Key Laboratory of Electromagnetic Space Information, Chinese Academy of Sciences, University of Science and Technology of China, Hefei 230026, China.
Sensors (Basel). 2019 Mar 3;19(5):1079. doi: 10.3390/s19051079.
Microwave staring correlated imaging (MSCI) can realize super resolution imaging without the limit of relative motion with the target. However, gain⁻phase errors generally exist in the multi-transmitter array, which results in imaging model mismatch and degrades the imaging performance considerably. In order to solve the problem of MSCI with gain⁻phase error in a large scene, a method of MSCI with strip-mode self-calibration of gain⁻phase errors is proposed. The method divides the whole imaging scene into multiple imaging strips, then the strip target scattering coefficient and the gain⁻phase errors are combined into a multi-parameter optimization problem that can be solved by alternate iteration, and the error estimation results of the previous strip can be carried into the next strip as the initial value. All strips are processed in multiple rounds, and the gain⁻phase error estimation results of the last strip can be taken as the initial value and substituted into the first strip for the correlated processing of the next round. Finally, the whole imaging in a large scene can be achieved by multi-strip image splicing. Numerical simulations validate its potential advantages to shorten the imaging time dramatically and improve the imaging and gain⁻phase error estimation performance.
微波凝视相关成像（MSCI）可以实现超分辨率成像，而不受与目标的相对运动的限制。然而，多发射阵列中通常存在增益-相位误差，这导致成像模型不匹配，并大大降低了成像性能。为了解决大场景下具有增益-相位误差的 MSCI 问题，提出了一种带模式自校正增益-相位误差的 MSCI 方法。该方法将整个成像场景分为多个成像带，然后将带目标散射系数和增益-相位误差合并为一个可以通过交替迭代求解的多参数优化问题，并且前一个带的误差估计结果可以作为下一个带的初始值。所有带都经过多轮处理，最后一个带的增益-相位误差估计结果可以作为初始值代入第一个带进行下一轮的相关处理。最后，通过多带图像拼接实现大场景的整体成像。数值模拟验证了其显著缩短成像时间和提高成像和增益-相位误差估计性能的潜在优势。
