传统捷变频成像方法具有高旁瓣、低分辨率的缺点。鉴于捷变频ISAR回波信号的稀疏性,该文基于原始数据的2维压缩感知方案,在贝叶斯原理框架下,用稀疏贝叶斯算法方差成分扩张压缩方法(Ex Co V)实现捷变频ISAR像的重建。贝叶斯框架下的稀...传统捷变频成像方法具有高旁瓣、低分辨率的缺点。鉴于捷变频ISAR回波信号的稀疏性,该文基于原始数据的2维压缩感知方案,在贝叶斯原理框架下,用稀疏贝叶斯算法方差成分扩张压缩方法(Ex Co V)实现捷变频ISAR像的重建。贝叶斯框架下的稀疏重构算法考虑了稀疏信号的先验信息以及测量过程中的加性噪声,因而能够更好地重建目标系数。作为一种新的稀疏贝叶斯算法,Ex Co V不同于稀疏贝叶斯学习(SBL)算法中赋予所有的信号元素各自的方差分量参数,Ex Co V方法仅仅赋予有重要意义的信号元素不同的方差分量,并拥有比SBL方法更少的参数,克服了SBL算法参数多时效性差的缺点。仿真结果表明,该方法能克服传统捷变频成像缺点,并能够实现低信噪比条件下的2维高精度成像。展开更多
In view of the shortcomings of traditional Bayesian network(BN)structure learning algorithm,such as low efficiency,premature algorithm and poor learning effect,the intelligent algorithm of cuckoo search(CS)and particl...In view of the shortcomings of traditional Bayesian network(BN)structure learning algorithm,such as low efficiency,premature algorithm and poor learning effect,the intelligent algorithm of cuckoo search(CS)and particle swarm optimization(PSO)is selected.Combined with the characteristics of BN structure,a BN structure learning algorithm of CS-PSO is proposed.Firstly,the CS algorithm is improved from the following three aspects:the maximum spanning tree is used to guide the initialization direction of the CS algorithm,the fitness of the solution is used to adjust the optimization and abandoning process of the solution,and PSO algorithm is used to update the position of the CS algorithm.Secondly,according to the structure characteristics of BN,the CS-PSO algorithm is applied to the structure learning of BN.Finally,chest clinic,credit and car diagnosis classic network are utilized as the simulation model,and the modeling and simulation comparison of greedy algorithm,K2 algorithm,CS algorithm and CS-PSO algorithm are carried out.The results show that the CS-PSO algorithm has fast convergence speed,high convergence accuracy and good stability in the structure learning of BN,and it can get the accurate BN structure model faster and better.展开更多
文摘传统捷变频成像方法具有高旁瓣、低分辨率的缺点。鉴于捷变频ISAR回波信号的稀疏性,该文基于原始数据的2维压缩感知方案,在贝叶斯原理框架下,用稀疏贝叶斯算法方差成分扩张压缩方法(Ex Co V)实现捷变频ISAR像的重建。贝叶斯框架下的稀疏重构算法考虑了稀疏信号的先验信息以及测量过程中的加性噪声,因而能够更好地重建目标系数。作为一种新的稀疏贝叶斯算法,Ex Co V不同于稀疏贝叶斯学习(SBL)算法中赋予所有的信号元素各自的方差分量参数,Ex Co V方法仅仅赋予有重要意义的信号元素不同的方差分量,并拥有比SBL方法更少的参数,克服了SBL算法参数多时效性差的缺点。仿真结果表明,该方法能克服传统捷变频成像缺点,并能够实现低信噪比条件下的2维高精度成像。
基金National Natural Science Foundation of China(Nos.61164010,61233003)。
文摘In view of the shortcomings of traditional Bayesian network(BN)structure learning algorithm,such as low efficiency,premature algorithm and poor learning effect,the intelligent algorithm of cuckoo search(CS)and particle swarm optimization(PSO)is selected.Combined with the characteristics of BN structure,a BN structure learning algorithm of CS-PSO is proposed.Firstly,the CS algorithm is improved from the following three aspects:the maximum spanning tree is used to guide the initialization direction of the CS algorithm,the fitness of the solution is used to adjust the optimization and abandoning process of the solution,and PSO algorithm is used to update the position of the CS algorithm.Secondly,according to the structure characteristics of BN,the CS-PSO algorithm is applied to the structure learning of BN.Finally,chest clinic,credit and car diagnosis classic network are utilized as the simulation model,and the modeling and simulation comparison of greedy algorithm,K2 algorithm,CS algorithm and CS-PSO algorithm are carried out.The results show that the CS-PSO algorithm has fast convergence speed,high convergence accuracy and good stability in the structure learning of BN,and it can get the accurate BN structure model faster and better.
