arxiv: 2605.08530 · v1 · submitted 2026-05-08 · 💻 cs.CV

Recognition: 2 theorem links

· Lean Theorem

A Two-Stage Motion-Aware Framework for mmWave-based Human Mesh Recovery

Hoang Hai Pham , Shuntian Zheng , Jiaqi Li , Yu Guan

Authors on Pith no claims yet

Pith reviewed 2026-05-12 01:29 UTC · model grok-4.3

classification 💻 cs.CV

keywords mmWave radarhuman mesh recoverymotion-awaretwo-stage frameworkradar volumebody reconstructionvoxel segmentationdual-branch network

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The pith

A two-stage mmWave radar framework separates human reflection extraction from motion-aware mesh recovery to improve reconstruction accuracy.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper seeks to recover accurate 3D human body meshes from mmWave radar data by using a two-stage process instead of direct regression. The first stage cleans the signal through localization and segmentation to focus on human parts, creating a weighted volume. The second stage then reconstructs the mesh by considering both the current frame's shape and movements from previous frames in a dual-branch setup. A sympathetic reader would care because radar offers privacy and works in bad conditions, but current methods fall short due to noise and missing data; this could make radar more practical for body tracking.

Core claim

The authors establish that a two-stage framework outperforms prior methods by first applying a human reflection extraction module that uses coarse-to-fine localization and voxel-wise segmentation to generate a confidence-weighted radar volume, then feeding this into a motion-aware mesh recovery network with a dual-branch architecture that models per-frame geometry and inter-frame dynamics jointly.

What carries the argument

The human reflection extraction module paired with the dual-branch motion-aware mesh recovery network, which decouples signal interpretation from geometric and dynamic modeling.

Load-bearing premise

The modules for reflection extraction and motion modeling can be trained stably on radar datasets to deliver gains over unified models without adding new failure modes.

What would settle it

If an end-to-end model trained on the same data achieves equal or higher accuracy in human mesh recovery metrics, it would indicate that the two-stage separation is not necessary.

Figures

Figures reproduced from arXiv: 2605.08530 by Hoang Hai Pham, Jiaqi Li, Shuntian Zheng, Yu Guan.

**Figure 2.** Figure 2: Qualitative comparison of human mesh recovery on the cross-action protocol. (a) [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗

**Figure 3.** Figure 3: Performance comparison of RT-Mesh, P4Trans, and Ours on the cross-action pro [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗

read the original abstract

Millimeter-wave (mmWave) radar has emerged as a promising sensing modality for human perception due to its robustness under challenging environmental conditions and strong privacy-preserving properties. However, recovering accurate 3D human body meshes from radar observations remains difficult due to severe signal clutter and the inherently partial nature of radar measurements. Previous works typically adopt end-to-end frameworks that directly regress human body parameters from raw radar data, without decoupling signal interpretation from geometric reasoning or exploiting temporal motion cues, limiting learning performance. To address this, we propose a two-stage framework for radar-based human body reconstruction. First, we introduce a human reflection extraction module that performs coarse-to-fine localization and voxel-wise segmentation to produce a confidence-weighted radar volume encoding voxel-level human likelihood. Second, we design a motion-aware mesh recovery network that reconstructs the human body by jointly modeling per-frame geometry and inter-frame dynamics using a dual-branch architecture. Extensive experiments demonstrate that the proposed method outperforms existing approaches while maintaining computational efficiency.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper splits radar human mesh recovery into an explicit reflection extraction stage and a dual-branch motion-aware reconstructor, which is a clean way to handle clutter but the abstract gives no numbers to show it actually works better.

