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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >A Novel Radar HRRP Recognition Method with Accelerated T-Distributed Stochastic Neighbor Embedding and Density-Based Clustering
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A Novel Radar HRRP Recognition Method with Accelerated T-Distributed Stochastic Neighbor Embedding and Density-Based Clustering

机译：加速T分布随机邻域嵌入和基于密度聚类的雷达HRRP识别新方法

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摘要

High-resolution range profile (HRRP) has attracted intensive attention from radar community because it is easy to acquire and analyze. However, most of the conventional algorithms require the prior information of targets, and they cannot process a large number of samples in real time. In this paper, a novel HRRP recognition method is proposed to classify unlabeled samples automatically where the number of categories is unknown. Firstly, with the preprocessing of HRRPs, we adopt principal component analysis (PCA) for dimensionality reduction of data. Afterwards, t-distributed stochastic neighbor embedding (t-SNE) with Barnes–Hut approximation is conducted for the visualization of high-dimensional data. It proves to reduce the dimensionality, which has significantly improved the computation speed. Finally, it is exhibited that the recognition performance with density-based clustering is superior to conventional algorithms under the condition of large azimuth angle ranges and low signal-to-noise ratio (SNR).
机译：高分辨率距离剖面（HRRP）易于获取和分析，引起了雷达界的广泛关注。然而，大多数常规算法需要目标的先验信息，并且它们不能实时处理大量样本。本文提出了一种新的HRRP识别方法，该方法可以自动分类类别数量未知的未标记样本。首先，通过对HRRP进行预处理，我们采用主成分分析（PCA）来减少数据的维数。然后，使用Barnes-Hut近似进行t分布随机邻居嵌入（t-SNE），以可视化高维数据。事实证明，它降低了维数，从而显着提高了计算速度。最后，证明了在大方位角范围和低信噪比（SNR）的条件下，基于密度的聚类的识别性能优于传统算法。

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