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Scale-Invariant Subspace Detectors based on First- and Second-Order Statistical Models
IEEE Transactions on Signal Processing ( IF 4.6 ) Pub Date : 2020-01-01 , DOI: 10.1109/tsp.2020.3036725
Ignacio Santamaria , Louis L. Scharf , David Ramirez

The problem is to detect a multi-dimensional source transmitting an unknown sequence of complex-valued symbols to a multi-sensor array. In some cases the channel subspace is known, and in others only its dimension is known. Should the unknown transmissions be treated as unknowns in a first-order statistical model, or should they be assigned a prior distribution that is then used to marginalize a first-order model for a second-order statistical model? This question motivates the derivation of subspace detectors for cases where the subspace is known, and for cases where only the dimension of the subspace is known. For three of these four models the GLR detectors are known, and they have been reported in the literature. But the GLR detector for the case of a known subspace and a second-order model for the measurements is derived for the first time in this paper. When the subspace is known, second-order generalized likelihood ratio (GLR) tests outperform first-order GLR tests when the spread of subspace eigenvalues is large, while first-order GLR tests outperform second-order GLR tests when the spread is small. When only the dimension of the subspace is known, second-order GLR tests outperform first-order GLR tests, regardless of the spread of signal subspace eigenvalues. For a dimension-1 source, first-order and second-order statistical models lead to equivalent GLR tests. This is a new finding.

中文翻译:

基于一阶和二阶统计模型的尺度不变子空间检测器

问题是检测向多传感器阵列传输未知复值符号序列的多维源。在某些情况下,通道子空间是已知的,而在其他情况下,只有其维度是已知的。未知传输是否应该被视为一阶统计模型中的未知数,还是应该为它们分配一个先验分布,然后用于将一阶模型边缘化为二阶统计模型?这个问题激发了子空间检测器的推导,用于子空间已知的情况,以及仅子空间维度已知的情况。对于这四个模型中的三个,GLR 检测器是已知的,并且它们已在文献中报道。但本文首次推导出了已知子空间情况下的 GLR 检测器和测量的二阶模型。当子空间已知时,当子空间特征值的传播较大时,二阶广义似然比 (GLR) 测试优于一阶 GLR 测试,而当传播较小时,一阶 GLR 测试优于二阶 GLR 测试。当仅知道子空间的维度时,无论信号子空间特征值的分布如何,二阶 GLR 测试都优于一阶 GLR 测试。对于维度 1 源,一阶和二阶统计模型导致等效的 GLR 测试。这是一个新的发现。当子空间特征值的传播较大时,二阶广义似然比 (GLR) 测试优于一阶 GLR 测试,而当传播较小时,一阶 GLR 测试优于二阶 GLR 测试。当仅知道子空间的维度时,无论信号子空间特征值的分布如何,二阶 GLR 测试都优于一阶 GLR 测试。对于维度 1 源,一阶和二阶统计模型导致等效的 GLR 测试。这是一个新的发现。当子空间特征值的传播较大时,二阶广义似然比 (GLR) 测试优于一阶 GLR 测试,而当传播较小时,一阶 GLR 测试优于二阶 GLR 测试。当仅知道子空间的维度时,无论信号子空间特征值的分布如何,二阶 GLR 测试都优于一阶 GLR 测试。对于维度 1 源,一阶和二阶统计模型导致等效的 GLR 测试。这是一个新的发现。一阶和二阶统计模型导致等效的 GLR 测试。这是一个新的发现。一阶和二阶统计模型导致等效的 GLR 测试。这是一个新的发现。
更新日期:2020-01-01
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