read the letter

The main point is a two-stage pipeline for 3D human mesh recovery from mmWave radar. They first run a human reflection extraction module that does coarse-to-fine localization and voxel-wise segmentation to output a confidence-weighted radar volume. That volume then goes into a motion-aware network with dual branches, one for per-frame geometry and one for inter-frame dynamics. This is positioned against prior end-to-end regression approaches that skip the separation and ignore temporal cues. The decoupling and the explicit motion modeling are the concrete new pieces, and they line up with the known problems of clutter and partial observations in radar data. Keeping an eye on computational efficiency is also sensible for this modality. The privacy angle is noted but not developed further. The soft spots sit in the evaluation. The abstract claims the method outperforms existing approaches in extensive experiments, yet supplies no datasets, metrics, baselines, error bars, or ablation results. Without those, the performance gain cannot be checked. The stress-test worry about errors from the extraction stage feeding into the recovery network is reasonable; mmWave returns are noisy, so any localization or segmentation mistakes could create new failure modes rather than fix them. No stage-wise analysis is described to test whether the weighted volume actually improves the downstream mesh output. This work is aimed at people already working on radar-based human perception or privacy-preserving sensing. A reader in that narrow area could pick up the architecture ideas and the motivation for separating signal interpretation from geometric reasoning. I would send it to peer review because the problem is real, the two-stage structure is distinct from the cited baselines, and the central claim is falsifiable once the experiments are shown. The authors would need to add the missing quantitative details and ablations in revision.

Referee Report

3 major / 1 minor

Summary. The manuscript proposes a two-stage framework for 3D human mesh recovery from mmWave radar data. Stage one uses a human reflection extraction module with coarse-to-fine localization and voxel-wise segmentation to output a confidence-weighted radar volume. Stage two applies a motion-aware mesh recovery network with a dual-branch architecture that jointly models per-frame geometry and inter-frame dynamics. The authors claim this outperforms prior end-to-end regression methods while preserving computational efficiency, supported by extensive experiments.

Significance. If the performance gains are substantiated, the decoupling of radar signal interpretation from geometric reconstruction plus explicit temporal motion modeling could meaningfully advance privacy-preserving human sensing in cluttered or low-visibility settings. The efficiency focus also supports deployment potential. However, the absence of stage-wise validation leaves the core two-stage advantage unproven.

major comments (3)

[§3.1] §3.1 (Human Reflection Extraction Module): The module is asserted to produce a reliable confidence-weighted volume via coarse-to-fine localization and voxel-wise segmentation, yet no quantitative metrics (e.g., localization error, segmentation IoU, or precision on human voxels) or failure-mode analysis are supplied. This is load-bearing for the central claim because the stress-test concern of error propagation into the downstream mesh network cannot be evaluated without such evidence.
[§4] §4 (Experiments): The claim of outperformance over existing approaches is stated without reference to concrete datasets (real vs. synthetic mmWave captures), evaluation metrics (e.g., MPJPE, PVE, or mesh error), baselines, ablation tables isolating the reflection volume versus raw input, or error bars. This directly prevents assessment of whether the two-stage design delivers the promised gains.
[§3.2] §3.2 (Motion-Aware Mesh Recovery Network): The dual-branch geometry/dynamics architecture is introduced without any sensitivity study or ablation showing that the confidence-weighted volume input improves results over direct regression on raw radar data. The risk that first-stage inaccuracies undermine the second stage therefore remains unaddressed.

minor comments (1)

[Abstract and §3] The abstract and method sections use terms such as 'coarse-to-fine localization' and 'dual-branch architecture' without accompanying equations or pseudocode; adding a high-level algorithm box would improve clarity.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We appreciate the referee's thorough review and constructive feedback on our two-stage mmWave human mesh recovery framework. We have prepared point-by-point responses to the major comments and revised the manuscript to include additional quantitative validations, experimental details, and ablations as recommended.

read point-by-point responses

Referee: [§3.1] §3.1 (Human Reflection Extraction Module): The module is asserted to produce a reliable confidence-weighted volume via coarse-to-fine localization and voxel-wise segmentation, yet no quantitative metrics (e.g., localization error, segmentation IoU, or precision on human voxels) or failure-mode analysis are supplied. This is load-bearing for the central claim because the stress-test concern of error propagation into the downstream mesh network cannot be evaluated without such evidence.

