Knowledge-aided space-time adaptive processing based on MORCHIN model

Xie, Mingchi; Yi, Wei; Kong, Lingjiang

doi:10.1049/cp.2015.1268

Cited by 5 publications

(1 citation statement)

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“…Based on their research results and methods, more research has been conducted on complex-terrain clutter simulations using digital elevation and traditional models in the last decade. Xie et al [21] proposed an improved knowledge-assisted algorithm based on the Morchin clutter model. Li et al [22] proposed a method based on DEMs and digital land feature classification data termed the nonuniform ground clutter simulation method, which selects different scattering models according to distinctive features to improve accuracy.…”

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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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: Introductionmentioning

confidence: 99%