基于多任务贝叶斯压缩感知的稀疏可重构天线阵的优化设计

Optimal Design of Sparse Reconfigurable Antenna Array Based on Multitask Bayesian Compressed Sensing

建立方向图可重构天线的联合稀疏模型,基于多任务贝叶斯压缩感知理论提出一种稀疏可重构天线阵的优化设计方法.该方法在实现方向图精确重构的同时可以大幅减少天线数量,节省平台空间,降低设计成本.首先基于多任务贝叶斯压缩感知理论建立多目标方向图的稀疏优化模型,根据权值向量的先验概率分布,利用快速相关向量机估计超参数的最大后验概率来得到多组阵元位置及其激励,实时改变激励以获得不同方向图的稀疏逼近.仿真验证了该方法能够以较少的阵元个数和较高的方向图拟合精度快速实现方向图重构.

In light of the equivalent joint sparse learning model,an effective method based on multitask Bayesian compressed sensing（ MT-BCS） is presented for the design of pattern reconfigurable antenna arrays. The method can dynamically reconfigure arbitrary radiation patterns with the exact pattern details and as fewer number of antenna elements as possible. Firstly,the sparse learning model of multiple reference patterns is built based on MT-BCS theory and priori assumption about the priori probability of weight vectors. Then fast relevance vector machine（ RVM） is exploited to estimate maximum posterior probability of hyper-parameter and further to obtain array optimizing positions and excitations. By varying excitations and optimized element positions,different patterns with desired and precise particulars can be achieved. Simulation results validate the efficiency of the proposed method for the design of maximally sparse reconfigurable antenna.