基于交替下降条件梯度的前视成像

基于网格的压缩感知(compressive sensing, CS)算法存在格点失配问题, 在分辨力不足的情况下容易产生伪影。而无网格的CS算法常用于二维谐波估计问题, 不适用于存在交叉项等复杂信号模型。对此, 提出一种基于交替下降条件梯度的前视成像算法。所提算法每次迭代首先获得散射点参数的粗估计, 并更新参数集合, 然后对更新的参数集合进行梯度下降, 获得参数集合的精细估计, 实现了在复杂信号模型下连续参数的二维高分辨成像。仿真实验说明了所提算法的优越性与有效性。

Forward-looking imaging based on alternating descent conditional gradient

The grid-based compressive sensing (CS) algorithm suffers from grid mismatch and is prone to artifacts in the case of insufficient resolution. The gridless CS algorithm is commonly used in two-dimensional harmonic estimation problems and is not suitable for complex signal models such as cross terms. In this regard, an alternating descent conditional gradient forward-looking imaging algorithm is proposed. The proposed algorithm firstly obtains a coarse estimate of the scattering point parameters with each iteration, and updates the parameter set. And then, the proposed algorithm performs gradient descent on the updated parameter set to obtain a fine estimate of the parameter set, realizing two-dimensional high-resolution imaging with continuous parameters under complex signal models. Simulation experiments illustrate the superiority and effectiveness of the proposed algorithm.