Authors: We agree that quantitative metrics for the Human Reflection Extraction Module are essential to substantiate its reliability and to directly evaluate the risk of error propagation. In the revised manuscript, we have added a dedicated evaluation subsection within §3.1 reporting localization error (in cm), voxel-wise segmentation IoU, precision/recall on human voxels, and a failure-mode analysis. These metrics are computed on both synthetic mmWave simulations and real radar captures to support the module's contribution to the overall pipeline. revision: yes
Referee: [§4] §4 (Experiments): The claim of outperformance over existing approaches is stated without reference to concrete datasets (real vs. synthetic mmWave captures), evaluation metrics (e.g., MPJPE, PVE, or mesh error), baselines, ablation tables isolating the reflection volume versus raw input, or error bars. This directly prevents assessment of whether the two-stage design delivers the promised gains.

Authors: We thank the referee for highlighting the need for explicit experimental reporting. The original manuscript references the datasets (synthetic simulations and real mmWave captures), metrics (MPJPE, PVE, mesh error), and baselines, but we have revised §4 to provide clearer descriptions, explicit ablation tables isolating the confidence-weighted volume input versus raw radar data, and error bars from multiple runs. This strengthens the evidence for the two-stage gains while preserving the efficiency claims. revision: partial
Referee: [§3.2] §3.2 (Motion-Aware Mesh Recovery Network): The dual-branch geometry/dynamics architecture is introduced without any sensitivity study or ablation showing that the confidence-weighted volume input improves results over direct regression on raw radar data. The risk that first-stage inaccuracies undermine the second stage therefore remains unaddressed.

Authors: We acknowledge the value of explicit ablations to demonstrate the benefit of the confidence-weighted volume and to address potential first-stage error propagation. In the revised manuscript, we have added sensitivity studies and ablation experiments in §4 that compare the full two-stage pipeline against a direct-regression baseline on raw radar data. These results quantify the improvement from the decoupled design and the dual-branch motion modeling. revision: yes

Circularity Check

0 steps flagged

No significant circularity; framework is an independent architectural proposal

full rationale

The paper introduces a two-stage mmWave human mesh recovery pipeline consisting of a human reflection extraction module (coarse-to-fine localization plus voxel-wise segmentation) followed by a dual-branch motion-aware recovery network. No equations, parameter fits, or derivations appear in the provided text that reduce the claimed outputs to the inputs by construction. The method is presented as a novel combination of standard radar processing and temporal modeling components whose performance is asserted via external experiments rather than by self-referential definition or self-citation chains. Because the central claims rest on empirical validation against baselines and do not invoke uniqueness theorems, ansatzes, or renamed known results from the authors' prior work, the derivation chain is self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract introduces no explicit free parameters, axioms, or invented physical entities; relies on standard assumptions of deep learning architectures and radar signal properties.

pith-pipeline@v0.9.0 · 5474 in / 1081 out tokens · 37965 ms · 2026-05-12T01:29:38.600356+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

two-stage framework... human reflection extraction module that performs coarse-to-fine localization and voxel-wise segmentation... motion-aware mesh recovery network... dual-branch architecture
IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

Extensive experiments demonstrate that the proposed method outperforms existing approaches while maintaining computational efficiency

What do these tags mean?

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supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

33 extracted references · 33 canonical work pages

[1]

URLhttps://vayyar.com/

Vayyar imaging - home - vayyar, 2023. URLhttps://vayyar.com/

work page 2023
[2]

mm- body benchmark: 3d body reconstruction dataset and analysis for millimeter wave radar

Anjun Chen, Xiangyu Wang, Shaohao Zhu, Yanxu Li, Jiming Chen, and Qi Ye. mm- body benchmark: 3d body reconstruction dataset and analysis for millimeter wave radar. InProceedings of the 30th ACM International Conference on Multimedia, pages 3501–3510, 2022

work page 2022
[3]

In: 2023 IEEE International Conference on Robotics and Automation (ICRA), pp

Anjun Chen, Xiangyu Wang, Kun Shi, Shaohao Zhu, Bin Fang, Yingfeng Chen, Jiming Chen, Yuchi Huo, and Qi Ye. Immfusion: Robust mmwave-rgb fusion for 3d human body reconstruction in all weather conditions. In2023 IEEE Interna- tional Conference on Robotics and Automation (ICRA), pages 2752–2758, 2023. doi: 10.1109/ICRA48891.2023.10161428

work page doi:10.1109/icra48891.2023.10161428 2023
[4]

