Convolutional Neural Network STAP Low Level Wind Shear Wind Speed Estimation
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摘要: 由于机载气象雷达前视阵下存在非均匀性地杂波，导致难以获得足够的独立同分布样本，影响杂波协方差矩阵准确估计，进而影响风速估计。对此，该文提出一种基于卷积神经网络STAP的低空风切变风速估计方法，通过少量样本就能够实现高分辨杂波空时谱估计。首先，基于卷积神经网络模型训练好高分辨杂波空时谱卷积神经网络，接着计算杂波协方差矩阵，进而计算卷积神经网络STAP最优权矢量进行杂波抑制，达到对低空风切变风速精确估计。该文在小样本情况下，将稀疏恢复问题通过卷积神经网络实现，完成对高分辨杂波空时谱有效估计，仿真实验结果表明该方法可以有效估计空时谱，并完成风速估计。Abstract: Due to the non-uniform ground clutter in the forward array of airborne weather radar, it is difficult to obtain enough independent and equally distributed samples, which affects the accurate estimation of clutter covariance matrix and wind speed estimation. In this paper, a novel estimation method of low altitude wind shear speed based on convolutional neural network STAP is proposed, which can realize high resolution clutter space-time spectrum estimation with a small number of samples. First, the high-resolution clutter space-time spectrum convolutional neural network is trained based on the convolutional neural network model, and then the clutter covariance matrix is calculated, and then the optimal weight vector of the convolutional neural network STAP is calculated for clutter suppression, so as to accurately estimate the wind shear speed at low altitude. The sparse recovery problem is realized by convolutional neural network in the case of small samples, and the space-time spectrum of high-resolution clutter is effectively estimated. The simulation results show that the proposed method can effectively estimate the space-time spectrum and complete the wind speed estimation.
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Key words:
- Airborne weather radar /
- CNN /
- Low-altitude windshear /
- Wind speed estimation
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表 1 网络结构参数
网络层 卷积核 输出通道 填充方式 激活函数 Conv2D $11 \times 11$ 16 SAME ReLU Conv2D $9 \times 9$ 8 SAME ReLU Conv2D $7 \times 7$ 4 SAME ReLU Conv2D $5 \times 5$ 2 SAME ReLU Conv2D $3 \times 3$ 1 SAME ReLU 
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表 3 雷达仿真具体参数
参数 参数值 参数 参数值 载机高度(m) 600 阵元数 8 波长(m) 0.05 相干脉冲数 64 脉冲重复频率(Hz) 7000 杂噪比(dB) 30～60 载机速度(m/s) 75 信噪比(dB) 5 距离分辨率(m) 150 主瓣角度(°) (90,0)
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表 4 不同方法误差比较
方法 均方根误差(m/s) STAP 29.3629 OMP STAP 14.9378 SBL STAP 11.0052 Focuss STAP 2.1438 卷积神经网络STAP 1.1618
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表 5 不同方法运算复杂度对比
方法 运算复杂度 STAP $O(2NM{({N_{\rm s}}{N_{\rm d}})^3})$ OMP STAP $O((NM{N_{\rm s}}{N_{\rm d}}) + {(NM)^3} + {(NM)^3}{N_{\rm s}}{N_{\rm d}} + 2NM{({N_{\rm s}}{N_{\rm d}})^2}{k_{{\mathrm{OMP}}}})$ SBL STAP $ O((NM{N_{\rm s}}{N_{\rm d}}) + {(NM)^3} + 3{(NM)^3}{N_{\rm s}}{N_{\rm d}} + 2NM{({N_{\rm s}}{N_{\rm d}})^2}{k_{{\mathrm{SBL}}}}) $ Focuss STAP $O((NM{N_{\rm s}}{N_{\rm d}}) + {(NM)^3} + 2{(NM)^2}{N_{{\mathrm{s}}}}{N_{{\mathrm{d}}}} + NM{({N_{\rm s}}{N_{\rm d}})^2}{k_{{{\mathrm{Focuss}}}}})$ 卷积神经网络STAP $O(28777{N_{{\mathrm{s}}}}{N_{{\mathrm{d}}}})$
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