Airborne Bistatic Radar Clutter Suppression Based on Sparse Bayesian Learning
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摘要: 机载双基雷达杂波与构型有关且具有严重的距离依赖性，因此杂波脊复杂多变，独立同分布(IID)的样本很少。传统的空时自适应处理(STAP)方法受独立同分布样本数的限制，对机载双基雷达杂波的抑制性能有限。基于机载雷达杂波在角度-多普勒域分布的稀疏特性和稀疏贝叶斯学习(SBL)在稀疏信号重建方面的优势，该文将SBL算法应用于较为复杂的机载双基雷达双动模式下杂波抑制，该方法可以用少量训练单元杂波估计待测距离单元的杂波协方差矩阵(CCM)，然后进行空时自适应处理；同时，该算法不需要样本独立同分布，在双基双动模式下对杂波的抑制性能较好，仿真结果验证了算法的有效性。Abstract: Clutter of airborne bistatic radar is related to configuration and has serious range dependence characteristic, therefore the clutter ridge is complex and variable, and few Independent and Identically Distributed (IID) samples exist. As the result, the traditional Space-Time Adaptive Processing (STAP) has a degraded suppression performance for airborne bistatic radar clutter. Based on the sparsity of airborne radar clutter in the angle-Doppler domain and the advantages of Sparse Bayesian Learning (SBL) in sparse signal reconstruction, SBL algorithm is applied to the more complex airborne bistatic radar with both transmitter and receiver moving. The method can estimate the Clutter Covariance Matrix (CCM) of the unit under test with very few training samples, then perform space-time adaptive processing. Since the method does not need independent and identically distributed samples, it has better performance of clutter suppression in the airborne bistatic radar with both transmitter and receiver moving. Simulation results verify the effectiveness of the algorithm.
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表 1 MSBL算法流程
(1)初始化 $\beta $的合理值； (2)初始化一个基向量 ${{φ}_1}$，由 ${{C}} \;=\; {{Φ}} \,{{{Λ}} ^{ - 1}}{{{Φ}} ^{\rm{H}}} \,+\, \beta\, {{{I}}_{MN}}$计算 ${{{C}}_{ - 1}} = \beta {{{I}}_M}$，由 ${a_i} = {φ} _i^{\rm{H}}{{C}}_{ - i}^{ - 1}{{φ}_i}$, ${b_i} = {φ} _i^{\rm{H}}{{C}}_{ - i}^{ - 1}{{x}}$计算 ${a_1}$, ${b_1}$， 根据式(19)计算 ${\hat \alpha _1}$，得到更新后的 ${{Λ}} $； (3)计算均值 ${{μ}} $和方差 ${{Σ}}$，及所有基向量对应的 ${a_i}$, ${b_i}$； (4)如果 $b_i^2 \,>\, {a_i}$且 ${\hat \alpha _i} \,<\, \infty $，则按式(19)更新 ${\hat \alpha _i}$；如果 $b_i^2 \,>\, {a_i}$且 ${\hat \alpha _i} = \infty $，在模型中增加基向量[16,17]${{φ} _i}$，并按式(19)更新 ${\hat \alpha _i}$；如 果 $b_i^2 < {a_i}$且 ${\hat \alpha _i} < \infty $，在模型中删除原子向量 ${{φ} _i}$，并更新 ${\hat \alpha _i} = \infty $，更新 ${{Λ}}$； (5)由 ${{μ}} $, ${{Σ}}$更新 $\beta $； (6)由 ${\beta _0}$, ${{Λ}} $计算均值 ${{μ}} $和方差 ${{Σ}}$及所有基向量对应的 ${a_i}$, ${b_i}$； (7)判断是否收敛，收敛则算法结束，否则转到步骤(4)继续迭代。 
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表 2 系统仿真参数
符号 参数名 参数值 ${f_{\rm{c}}}$ 载频 1 GHz ${f_{{\rm{prf}}}}$ 脉冲重复频率 1200 Hz $d$ 天线阵元间隔 0.15 m $N$ 天线阵元数 16 $M$ 相干积累脉冲数 10 $L$ 基线长度 50 km ${R_{{\rm{st}}}}$ 目标单元双基距离和 70 km ${H_{\rm{T}}}$ 发射载机平台高度 2 km ${H_{\rm{R}}}$ 接收载机平台高度 5 km ${V_{\rm{T}}}$ 发射载机平台速度 100 m/s ${V_{\rm{R}}}$ 接收载机平台速度 80 m/s CNR 杂噪比 30 dB
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