基于Rao和Wald检验准则,本文推导了联合子空间(Union of Subspaces,UoS)检测器(UoS-Rao、UoS-Wald),并通过引入一个可调参数提出一种可调检测器(UoS-Tunable),从而实现联合子空间目标检测,通过调节参数来灵活调节检测器的检测性能、分...基于Rao和Wald检验准则,本文推导了联合子空间(Union of Subspaces,UoS)检测器(UoS-Rao、UoS-Wald),并通过引入一个可调参数提出一种可调检测器(UoS-Tunable),从而实现联合子空间目标检测,通过调节参数来灵活调节检测器的检测性能、分类性能与选择性。当可调参数较小时,可以提高检测性能与分类性能,并提高其对信号失配的鲁棒性;当可调参数较大时,则会降低检测性能与分类性能,但会提高其对于信号失配的选择性。最后,仿真实验验证了所提方法的有效性。展开更多
针对现有文献对自适应阵列检测问题的研究相对比较少的问题,构建高斯背景中一类新的自适应阵列检测器。从新的视角理解高斯背景中自适应阵列检测器的原理,分析了信号子空间建模目的,以及结构化与无结构化检测器、解耦与不解耦检测器之...针对现有文献对自适应阵列检测问题的研究相对比较少的问题,构建高斯背景中一类新的自适应阵列检测器。从新的视角理解高斯背景中自适应阵列检测器的原理,分析了信号子空间建模目的,以及结构化与无结构化检测器、解耦与不解耦检测器之间的性能差异,并提出了调节因子的概念,构造并分析了一类新的结构化检测器。仿真结果表明:从计算负担和检测能力2方面考虑,Hyung Soo Kim的解耦结构化Kelly检测器,Yow-Ling Gau的降秩检测器,以及构造的解耦结构化Kalson检测器都具有比其他检测器更好的综合性能。该研究可为寻找非高斯背景中自适应阵列检测器的构造方法提供参考。展开更多
The Khatri-Rao(KR) subspace method is a high resolution method for direction-of-arrival(DOA) estimation.Combined with 2q level nested array,the KR subspace method can detect O(N2q) sources with N sensors.However,the m...The Khatri-Rao(KR) subspace method is a high resolution method for direction-of-arrival(DOA) estimation.Combined with 2q level nested array,the KR subspace method can detect O(N2q) sources with N sensors.However,the method cannot be applicable to Gaussian sources when q is equal to or greater than 2 since it needs to use 2q-th order cumulants.In this work,a novel approach is presented to conduct DOA estimation by constructing a fourth order difference co-array.Unlike the existing DOA estimation method based on the KR product and 2q level nested array,the proposed method only uses second order statistics,so it can be employed to Gaussian sources as well as non-Gaussian sources.By exploiting a four-level nested array with N elements,our method can also identify O(N4) sources.In order to estimate the wideband signals,the proposed method is extended to the wideband scenarios.Simulation results demonstrate that,compared to the state of the art KR subspace based methods,the new method achieves higher resolution.展开更多
文摘基于Rao和Wald检验准则,本文推导了联合子空间(Union of Subspaces,UoS)检测器(UoS-Rao、UoS-Wald),并通过引入一个可调参数提出一种可调检测器(UoS-Tunable),从而实现联合子空间目标检测,通过调节参数来灵活调节检测器的检测性能、分类性能与选择性。当可调参数较小时,可以提高检测性能与分类性能,并提高其对信号失配的鲁棒性;当可调参数较大时,则会降低检测性能与分类性能,但会提高其对于信号失配的选择性。最后,仿真实验验证了所提方法的有效性。
文摘针对现有文献对自适应阵列检测问题的研究相对比较少的问题,构建高斯背景中一类新的自适应阵列检测器。从新的视角理解高斯背景中自适应阵列检测器的原理,分析了信号子空间建模目的,以及结构化与无结构化检测器、解耦与不解耦检测器之间的性能差异,并提出了调节因子的概念,构造并分析了一类新的结构化检测器。仿真结果表明:从计算负担和检测能力2方面考虑,Hyung Soo Kim的解耦结构化Kelly检测器,Yow-Ling Gau的降秩检测器,以及构造的解耦结构化Kalson检测器都具有比其他检测器更好的综合性能。该研究可为寻找非高斯背景中自适应阵列检测器的构造方法提供参考。
基金Project(2010ZX03006-004) supported by the National Science and Technology Major Program of ChinaProject(YYYJ-1113) supported by the Knowledge Innovation Program of the Chinese Academy of SciencesProject(2011CB302901) supported by the National Basic Research Program of China
文摘The Khatri-Rao(KR) subspace method is a high resolution method for direction-of-arrival(DOA) estimation.Combined with 2q level nested array,the KR subspace method can detect O(N2q) sources with N sensors.However,the method cannot be applicable to Gaussian sources when q is equal to or greater than 2 since it needs to use 2q-th order cumulants.In this work,a novel approach is presented to conduct DOA estimation by constructing a fourth order difference co-array.Unlike the existing DOA estimation method based on the KR product and 2q level nested array,the proposed method only uses second order statistics,so it can be employed to Gaussian sources as well as non-Gaussian sources.By exploiting a four-level nested array with N elements,our method can also identify O(N4) sources.In order to estimate the wideband signals,the proposed method is extended to the wideband scenarios.Simulation results demonstrate that,compared to the state of the art KR subspace based methods,the new method achieves higher resolution.