Active False Target Clustering Identification Method Based on Frequency Response Features in Multi-Coherent Processing Intervals
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摘要: 现有真-假目标智能识别算法大多基于监督学习，且在低信噪比条件下表现不好。针对上述问题，该文分别利用真、假目标在多个相参处理间隔(CPIs)内散射特性的时变性和唯一性，提出一种多相参处理间隔频响特征聚类的真、假目标无监督鉴别方法。首先，在快-慢时域中沿快时间维度对真、假目标进行加窗截断，提取快-慢时间域频率响应特征用于构建初步样本集；然后，通过Agglomerative聚类和特征融合网络组成的两步识别算法对真-假目标进行识别；最后，提出一种多相参处理间隔联合决策方法提升识别性能和可靠性。经仿真和实测数据检验，证明了所提方法可实现真实目标和多种有源假目标的有效分离。
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关键词:
- 有源假目标 /
- 多相参处理间隔 /
- 散射特性 /
- 快-慢时间域频率响应 /
- 无监督 /
- Agglomerative聚类
Abstract: Most of the existing intelligent algorithms for identifying real and false targets are based on supervised learning and perform poorly under a low signal-to-noise ratio. Considering the above problems, an unsupervised clustering identification method of real and false targets based on frequency response features in multi-Coherent Processing Intervals(CPIs) is proposed by using the variability and uniqueness of the scattering characteristics of real and false targets in multi-CPIs, respectively. Firstly, the real and false targets are windowed and truncated along the fast time dimension in the fast-slow time domain, and the fast-slow time domain frequency response features are extracted to construct a preliminary sample set. Then, the real and false targets are identified by a two-step recognition algorithm composed of an Agglomerative clustering and a feature fusion network. Finally, a multi-CPI joint decision method is proposed to improve the recognition performance and reliability. It is proved by simulation and measured data that the proposed method can effectively identify real targets and multiple active false targets. -
表 1 仿真参数
参数 取值 参数 取值 载频 10 GHz 脉冲重复频率 3 kHz 脉宽 70${\text{ μs}}$ 相参累积脉冲数量 64 带宽 25 MHz 矩形窗口长度 128 采样频率 60 MHz 截断后FFT点数 256 真实目标距离 10.5 km 真实目标速度 18 m/s 假目标个数 4 假目标距离 真实目标附近2 km 
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表 2 1DCNN-LSTM参数
上通道 卷积核大小 激活函数 池化 卷积层1 8 × 1 × 4 ReLU 2 × 1 卷积层2 8 × 1 × 4 ReLU 2 × 1 卷积层3 8 × 1 × 4 ReLU 2 × 1
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表 3 实测数据实验参数
参数 取值 参数 取值 载频 10 GHz 脉冲重复频率 3 kHz 脉宽 60${\text{ μs}}$ 相参累积脉冲数量 64 带宽 30 MHz 真实目标距离 8.5 km 采样频率 80 MHz 假目标距离 真实目标附近1 km
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表 5 平均识别率与文献[14]对比结果(%)
信噪比(dB) 本文方法 文献[14]方法 –12 99.2 53.6 –11 99.4 65.2 –10 99.3 80.5 –9 99.7 90.3 –8 99.8 95.8 –7 100.0 98.7 –6 100.0 99.7
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