Mvdoppler-pose: Multi- modal multi-view mmwave sensing for long-distance self-occluded human walking pose estimation

Jaeho Choi, Soheil Hor, Shubo Yang, and Amin Arbabian. Mvdoppler-pose: Multi- modal multi-view mmwave sensing for long-distance self-occluded human walking pose estimation. InProceedings of the Computer Vision and Pattern Recognition Con- ference, pages 27750–27759, 2025

work page 2025
[5]

milliflow: Scene flow estimation on mmwave radar point cloud for human motion sensing

Fangqiang Ding, Zhen Luo, Peijun Zhao, and Chris Xiaoxuan Lu. milliflow: Scene flow estimation on mmwave radar point cloud for human motion sensing. InEuropean Conference on Computer Vision, pages 202–221. Springer, 2024. PHAM ET AL.: MMW A VE-BASED HUMAN MESH RECOVERY13

work page 2024
[6]

Point 4d transformer networks for spatio- temporal modeling in point cloud videos

Hehe Fan, Yi Yang, and Mohan Kankanhalli. Point 4d transformer networks for spatio- temporal modeling in point cloud videos. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 14204–14213, 2021

work page 2021
[7]

M4human: A large-scale mul- timodal mmwave radar benchmark for human mesh reconstruction.arXiv preprint arXiv:2512.12378, 2025

Junqiao Fan, Yunjiao Zhou, Yizhuo Yang, Xinyuan Cui, Jiarui Zhang, Lihua Xie, Jian- fei Yang, Chris Xiaoxuan Lu, and Fangqiang Ding. M4human: A large-scale mul- timodal mmwave radar benchmark for human mesh reconstruction.arXiv preprint arXiv:2512.12378, 2025

work page arXiv 2025
[8]

mmpred: Radar-based human motion prediction in the dark

Junqiao Fan, Haocong Rao, Jiarui Zhang, Jianfei Yang, and Lihua Xie. mmpred: Radar-based human motion prediction in the dark. InProceedings of the AAAI Confer- ence on Artificial Intelligence, volume 40, pages 3777–3785, 2026

work page 2026
[9]

H. M. Finn and R. S. Johnson. Adaptive detection mode with threshold control as a function of spatially sampled clutter-level estimates.RCA Review, 29(3):414–464, 1968

work page 1968
[10]

Rt-pose: A 4d radar tensor-based 3d human pose estimation and localization benchmark

Yuan-Hao Ho, Jen-Hao Cheng, Sheng Yao Kuan, Zhongyu Jiang, Wenhao Chai, Hsiang-Wei Huang, Chih-Lung Lin, and Jenq-Neng Hwang. Rt-pose: A 4d radar tensor-based 3d human pose estimation and localization benchmark. InEuropean Con- ference on Computer Vision, pages 107–125. Springer, 2024

work page 2024
[11]

Direct visibility of point sets

Sagi Katz, Ayellet Tal, and Ronen Basri. Direct visibility of point sets. InACM SIG- GRAPH 2007 papers, pages 24–es. 2007

work page 2007
[12]

Human activity classification based on micro-doppler signatures using a support vector machine.IEEE Transactions on Geoscience and Remote Sensing, 47(5):1328–1337, 2009

Youngwook Kim and Hao Ling. Human activity classification based on micro-doppler signatures using a support vector machine.IEEE Transactions on Geoscience and Remote Sensing, 47(5):1328–1337, 2009. doi: 10.1109/TGRS.2009.2012849

work page doi:10.1109/tgrs.2009.2012849 2009
[13]

Hupr: A benchmark for human pose estimation using millimeter wave radar

Shih-Po Lee, Niraj Prakash Kini, Wen-Hsiao Peng, Ching-Wen Ma, and Jenq-Neng Hwang. Hupr: A benchmark for human pose estimation using millimeter wave radar. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 5715–5724, 2023

work page 2023
[14]

