A novel method of deinterleaving radar pulse sequences based on a modified DBSCAN algorithm

Dadgarnia, Abolfazl; Sadeghi, Mohammad Taghi

doi:10.23919/jcc.2023.02.012

Cited by 6 publications

(1 citation statement)

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“…Due to its many advantages, DBSCAN continues to be widely used in various clustering problems to this day. However, the performance of the DBSCAN model heavily relies on how the parameters ( , MinPts) are set [28]. Thus, selecting optimal parameter values is still an ongoing task for researchers and engineers, to achieve optimal clustering performance with DBSCAN.…”

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confidence: 99%

A Novel Batch Streaming Pipeline for Radar Emitter Classification

Park,

Seo,

Baek

et al. 2023

Applied Sciences

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In electronic warfare, radar emitter classification plays a crucial role in identifying threats in complex radar signal environments. Traditionally, this has been achieved using heuristic-based methods and handcrafted features. However, these methods struggle to adapt to the complexities of modern combat environments and varying radar signal characteristics. To address these challenges, this paper introduces a novel batch streaming pipeline for radar emitter classification. Our pipeline consists of two key components: radar deinterleaving and radar pattern recognition. We leveraged the DBSCAN algorithm and an RNN encoder, which are relatively light and simple models, considering the limited hardware resource environment of a military weapon system. Although we chose to utilize lightweight machine learning and deep learning models, we designed our pipeline to perform optimally through hyperparameter optimization of each component. We demonstrate the effectiveness of our proposed model and pipeline through experimental validation and analysis. Overall, this paper provides background knowledge on each model, introduces the proposed pipeline, and presents experimental results.

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confidence: 99%

A Novel Batch Streaming Pipeline for Radar Emitter Classification

Park,

Seo,

Baek

et al. 2023

Applied Sciences

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Improved radar signal sorting algorithm based on density-peak-clustering approach

Ren,

Deng,

Xie

et al. 2024

J. Phys.: Conf. Ser.

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This paper proposes improved radar pulse sorting signals by the Density-Peak (DP) algorithm. In this study, we propose finding a density threshold within the entire density interval to distinguish radar pulse signals and noise pulse signals, leading to higher sorting accuracy. Through MATLAB simulations, we compared the performance of the improved DP algorithm with the original Density Peak algorithm, as well as the traditional K-means and FCM algorithms. Our results verify that the proposed DP algorithm outperforms these other methods by sorting accuracy and Adjusted Rand Index (ARI). The accuracy of this work can reach 98.30%, and the ARI of this work can reach 0.9627, which verifies excellent performance.

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