For the unsorted database quantum search with the unknown fraction λ of target items, there are mainly two kinds of methods, i.e., fixed-point and trail-and-error.(i) In terms of the fixed-point method, Yoder et al. ...For the unsorted database quantum search with the unknown fraction λ of target items, there are mainly two kinds of methods, i.e., fixed-point and trail-and-error.(i) In terms of the fixed-point method, Yoder et al. [Phys. Rev. Lett.113 210501(2014)] claimed that the quadratic speedup over classical algorithms has been achieved. However, in this paper, we point out that this is not the case, because the query complexity of Yoder’s algorithm is actually in O(1/λ01/2)rather than O(1/λ1/2), where λ0 is a known lower bound of λ.(ii) In terms of the trail-and-error method, currently the algorithm without randomness has to take more than 1 times queries or iterations than the algorithm with randomly selected parameters. For the above problems, we provide the first hybrid quantum search algorithm based on the fixed-point and trail-and-error methods, where the matched multiphase Grover operations are trialed multiple times and the number of iterations increases exponentially along with the number of trials. The upper bound of expected queries as well as the optimal parameters are derived. Compared with Yoder’s algorithm, the query complexity of our algorithm indeed achieves the optimal scaling in λ for quantum search, which reconfirms the practicality of the fixed-point method. In addition, our algorithm also does not contain randomness, and compared with the existing deterministic algorithm, the query complexity can be reduced by about 1/3. Our work provides a new idea for the research on fixed-point and trial-and-error quantum search.展开更多
Based on the distance of interval numbers and the two-stage decision methods, this paper expands the decision model of grey target into some situation under which the decision information and target weights are the in...Based on the distance of interval numbers and the two-stage decision methods, this paper expands the decision model of grey target into some situation under which the decision information and target weights are the interval numbers at the same time. It also gives the optimization method of weights in the grey target. We get the optimum coordinated vector utilizing the combination assigning method, based on the local optimization of various schemes. So it can shift the weights of interval number into real number form and sequence it according to the weighted off-target distance. Finally the effectiveness and practicality of the model is proved by a real project.展开更多
Target recognition performance can be affected by radar waveform parameters.In this paper,we established rigorous relationship between target recognition efficiency and the parameters of a repeatedly transmitted wavef...Target recognition performance can be affected by radar waveform parameters.In this paper,we established rigorous relationship between target recognition efficiency and the parameters of a repeatedly transmitted waveform.It is based on Kullback-Leibler Information Number of single observation(KLINs),which measures the dissimilarity between targets depicted by a range-velocity double spread density function in frequency domain.We considered two signal models which are different in the coherence of the observations.The method we proposed takes advantage of the methodology of sequential hypothesis test,and then the recognition performance in terms of correct classification rate is expressed by Receiver Operating Characteristic(ROC).Simulation results about the parameters of LFM signal show the validity of the method.展开更多
为提升反辐射导引头抗干扰能力,提出一种利用辐射源到达角(Direction of Arrival,DOA)信息和K-means的聚类算法。该方法利用角度网格完成测向数据图形化,通过对当前图形斑块和理想条件下单目标斑块特征参数进行比对,实现斑块内目标数量...为提升反辐射导引头抗干扰能力,提出一种利用辐射源到达角(Direction of Arrival,DOA)信息和K-means的聚类算法。该方法利用角度网格完成测向数据图形化,通过对当前图形斑块和理想条件下单目标斑块特征参数进行比对,实现斑块内目标数量自动估计,并借助K-means算法完成目标角度估计。经仿真验证,该方法能够在目标数量未知条件下,实现目标聚类,且能够适应多个辐射源目标彼此靠近,信号脉冲测向分布彼此重叠的情况,聚类后的估计角度误差不大于系统固有误差。展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11504430 and 61502526)the National Basic Research Program of China(Grant No.2013CB338002)
文摘For the unsorted database quantum search with the unknown fraction λ of target items, there are mainly two kinds of methods, i.e., fixed-point and trail-and-error.(i) In terms of the fixed-point method, Yoder et al. [Phys. Rev. Lett.113 210501(2014)] claimed that the quadratic speedup over classical algorithms has been achieved. However, in this paper, we point out that this is not the case, because the query complexity of Yoder’s algorithm is actually in O(1/λ01/2)rather than O(1/λ1/2), where λ0 is a known lower bound of λ.(ii) In terms of the trail-and-error method, currently the algorithm without randomness has to take more than 1 times queries or iterations than the algorithm with randomly selected parameters. For the above problems, we provide the first hybrid quantum search algorithm based on the fixed-point and trail-and-error methods, where the matched multiphase Grover operations are trialed multiple times and the number of iterations increases exponentially along with the number of trials. The upper bound of expected queries as well as the optimal parameters are derived. Compared with Yoder’s algorithm, the query complexity of our algorithm indeed achieves the optimal scaling in λ for quantum search, which reconfirms the practicality of the fixed-point method. In addition, our algorithm also does not contain randomness, and compared with the existing deterministic algorithm, the query complexity can be reduced by about 1/3. Our work provides a new idea for the research on fixed-point and trial-and-error quantum search.
基金supported by the National Natural Science Foundation for Young Scholar of China(70901040)the Doctoral Fund of Ministry of Education of China(200802870020)the Nanjing University of Aeronautics and Astronautics Innovation Foundation(Y0811-091).
文摘Based on the distance of interval numbers and the two-stage decision methods, this paper expands the decision model of grey target into some situation under which the decision information and target weights are the interval numbers at the same time. It also gives the optimization method of weights in the grey target. We get the optimum coordinated vector utilizing the combination assigning method, based on the local optimization of various schemes. So it can shift the weights of interval number into real number form and sequence it according to the weighted off-target distance. Finally the effectiveness and practicality of the model is proved by a real project.
文摘Target recognition performance can be affected by radar waveform parameters.In this paper,we established rigorous relationship between target recognition efficiency and the parameters of a repeatedly transmitted waveform.It is based on Kullback-Leibler Information Number of single observation(KLINs),which measures the dissimilarity between targets depicted by a range-velocity double spread density function in frequency domain.We considered two signal models which are different in the coherence of the observations.The method we proposed takes advantage of the methodology of sequential hypothesis test,and then the recognition performance in terms of correct classification rate is expressed by Receiver Operating Characteristic(ROC).Simulation results about the parameters of LFM signal show the validity of the method.
文摘为提升反辐射导引头抗干扰能力,提出一种利用辐射源到达角(Direction of Arrival,DOA)信息和K-means的聚类算法。该方法利用角度网格完成测向数据图形化,通过对当前图形斑块和理想条件下单目标斑块特征参数进行比对,实现斑块内目标数量自动估计,并借助K-means算法完成目标角度估计。经仿真验证,该方法能够在目标数量未知条件下,实现目标聚类,且能够适应多个辐射源目标彼此靠近,信号脉冲测向分布彼此重叠的情况,聚类后的估计角度误差不大于系统固有误差。
