Tamura Coarseness for OTH Radar Image Evaluation and Its Application in Adaptive Optimization of Interference Suppression
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摘要: 针对OTH雷达距离-多普勒(Range-Doppler, RD)图, 本文首次提出采用纹理粗糙度作为RD图质量的评价指标, 即计算RD图所转化灰度图的Tamura纹理粗糙度. 分析表明, 粗糙度指标能准确反映RD图受干扰情况, 对于不同灰度转换函数具有稳健性. 作为应用举例, 本文将图像粗糙度用于改进射频干扰抑制算法, 使干扰抑制达到自适应优化. 实验结果表明, Tamura粗糙度能够正确反映RD图干扰抑制情况, 优化粗糙度指标能够使干扰抑制自适应达到最优.Abstract: Tamura coarseness is introduced as a reasonable index for evaluating the quality of range-Doppler (RD) map in OTH radar. Tamura coarseness is calculated as the texture coarseness of a gray image which is transformed from the RD map. The analysis shows that Tamura coarseness can correctly reflect the radio frequency interference in the RD image and is also robust when the gray transforming function changes. As an example of its application, Tamura coarseness is adopted to improve the interference suppression algorithm, so that the suppression can be optimized adaptively. Simulation results show that Tamura coarseness can be used as a quality index of the RD image, which helps to suppress the interference perfectly.
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Key words:
- OTH radar /
- RD map /
- Tamura coarseness /
- Interference suppression
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图 2 不同干扰情形下的RD灰度图: (a)-(c)分别表示无动态范围转换的无干扰、有窄带射频干扰、有宽带射频干扰的RD灰度图; (d)-(f)分别表示对应的动态范围为80 dB转换的RD灰度图
Fig. 2 RD gray-scale images: (a)-(c) are images without dynamic range conversion, for no RFI, narrowband RFI, and wideband RFI, respectively; (d)-(f) are corresponding images with dynamic range conversion for 80 dB
图 3 不同干扰抑制结果的RD灰度图: (a)-(e)分别表示无动态范围转换的无干扰、有宽带干扰、干扰未完全抑制、干扰完全抑制、干扰过度抑制的RD灰度图; (f)-(j)分别表示对应的动态范围为80 dB转换的RD灰度图
Fig. 3 RD gray-scale image for various interference suppression results: (a)-(e) are images without interference, broadband RFI, incomplete suppression, complete suppression, and excessive suppression, without dynamic range conversion; (f)-(j) are the corresponding images with dynamic range conversion for 80 dB
图 4 在无干扰的海杂波数据中加入目标回波后, RD图中20个目标的距离-多普勒单元位置
Fig. 4 The position of 20 targets in the RD map without any RFI
图 6 基于不同
$ \varepsilon$ 参数下的RD灰度图与粗糙度Fig. 6 RD gray-scale image and coarseness for varying
$ \varepsilon$ 
图 8 不同
$ K$ 值下$ \varepsilon$ 变化时的$ k_{best}$ 比例Fig. 8 The ratio of
$ k_{best}$ versus$ \varepsilon$ for varying$ K$ 表 1 不同情况下RD图的粗糙度
Table 1 Coarseness of RD images in various cases
无动态转换时灰度级 有动态转换时灰度级 256 64 32 256 64 32 无干扰 6.92 6.92 6.91 6.84 6.84 6.83 窄带RFI 7.37 7.37 7.36 7.31 7.31 7.30 宽带RFI 8.58 8.58 8.55 8.58 8.58 8.56 
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表 2 不同情况下RD图的粗糙度
Table 2 Coarseness of RD images in various cases
无动态转换时灰度级 有动态转换时灰度级 256 64 32 256 64 32 无干扰 6.92 6.92 6.91 6.84 6.84 6.83 有干扰 8.58 8.58 8.55 8.58 8.58 8.56 未完全抑制 7.68 7.68 7.66 7.68 7.68 7.67 完全抑制 7.12 7.12 7.10 6.89 6.89 6.89 过度抑制 7.68 7.68 7.67 7.67 7.67 7.66
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表 3 不同目标情况下RD图的粗糙度
Table 3 Coarseness of RD images of various targets
目标个数 0 1 5 10 15 20 无干扰 6.84 6.84 6.85 6.86 6.87 6.89 有干扰 8.58 8.58 8.58 8.58 8.58 8.58 未完全抑制 7.68 7.68 7.69 7.69 7.69 7.70 完全抑制 6.89 6.89 6.90 6.91 6.92 6.94 过度抑制 7.67 7.67 7.69 7.69 7.70 7.71
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表 4 滤波器自适应优化算法
Table 4 Adaptive algorithm of filter optimization
步骤 操作流程 步1 设置滤波器参数 $\varepsilon$ 的优化步长为$\triangle\varepsilon,$ 阈值比率$\eta\!=\!0.01.$ 初始化迭代次数$j\!=\!0,$ 对应滤波器参数为$\varepsilon_0\!=\!1,$ 计算干扰噪声区域的粗糙度值$F_0.$ 步2 $j=j+1,\varepsilon_j=\varepsilon_{j-1}-\triangle\varepsilon,$ 根据$\varepsilon_j$ 设计滤波器并抑制干扰, 计算其RD灰度图的粗糙度$F_j.$ 步3 计算粗糙度差值 $\triangle F=F_{j-1}-F_j.$ 判断: 若$\triangle F/{F_{j-1}}<\eta,$ 取得最优抑制, 转至步4; 否则, 转至步2.步4 若 $\triangle F<0,\varepsilon_{opt}=\varepsilon_{j-1};$ 否则,$\varepsilon_{opt}=\varepsilon_{j}.$ 输出$\varepsilon_{opt}$ 对应最优滤波器和干扰抑制结果.
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