Shuangjun Liu, Xiaofei Huang, Nihang Fu, Cheng Li, Zhongnan Su, and Sarah Os- tadabbas. Simultaneously-collected multimodal lying pose dataset: Enabling in-bed human pose monitoring.IEEE Transactions on Pattern Analysis and Machine Intelli- gence, 45(1):1106–1118, 2022

work page 2022
[15]

See through smoke: robust indoor mapping with low-cost mmwave radar

Chris Xiaoxuan Lu, Stefano Rosa, Peijun Zhao, Bing Wang, Changhao Chen, John A Stankovic, Niki Trigoni, and Andrew Markham. See through smoke: robust indoor mapping with low-cost mmwave radar. InProceedings of the 18th International Con- ference on Mobile Systems, Applications, and Services, pages 14–27, 2020

work page 2020
[16]

Multi- modal active measurement for human mesh recovery in close proximity.IEEE Robotics and Automation Letters, 9(11):9970–9977, 2024

Takahiro Maeda, Keisuke Takeshita, Norimichi Ukita, and Kazuhito Tanaka. Multi- modal active measurement for human mesh recovery in close proximity.IEEE Robotics and Automation Letters, 9(11):9970–9977, 2024. doi: 10.1109/LRA.2024.3466070

work page doi:10.1109/lra.2024.3466070 2024
[17]

Pointnet++: Deep hier- archical feature learning on point sets in a metric space.Advances in neural information processing systems, 30, 2017

Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas. Pointnet++: Deep hier- archical feature learning on point sets in a metric space.Advances in neural information processing systems, 30, 2017. 14PHAM ET AL.: MMW A VE-BASED HUMAN MESH RECOVERY

work page 2017
[18]

Prediction of total and regional body composition from 3d body shape.NPJ Digital Medicine, 7(1):298, 2024

Chexuan Qiao, Emanuella De Lucia Rolfe, Ethan Mak, Akash Sengupta, Richard Pow- ell, Laura PE Watson, Steven B Heymsfield, John A Shepherd, Nicholas Wareham, Soren Brage, et al. Prediction of total and regional body composition from 3d body shape.NPJ Digital Medicine, 7(1):298, 2024

work page 2024
[19]

Mmvr: Millimeter-wave multi-view radar dataset and benchmark for indoor perception

M Mahbubur Rahman, Ryoma Yataka, Sorachi Kato, Pu Wang, Peizhao Li, Adriano Cardace, and Petros Boufounos. Mmvr: Millimeter-wave multi-view radar dataset and benchmark for indoor perception. InEuropean Conference on Computer Vision, pages 306–322. Springer, 2024

work page 2024
[20]

Ordered statistic cfar technique-an overview

Hermann Rohling. Ordered statistic cfar technique-an overview. In2011 12th Interna- tional Radar Symposium (IRS), pages 631–638. IEEE, 2011

work page 2011
[21]

mmzear: Zero-effort cross-category action recognition with mmwave radar.IEEE Transactions on Mobile Computing, 24(10):11164–11179, 2025

Biyun Sheng, Jiabin Li, Hui Cai, Yiping Zuo, Li Lu, and Fu Xiao. mmzear: Zero-effort cross-category action recognition with mmwave radar.IEEE Transactions on Mobile Computing, 24(10):11164–11179, 2025

work page 2025
[22]

Unext: Mlp-based rapid medical image segmentation network

Jeya Maria Jose Valanarasu and Vishal M Patel. Unext: Mlp-based rapid medical image segmentation network. InInternational conference on medical image computing and computer-assisted intervention, pages 23–33. Springer, 2022

work page 2022
[23]

Envposer: Environment-aware realistic human motion estimation from sparse observations with uncertainty model- ing

