Clutter Suppression of Wind Farm Based on Sparse Reconstruction and Morphological Component Analysis for ATC Radar under Short Coherent Processing Interval Condition
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摘要: 近些年来，世界各国越来越重视风力发电的发展。风电场的存在可能对航管监视雷达性能产生负面影响，因此风电场杂波抑制技术的研究对于提升航管监视雷达工作性能、保障空中交通安全具有重大意义。形态成分分析(MCA)算法根据信号稀疏特征的不同应用于风电场杂波抑制时，计算量较低且性能较好。但是针对实际雷达参数中相参处理间隔(CPI)较短造成的谱分辨率降低及信号特征不明显时，MCA算法的杂波抑制性能受到影响，因此选择将稀疏重构算法与MCA算法结合用于短CPI情况下的风电场杂波抑制。该文认为短CPI接收回波数据为较长CPI雷达回波数据基础上发生尾部数据缺省，继而利用稀疏重构算法对缺省数据进行恢复，再利用MCA算法抑制风电场杂波。实验结果验证了该方法的有效性。Abstract: In recent years, countries around the world have paid more and more attention to the development of wind power. The existence of wind farms may have a negative impact on the performance of air traffic control surveillance radars. Therefore, the research on the clutter suppression technology of wind farms is of great significance to improve the work performance of air traffic control surveillance radars and ensure the safety of air traffic. When the Morphological Component Analysis(MCA)algorithm is applied to the wind farm clutter suppression based on the difference of sparse characteristics for the signals, the calculation burden is lower and the performance is better. However, the clutter suppression performance of the MCA algorithm is affected when the spectral resolution is reduced due to the short Coherent Processing Interval(CPI)and the signal characteristics are not obvious. Therefore, the sparse reconstruction algorithm and the MCA algorithm are combined to suppress the clutter in the wind farm with a small number of coherent pulses. It is considered that the short CPI received echo data is the default of tail data on the basis of the longer CPI radar echo data, and then the sparse reconstruction algorithm is used to recover the default data, and the MCA algorithm is used to suppress wind farm clutter. The experimental results verify the effectiveness of the proposed method.
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表 1 ALM迭代求解算法
初始化：$\mu > 0,d$ 迭代优化： ${w_1} \leftarrow \mathop {\arg \min }\limits_{{w_1}} \lambda {\left\| {{w_1}} \right\|_1} + 1/2 \cdot \mu \left\| {{w_1} - {x_1} - d} \right\|_2^2$ (8) ${x_1} \leftarrow \mathop {\arg \min }\limits_{ {x_1} } \left\| { {w_1} - {x_1} - d} \right\|_2^2\ \ {\rm{s.t} }.{Y_1} = {{{S}}_1}{{A}}{x_1}$ (9) $d \leftarrow d - ({w_1} - {x_1})$ 直到收敛结束 
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表 2 对应化简求解算法
初始化：$\mu > 0,d$ 迭代优化： ${w_1} \leftarrow {\rm{soft}}({x_1} + d,\lambda /\mu )$ (10) ${x_1} \!\!\leftarrow\!\! ({w_1} \!\!-\! d) \!\!+\! {\left(\! { { {{S} }_1}{{A} } } \right)^{\rm{H} } }\!{\left[\! { { {{S} }_1}{{A} }{ {({ {{S} }_1}{{A} })}^{\rm{H} } } } \right]^{ - 1} }\!\!\left[ { {Y_1} \!-\! { {{S} }_1}{{A} }({w_1} \!-\! d)} \right]$ (11) $d \leftarrow d - ({w_1} - {x_1})$ 直到收敛结束
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表 3 最终求解算法
初始化：$\mu > 0,d$ 迭代优化： ${v_1} \leftarrow {\rm{soft}}({x_1} + d,\lambda /\mu ) - d$ (13) $d \leftarrow \dfrac{1}{p}{ {{A} }^{\rm{H} } }\left( { {Y_1} - {{{S}}_1}{{A} }{v_1} } \right)$ (14) ${x_1} \leftarrow {v_1} + d$ 直到收敛结束
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表 4 雷达参数
雷达参数 数值 载频 2.9 GHz 脉冲重复间隔(PRI) 3000 μs 带宽 0.75 MHz 脉冲宽度 270 μs 方位扫描间隔 0.7° 相干脉冲数 6 信噪比(SNR) 10 dB
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