to urban observation and counter-UAV planning. Radar is critical for urban security against Unmanned Aerial Vehicles (UAVs), yet signal occlusion caused by dense buildings and complex urban structures remains a major challenge for coverage assessment. Existing approaches commonly rely on 2D maps or 2.5D Digital Surface Models (DSMs), which have difficulty representing vertical facades, vegetation, bridges, overhanging structures, and void spaces. These geometric limitations can introduce errors in radar occlusion determination and direct-path power-density estimation. Full 3D ray-tracing methods offer high fidelity, but their multi-path modeling and material-parameter requirements can be costly for large oblique photogrammetric city meshes. To address this problem, this paper proposes the Visible Radar Power-Density Field (VRPF), a 3D radar power-density field computation framework based on high-resolution oblique photogrammetric models. The method constructs a reusable spatial index for large numbers of triangular facets and performs two-stage occlusion queries: rapid Axis-Aligned Bounding Box (AABB) pruning followed by ray-triangle intersection tests. Together, these components enable efficient direct-path power-density calculation while accounting for line-of-sight occlusion in complex urban scenes. Qualitative and quantitative experiments show that VRPF better preserves occlusion boundaries around building edges, vegetation, and elevated structures than DSM-based baselines. VRPF also requires less time for repeated occlusion queries than a conventional 3D BVH ray-casting baseline while maintaining highly consistent radar-signal occlusion determinations. With 32 threads, VRPF computes power density for 10 8 target points in 5.92 s, about 2.66 × faster than the 1 m DSM method. These results indicate that VRPF provides a practical balance between geometric fidelity and computational efficiency for direct-path radar power-density assessment with urban geometric occlusion. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/rs18121936
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        Text: English
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        StartPage: 1936
    Subjects:
      – SubjectFull: Ray tracing
        Type: general
      – SubjectFull: Spatial data structures
        Type: general
      – SubjectFull: Drone aircraft
        Type: general
      – SubjectFull: Geographic spatial analysis
        Type: general
      – SubjectFull: Sensor placement
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      – TitleFull: VRPF: A Fine-Grained 3D Radar Power-Density Computation Framework Based on Photogrammetric City Models for Urban Observation.
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            NameFull: Jiao, Linhui
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              Text: Jun2026
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              Y: 2026
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