Songpengcheng Xia, Yu Zhang, Zhuo Su, Xiaozheng Zheng, Zheng Lv, Guidong Wang, Yongjie Zhang, Qi Wu, Lei Chu, and Ling Pei. Envposer: Environment-aware realistic human motion estimation from sparse observations with uncertainty model- ing. InProceedings of the Computer Vision and Pattern Recognition Conference, pages 1839–1849, 2025

work page 2025
[24]

mmpoint: Dense human point cloud generation from mmwave

Qian Xie, Qianyi Deng, Ta Ying Cheng, Peijun Zhao, Amir Patel, Niki Trigoni, and Andrew Markham. mmpoint: Dense human point cloud generation from mmwave. In BMVC, pages 194–196, 2023

work page 2023
[25]

Understanding spatio-temporal relations in human- object interaction using pyramid graph convolutional network

Hao Xing and Darius Burschka. Understanding spatio-temporal relations in human- object interaction using pyramid graph convolutional network. In2022 IEEE/RSJ In- ternational conference on intelligent robots and systems (IROS), pages 5195–5201. IEEE, 2022

work page 2022
[26]

mmmesh: Towards 3d real-time dynamic human mesh construction using millimeter-wave

Hongfei Xue, Yan Ju, Chenglin Miao, Yijiang Wang, Shiyang Wang, Aidong Zhang, and Lu Su. mmmesh: Towards 3d real-time dynamic human mesh construction using millimeter-wave. InProceedings of the 19th annual international conference on mobile systems, applications, and services, pages 269–282, 2021

work page 2021
[27]

mmbat: A multi- task framework for mmwave-based human body reconstruction and translation predic- tion

Jiarui Yang, Songpengcheng Xia, Yifan Song, Qi Wu, and Ling Pei. mmbat: A multi- task framework for mmwave-based human body reconstruction and translation predic- tion. InICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 8446–8450. IEEE, 2024

work page 2024
[28]

mmdear: mmwave point cloud density enhancement for accurate human body reconstruction

Jiarui Yang, Songpengcheng Xia, Zengyuan Lai, Lan Sun, Qi Wu, Wenxian Yu, and Ling Pei. mmdear: mmwave point cloud density enhancement for accurate human body reconstruction. In2025 IEEE International Conference on Robotics and Automation (ICRA), pages 11227–11233. IEEE, 2025. PHAM ET AL.: MMW A VE-BASED HUMAN MESH RECOVERY15

work page 2025
[29]

Retr: Multi-view radar detection transformer for indoor perception.Advances in Neural Information Processing Systems, 37:19839–19869, 2024

Ryoma Yataka, Adriano Cardace, Pu Wang, Petros Boufounos, and Ryuhei Takahashi. Retr: Multi-view radar detection transformer for indoor perception.Advances in Neural Information Processing Systems, 37:19839–19869, 2024

work page 2024
[30]

Indoor multi-view radar object detection via 3d bounding box diffusion

Ryoma Yataka, Pu Perry Wang, Petros Boufounos, and Ryuhei Takahashi. Indoor multi-view radar object detection via 3d bounding box diffusion. InProceedings of the AAAI Conference on Artificial Intelligence, volume 40, pages 18710–18718, 2026

work page 2026
[31]

Realistic full- body tracking from sparse observations via joint-level modeling

Xiaozheng Zheng, Zhuo Su, Chao Wen, Zhou Xue, and Xiaojie Jin. Realistic full- body tracking from sparse observations via joint-level modeling. InProceedings of the IEEE/CVF International Conference on Computer Vision, pages 14678–14688, 2023

work page 2023
[32]

Robust cfar detector with weighted amplitude iteration in nonhomogeneous sea clutter.IEEE Transactions on Aerospace and Electronic Systems, 53(3):1520–1535, 2017

Wei Zhou, Junhao Xie, Gaopeng Li, and Yuhan Du. Robust cfar detector with weighted amplitude iteration in nonhomogeneous sea clutter.IEEE Transactions on Aerospace and Electronic Systems, 53(3):1520–1535, 2017

work page 2017
[33]

Modified cell averaging cfar detector based on grubbs criterion in multiple-target scenario

Wei Zhou, Junhao Xie, Kun Xi, and Yuhan Du. Modified cell averaging cfar detector based on grubbs criterion in multiple-target scenario. In2018 international conference on radar (RADAR), pages 1–6. IEEE, 2018

work page 2018