Spectrum sensing is the key and premise of cognitive radio( CR). Current parallel cooperative spectrum sensing strategies have some problems,such as large number of cooperative secondary users and lack of consideratio...Spectrum sensing is the key and premise of cognitive radio( CR). Current parallel cooperative spectrum sensing strategies have some problems,such as large number of cooperative secondary users and lack of consideration for the sensing overhead and the transmission gain. To solve those problems,an optimized parallel cooperative spectrum sensing strategy based on iterative KuhnMunkres( KM) algorithm was proposed. To maximize the total system profit,it considers the tradeoff between the sensing overhead and the transmission gain. Iterative KM algorithm was applied to obtaining the optimal assignment,which indicated when and which channels secondary users should sense. Furthermore,the required detection probability was introduced to avoid unnecessary waste when the accuracy met the system requirement. Monte Carlo simulations show that the proposed strategy can obtain higher total system profit with fewer cooperative secondary users.展开更多
A quadratic bilevel programming problem is transformed into a single level complementarity slackness problem by applying Karush-Kuhn-Tucker（KKT） conditions.To cope with the complementarity constraints,a binary encod...A quadratic bilevel programming problem is transformed into a single level complementarity slackness problem by applying Karush-Kuhn-Tucker（KKT） conditions.To cope with the complementarity constraints,a binary encoding scheme is adopted for KKT multipliers,and then the complementarity slackness problem is simplified to successive quadratic programming problems,which can be solved by many algorithms available.Based on 0-1 binary encoding,an orthogonal genetic algorithm,in which the orthogonal experimental design with both two-level orthogonal array and factor analysis is used as crossover operator,is proposed.Numerical experiments on 10 benchmark examples show that the orthogonal genetic algorithm can find global optimal solutions of quadratic bilevel programming problems with high accuracy in a small number of iterations.展开更多
点航间的关联算法是雷达跟踪多目标的核心,而传统的数据关联算法都存在各自的不足。最近邻域(Nearest Neighbor,NN)法的关联逻辑简单、容易误跟踪,联合概率数据互联(Joint Probabilistic Data Association,JPDA)计算复杂、工程不易实现...点航间的关联算法是雷达跟踪多目标的核心,而传统的数据关联算法都存在各自的不足。最近邻域(Nearest Neighbor,NN)法的关联逻辑简单、容易误跟踪,联合概率数据互联(Joint Probabilistic Data Association,JPDA)计算复杂、工程不易实现,导致雷达在复杂场景下无法正确关联目标点迹进而跟踪丢失,抑或实时性太差无法直接应用在产品上。提出了一种基于模糊估计和最大权值匹配的多目标跟踪算法。算法在跟踪过程中起始当前态势所有航迹。将探测到的量测点迹和态势中所有航迹进行模糊匹配并建立关联权值矩阵,以矩阵总关联权值最大为目标,采用Kuhn-Munkres算法从关联权值矩阵中获取能够使全局权值最大的点航迹匹配组合。通过仿真与NN法、JPDA对比,在实测数据中应用了所提算法。实验结果表明,该算法能够解决多目标跟踪的关联错误问题,在实际应用中能够避免杂波和其他航迹的影响而保持对目标的稳定跟踪,计算量可接受,具有较好的工程应用前景。展开更多
基金Young Scientists Fund of the National Natural Science Foundation of China(No.61101141)Fundamental Research Funds for the Central Universities of China(No.HEUCF130807)Heilongjiang Province Natural Science Foundation for the Youth,China(No.QC2012C070/F010106)
文摘Spectrum sensing is the key and premise of cognitive radio( CR). Current parallel cooperative spectrum sensing strategies have some problems,such as large number of cooperative secondary users and lack of consideration for the sensing overhead and the transmission gain. To solve those problems,an optimized parallel cooperative spectrum sensing strategy based on iterative KuhnMunkres( KM) algorithm was proposed. To maximize the total system profit,it considers the tradeoff between the sensing overhead and the transmission gain. Iterative KM algorithm was applied to obtaining the optimal assignment,which indicated when and which channels secondary users should sense. Furthermore,the required detection probability was introduced to avoid unnecessary waste when the accuracy met the system requirement. Monte Carlo simulations show that the proposed strategy can obtain higher total system profit with fewer cooperative secondary users.
基金supported by the National Natural Science Foundation of China (60873099)
文摘A quadratic bilevel programming problem is transformed into a single level complementarity slackness problem by applying Karush-Kuhn-Tucker（KKT） conditions.To cope with the complementarity constraints,a binary encoding scheme is adopted for KKT multipliers,and then the complementarity slackness problem is simplified to successive quadratic programming problems,which can be solved by many algorithms available.Based on 0-1 binary encoding,an orthogonal genetic algorithm,in which the orthogonal experimental design with both two-level orthogonal array and factor analysis is used as crossover operator,is proposed.Numerical experiments on 10 benchmark examples show that the orthogonal genetic algorithm can find global optimal solutions of quadratic bilevel programming problems with high accuracy in a small number of iterations.
文摘点航间的关联算法是雷达跟踪多目标的核心,而传统的数据关联算法都存在各自的不足。最近邻域(Nearest Neighbor,NN)法的关联逻辑简单、容易误跟踪,联合概率数据互联(Joint Probabilistic Data Association,JPDA)计算复杂、工程不易实现,导致雷达在复杂场景下无法正确关联目标点迹进而跟踪丢失,抑或实时性太差无法直接应用在产品上。提出了一种基于模糊估计和最大权值匹配的多目标跟踪算法。算法在跟踪过程中起始当前态势所有航迹。将探测到的量测点迹和态势中所有航迹进行模糊匹配并建立关联权值矩阵,以矩阵总关联权值最大为目标,采用Kuhn-Munkres算法从关联权值矩阵中获取能够使全局权值最大的点航迹匹配组合。通过仿真与NN法、JPDA对比,在实测数据中应用了所提算法。实验结果表明,该算法能够解决多目标跟踪的关联错误问题,在实际应用中能够避免杂波和其他航迹的影响而保持对目标的稳定跟踪,计算量可接受,具有较好的工程应用前景。
