Accurate Clutter Synthesis for Heterogeneous Textures and Dynamic Radar Environments

Kim, Dong-Hoon; Park, Andrew Junghoon; Suh, Ui-Suk; Goo, Dongwoo; Kim, Dong Hwan; Yoon, Boram; Ra, Won‐Sang; Kim, Sanghoek

doi:10.1109/taes.2022.3151585

Cited by 3 publications

(3 citation statements)

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“…A dataset of the association and matching between the clutter and environmental parameters was constructed by reconstructing the geometric relationship of the test scene, dividing the terrain, and matching the illuminated areas. Compared with previous clutter data [22,25], the dataset constructed in this study has the characteristics of wide coverage and many ground object parameters. This dataset overcomes the problem of quantitative parameter representation in the study of large-scene clutter characteristics and enables a better analysis and understanding.…”

Section: Discussionmentioning

confidence: 99%

“…Luo et al [24] proposed an optimal searching strategy based on DEMs to suppress ground clutter and achieve a fast and high-probability search for low-altitude targets. Kim et al [25] proposed a method for synthesizing clutter based on an empirical model by creating and accumulating clutter block by block to predict clutter in unexplored environments and operational conditions.…”

Section: Introductionmentioning

confidence: 99%

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Estimating Scattering Coefficient in a Large and Complex Terrain through Multifactor Association

Zhao,

Zhang,

Zhu

et al. 2024

Remote Sensing

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Complex-terrain clutter presents serious nonuniformity, which has a significant impact on radar target detection, communication, and navigation. The accurate acquisition of the clutter characteristics by measurement or calculation for large and complex terrains has always posed a challenge due to the high costs of the measurement, as well as the intricate and diverse environmental factors. To address this challenge, we proposed a research methodology that leverages the similarity of multidimensional terrain features to infer the clutter characteristics of unmeasured regions, particularly those that are difficult or impossible to measure directly. In order to realize this study object, we constructed a dataset consisting of multidimensional environmental and clutter data to quantitatively characterize the complex environmental information in a vast territory. Within the dataset, we selected two regions with similar terrain characteristics: one region served as the source data for mining and analyzing features, while the other was designated as the target data region for method validation. Through the application of prior-knowledge-based classification and multifactor weight analysis on the dataset, two novel estimation techniques were devised. The first method, designated as PCKRF, blended prior-knowledge classification, weighted K-means clustering, and the random forest (RF) algorithm; and the second method, labeled PCKMW, integrated prior-knowledge classification, weighted K-means clustering, and the minimum weighted distance (MW) approach. In estimating and validating the clutter data from the source region to the target region, the performances of both the PCKRF and PCKMW methods were notably superior to those of the RF, MW, and K-means minimum weighting (KMW). Specifically, the root-mean-squared error (RMSE) was enhanced from a range of 7 dB–10 dB to a range of 4 dB–6 dB, while the determination coefficient (R2) was increased from a range of −1.15–0.09 to a range of 0.25–0.66. The above demonstration illustrates that the current achievements in the clutter estimation methods offer a viable option for accurately recognizing clutter characteristics in complex-terrain environments where comprehensive data collection may be difficult or impossible, with lower human and economic costs.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating Scattering Coefficient in a Large and Complex Terrain through Multifactor Association

Zhao,

Zhang,

Zhu

et al. 2024

Remote Sensing

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“…Variable and constant value of formula ( 8) and (9). [4] . Kulemin에 의하면 normalized RCS   는 다음과 같은 파워 스펙트럼 분포를 갖는다.…”

Section: 낼 때unclassified

Characteristics of Land Clutter Signals and Their Synthesis Methods

Kim¹,

Kim²

2023

J. Korean Inst. Electromagn. Eng. Sci.

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Clutter signals are undesired for radar detection. Clutter signals can be categorized as volume, land, and sea clutter. These unwanted signals disturb radar by detecting the target. To increase detection accuracy, the radar must be able to differentiate clutter signals from the target and remove cluttering signals. Therefore, it is important to understand the characteristics of clutter signals. In this paper, we review the characteristics of land clutter signals and the methods used to generate them.

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6G Integrated Sensing and Communications Channel Modeling: Challenges and Opportunities

Liu,

Guan,

et al. 2024

IEEE Veh. Technol. Mag.

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Accurate Clutter Synthesis for Heterogeneous Textures and Dynamic Radar Environments

Cited by 3 publications

References 34 publications

Estimating Scattering Coefficient in a Large and Complex Terrain through Multifactor Association

Estimating Scattering Coefficient in a Large and Complex Terrain through Multifactor Association

Characteristics of Land Clutter Signals and Their Synthesis Methods

6G Integrated Sensing and Communications Channel Modeling: Challenges and Opportunities

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