基于稀疏重构的光学传感器扩展目标量测划分（英文）

Partition Algorithm for Extended Targets in Optical Sensor Using Sparse Reconstruction

提出一种稀疏重构框架下利用幅度实现扩展目标量测划分的方法.利用衍射受限光学系统特性对像平面进行网格采样,建立稀疏重构模型及"超完备字典".通过重构挖掘像元幅度值中的有效信息并基于成像机理对重构出的非目标量测进行抑制处理,利用重构出的亚像元级目标位置、幅度信息实现目标量测的划分.仿真结果表明:信噪比为6dB时,本文算法比传统方法在实现对所有扩展目标量测的正确划分上提前30s,对目标探测信息能充分利用,在量测划分准确性上比依靠距离划分的传统方法有较大提高,尤其在低信噪比条件下较传统方法量测划分的准确性提升明显.

A partition algorithm method with sparse reconstruction was proposed to partition measurements making full use of the information of the extended targets.The grid sampling of the image plane was carried out by using diffraction limited optical system′s features,a sparse reconstruction model was set up with a＂super complete dictionary＂.Effective information in the amplitude of every pixel was extracted by sparse reconstruction,non-target measurement was eliminated by physical features.Partitioning measurements was realized by using the reconstructed sub-pixel-level target location and amplitude information.Simulation results indicate that the proposed method correctly partitions all the targets as separate measurements about 30 searlier than traditional distance-based partitioning method when signal noise ratio is 6dB.Due to the full and effective use of target information,the proposed method outperforms the traditional distance-based partitioning methods in terms of the partitioning results′accuracy,especially in the cases of poor signal noise ratio.