基于关联计算的相控阵雷达前视成像技术

基于关联计算成像原理, 提出一种适用于相控阵雷达的前视成像技术, 其随机移相是由配置二维相控阵雷达来实现的.为了模拟经典量子关联成像中的随机涨落光场以实现测量的不相关性, 需要控制二维相控阵辐射出的波前呈现随机幅相波动特性, 再结合压缩感知(compressive sensing, CS)的模型框架与稀疏贝叶斯学习(sparse Bayesian learning, SBL)算法解决关联耦合问题以实现目标场景的方向维和俯仰维超分辨成像。甚至可以在不需要雷达与目标相对运动的情况下, 结合宽带信号体制实现雷达前视超分辨三维成像。仿真实验和实测数据验证了其原理的正确性和算法的有效性。

Phased array radar forward-looking imaging based on correlated imaging

Based on the principle of correlated computing imaging, a forward-looking imaging technology for phased array radar is proposed. The random phase shift is realized by configuring two dimensional phased array radar. In order to simulate the random fluctuation light field in classical quantum correlation imaging and realize the measurement uncorrelation, it is necessary to control the wavefront radiated by two-dimensional phased array to exhibit random amplitude and phase fluctuation characteristics, and then combine the compressed sensing (CS) model framework with sparse Bayesian learning (SBL), SBL algorithm solves the problem of correlation coupling to realize the super resolution imaging in the direction and elevation dimensions of the target scene. Even without the relative motion between the radar and the target, the forward-looking super resolution three dimensional imaging can be realized by combining the wideband signal system. The correctness of the principle and the effectiveness of the algorithm are verified by simulation experiments and measured data.