HyperNCMD: A Scene-Adaptive Clutter Measurement Density Estimator for Radar Tracking via Hypernetworks and Normalizing Flows.
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| Title: | HyperNCMD: A Scene-Adaptive Clutter Measurement Density Estimator for Radar Tracking via Hypernetworks and Normalizing Flows. |
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| Authors: | Cao, Zongqing1 (AUTHOR), Yang, Jianchao1 (AUTHOR), Sun, Wang1 (AUTHOR), Lu, Xingyu1 (AUTHOR), Tan, Ke1 (AUTHOR), Dai, Zheng1 (AUTHOR), Yu, Wenchao1 (AUTHOR), Gu, Hong1 (AUTHOR) guhong666@njust.edu.cn |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1541. 30p. |
| Subjects: | Clutter (Radar), Multiple target tracking, Probabilistic generative models, Tracking radar, Encoding |
| Abstract: | Highlights: What are the main findings? We propose HyperNCMD, a scene-adaptive clutter measurement density (CMD) estimator that employs a hypernetwork to dynamically generate normalizing flow parameters conditioned on scene representations, enabling fast adaptation to unseen environments without full retraining. HyperNCMD leverages Random Fourier Features (RFFs) and a proposed ISAB-LSTM module to encode spatio-temporal information from raw radar measurements, and further improves adaptation to novel environments via Feature-wise Linear Modulation (FiLM)-based test-time fine-tuning. What are the implications of the main findings? HyperNCMD demonstrates strong robustness and estimation accuracy across spatially and temporally varying clutter, highlighting the benefit of hypernetwork-driven parameter generation for adaptive radar CMD modeling. The proposed framework provides a scalable and deployment-friendly solution for CMD estimation, enabling more reliable clutter distribution modeling for multi-target tracking (MTT) and downstream radar perception in complex environments. Accurateestimation of clutter measurement density (CMD) is crucial for radar-based multi-target tracking (MTT), especially under spatially non-uniform and temporally varying environments. Existing methods, including finite mixture models, kernel density estimation, and normalizing flows, often require scene-specific tuning and exhibit limited generalization. To address these limitations, we propose HyperNCMD, a scene-adaptive CMD estimator that employs hypernetworks to dynamically generate the parameters of normalizing flows. To capture spatial variability, radar measurements are first embedded using Random Fourier Features (RFFs), and then processed by a spatio-temporal encoder that jointly models spatial structures and temporal clutter dynamics. The hypernetwork leverages the encoded embedding to adaptively produce flow parameters, enabling flexible CMD estimation across diverse environments. Lightweight data augmentation is further applied to make the estimator more robust across diverse environments, while a Feature-wise Linear Modulation (FiLM)-based fine-tuning scheme enhances test-time adaptation. Experiments on both synthetic and real radar datasets demonstrate that HyperNCMD achieves superior accuracy and robustness, achieving up to 10.5% reduction in per-point negative log-likelihood under dynamically varying conditions. These results highlight the potential of hypernetwork-driven CMD modeling for reliable radar perception in complex sensing environments. [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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| Header | DbId: egs DbLabel: Engineering Source An: 194141066 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: HyperNCMD: A Scene-Adaptive Clutter Measurement Density Estimator for Radar Tracking via Hypernetworks and Normalizing Flows. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Cao%2C+Zongqing%22">Cao, Zongqing</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Jianchao%22">Yang, Jianchao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sun%2C+Wang%22">Sun, Wang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lu%2C+Xingyu%22">Lu, Xingyu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tan%2C+Ke%22">Tan, Ke</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dai%2C+Zheng%22">Dai, Zheng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yu%2C+Wenchao%22">Yu, Wenchao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gu%2C+Hong%22">Gu, Hong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> guhong666@njust.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. May2026, Vol. 18 Issue 10, p1541. 30p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Clutter+%28Radar%29%22">Clutter (Radar)</searchLink><br /><searchLink fieldCode="DE" term="%22Multiple+target+tracking%22">Multiple target tracking</searchLink><br /><searchLink fieldCode="DE" term="%22Probabilistic+generative+models%22">Probabilistic generative models</searchLink><br /><searchLink fieldCode="DE" term="%22Tracking+radar%22">Tracking radar</searchLink><br /><searchLink fieldCode="DE" term="%22Encoding%22">Encoding</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? We propose HyperNCMD, a scene-adaptive clutter measurement density (CMD) estimator that employs a hypernetwork to dynamically generate normalizing flow parameters conditioned on scene representations, enabling fast adaptation to unseen environments without full retraining. HyperNCMD leverages Random Fourier Features (RFFs) and a proposed ISAB-LSTM module to encode spatio-temporal information from raw radar measurements, and further improves adaptation to novel environments via Feature-wise Linear Modulation (FiLM)-based test-time fine-tuning. What are the implications of the main findings? HyperNCMD demonstrates strong robustness and estimation accuracy across spatially and temporally varying clutter, highlighting the benefit of hypernetwork-driven parameter generation for adaptive radar CMD modeling. The proposed framework provides a scalable and deployment-friendly solution for CMD estimation, enabling more reliable clutter distribution modeling for multi-target tracking (MTT) and downstream radar perception in complex environments. Accurateestimation of clutter measurement density (CMD) is crucial for radar-based multi-target tracking (MTT), especially under spatially non-uniform and temporally varying environments. Existing methods, including finite mixture models, kernel density estimation, and normalizing flows, often require scene-specific tuning and exhibit limited generalization. To address these limitations, we propose HyperNCMD, a scene-adaptive CMD estimator that employs hypernetworks to dynamically generate the parameters of normalizing flows. To capture spatial variability, radar measurements are first embedded using Random Fourier Features (RFFs), and then processed by a spatio-temporal encoder that jointly models spatial structures and temporal clutter dynamics. The hypernetwork leverages the encoded embedding to adaptively produce flow parameters, enabling flexible CMD estimation across diverse environments. Lightweight data augmentation is further applied to make the estimator more robust across diverse environments, while a Feature-wise Linear Modulation (FiLM)-based fine-tuning scheme enhances test-time adaptation. Experiments on both synthetic and real radar datasets demonstrate that HyperNCMD achieves superior accuracy and robustness, achieving up to 10.5% reduction in per-point negative log-likelihood under dynamically varying conditions. These results highlight the potential of hypernetwork-driven CMD modeling for reliable radar perception in complex sensing environments. [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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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs18101541 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 30 StartPage: 1541 Subjects: – SubjectFull: Clutter (Radar) Type: general – SubjectFull: Multiple target tracking Type: general – SubjectFull: Probabilistic generative models Type: general – SubjectFull: Tracking radar Type: general – SubjectFull: Encoding Type: general Titles: – TitleFull: HyperNCMD: A Scene-Adaptive Clutter Measurement Density Estimator for Radar Tracking via Hypernetworks and Normalizing Flows. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Cao, Zongqing – PersonEntity: Name: NameFull: Yang, Jianchao – PersonEntity: Name: NameFull: Sun, Wang – PersonEntity: Name: NameFull: Lu, Xingyu – PersonEntity: Name: NameFull: Tan, Ke – PersonEntity: Name: NameFull: Dai, Zheng – PersonEntity: Name: NameFull: Yu, Wenchao – PersonEntity: Name: NameFull: Gu, Hong IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 10 Titles: – TitleFull: Remote Sensing Type: main |
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