针对现有相干分布源波达方向(Direction Of Arrival,DOA)估计方法计算量大、抗冲击噪声能力弱和不能有效去相干等难题,本文提出了一种冲击噪声下相干分布源多峰DOA估计方法,并推导了冲击噪声下相干分布源DOA估计的克拉美罗界.为了实现...针对现有相干分布源波达方向(Direction Of Arrival,DOA)估计方法计算量大、抗冲击噪声能力弱和不能有效去相干等难题,本文提出了一种冲击噪声下相干分布源多峰DOA估计方法,并推导了冲击噪声下相干分布源DOA估计的克拉美罗界.为了实现冲击噪声下相干分布源DOA估计,采用加权范数协方差抑制冲击噪声,进而首次推导出多峰加权信号子空间拟合方程,并设计了一种多峰量子秃鹰算法快速无量化误差求解.仿真结果表明,所提方法在冲击噪声下能够以较小的快拍数实现相干分布源DOA估计,且无需额外的解相干操作即可有效去相干.与一些已有的高精度DOA估计方法相比,所提方法仿真时间明显缩短,且具有更高的估计精度和估计成功概率,突破了已有相干分布源DOA估计方法的应用局限,可推广应用于其他复杂的DOA估计问题中.展开更多
In order to solve discrete multi-objective optimization problems, a non-dominated sorting quantum particle swarm optimization (NSQPSO) based on non-dominated sorting and quantum particle swarm optimization is proposed...In order to solve discrete multi-objective optimization problems, a non-dominated sorting quantum particle swarm optimization (NSQPSO) based on non-dominated sorting and quantum particle swarm optimization is proposed, and the performance of the NSQPSO is evaluated through five classical benchmark functions. The quantum particle swarm optimization (QPSO) applies the quantum computing theory to particle swarm optimization, and thus has the advantages of both quantum computing theory and particle swarm optimization, so it has a faster convergence rate and a more accurate convergence value. Therefore, QPSO is used as the evolutionary method of the proposed NSQPSO. Also NSQPSO is used to solve cognitive radio spectrum allocation problem. The methods to complete spectrum allocation in previous literature only consider one objective, i.e. network utilization or fairness, but the proposed NSQPSO method, can consider both network utilization and fairness simultaneously through obtaining Pareto front solutions. Cognitive radio systems can select one solution from the Pareto front solutions according to the weight of network reward and fairness. If one weight is unit and the other is zero, then it becomes single objective optimization, so the proposed NSQPSO method has a much wider application range. The experimental research results show that the NSQPS can obtain the same non-dominated solutions as exhaustive search but takes much less time in small dimensions; while in large dimensions, where the problem cannot be solved by exhaustive search, the NSQPSO can still solve the problem, which proves the effectiveness of NSQPSO.展开更多
文摘针对现有相干分布源波达方向(Direction Of Arrival,DOA)估计方法计算量大、抗冲击噪声能力弱和不能有效去相干等难题,本文提出了一种冲击噪声下相干分布源多峰DOA估计方法,并推导了冲击噪声下相干分布源DOA估计的克拉美罗界.为了实现冲击噪声下相干分布源DOA估计,采用加权范数协方差抑制冲击噪声,进而首次推导出多峰加权信号子空间拟合方程,并设计了一种多峰量子秃鹰算法快速无量化误差求解.仿真结果表明,所提方法在冲击噪声下能够以较小的快拍数实现相干分布源DOA估计,且无需额外的解相干操作即可有效去相干.与一些已有的高精度DOA估计方法相比,所提方法仿真时间明显缩短,且具有更高的估计精度和估计成功概率,突破了已有相干分布源DOA估计方法的应用局限,可推广应用于其他复杂的DOA估计问题中.
基金Foundation item： Projects（61102106, 61102105） supported by the National Natural Science Foundation of China Project（2013M530148） supported by China Postdoctoral Science Foundation Project（HEUCF120806） supported by the Fundamental Research Funds for the Central Universities of China
文摘In order to solve discrete multi-objective optimization problems, a non-dominated sorting quantum particle swarm optimization (NSQPSO) based on non-dominated sorting and quantum particle swarm optimization is proposed, and the performance of the NSQPSO is evaluated through five classical benchmark functions. The quantum particle swarm optimization (QPSO) applies the quantum computing theory to particle swarm optimization, and thus has the advantages of both quantum computing theory and particle swarm optimization, so it has a faster convergence rate and a more accurate convergence value. Therefore, QPSO is used as the evolutionary method of the proposed NSQPSO. Also NSQPSO is used to solve cognitive radio spectrum allocation problem. The methods to complete spectrum allocation in previous literature only consider one objective, i.e. network utilization or fairness, but the proposed NSQPSO method, can consider both network utilization and fairness simultaneously through obtaining Pareto front solutions. Cognitive radio systems can select one solution from the Pareto front solutions according to the weight of network reward and fairness. If one weight is unit and the other is zero, then it becomes single objective optimization, so the proposed NSQPSO method has a much wider application range. The experimental research results show that the NSQPS can obtain the same non-dominated solutions as exhaustive search but takes much less time in small dimensions; while in large dimensions, where the problem cannot be solved by exhaustive search, the NSQPSO can still solve the problem, which proves the effectiveness of NSQPSO.
