摘要
对合成孔径雷达图像统计模型的准确分析直接关系到目标的检测性能。本文借鉴蚁群算法的进化思想,提出了一种应用于合成孔径雷达图像分布模型参数估计的求解算法。该算法用相关熵作为目标函数,用蚁群算法进行分布参数寻优。在算法中,每完成一次循环,都对各路径点的信息量进行更新,再根据信息量的大小来决定蚂蚁的移动方向。仿真结果表明,该算法可以得到较好的估计结果,具有很强的灵活性、适应性和稳定性。
The accuracy of estimating statistical parameters is related to the performance of the target detection. A new method based on ant colony algorithm （ACA） is proposed to estimate statistical parameters for synthetic aperture radar （SAR） images. The method extends the ACA to parameter estimation. In the system, the correlated entropy is used as an objective function, and a set of ants is cooperated to find the best solution through the interchange of the information contained in the pheromone deposits of the different routes. After the each cycle is done, the information will be updated according to the direction as the ants move. Experimental results show that the algorithm achieves accurate results to be effective, feasible and stable.
出处
《数据采集与处理》
CSCD
北大核心
2009年第1期78-81,共4页
Journal of Data Acquisition and Processing
基金
国防预研课题资助项目
关键词
合成孔径雷达
蚁群算法
参数估计
相关熵
synthetic aperture radar
ant colony algorithm
parameter estimation
correlatedentropy
