毫米波合成孔径技术利用了物体自身辐射电磁波的相位信息，采用很少阵元就能实现实时成像，但目前合成孔径技术只是限于亮温成像，缺少了目标的距离信息，这对于目标探测与识别很不利。提出在近程条件下合成孔径被动测距原理和相应的双正交傅立叶变换算法，并给出了近程距离分辨率公式，近程成像的特点是成像公式中存在二次相位因子，而且距离分辨率和距离的平方成反比。毫米波合成孔径被动成像可以同时得到物体辐射的亮温分布和距离分布，能够有效克服离焦引起的图像模糊，对图像的反降晰和目标识别具有重要意义。

The Particle Swarm Optimization (PSO) is an evolutionary computation, which not only can search solutions randomly and fully, but also is convenient to be carried out. Hence, the article focuses on the application of PSO to the multiple-objective coordination optimization of project, looking forward to seeking best solutions easily and quickly. After introducing the basic theory of the algorithms and its several versions, the article aims at the efficiency coefficient of quality, cost, time and resource subsystem, and set up a multiple optimization coordination model. In the following part, the article introduces how to apply PSO to solve the project coordination optimization problem in detail. The numeric example followed indicates that PSO can solve the multiple-objectives coordination optimization problem of project exactly and quickly.