This paper is focused on simultaneous target detection and angle estimation with a multichannel phased array radar. Resorting to a linearized expression for the array steering vector around the beam pointing direction, the problem is formulated as a composite binary hypothesis test where the unknowns, under the alternative hypothesis, include the target directional cosines displacements with respect to the array nominal coarse pointing direction. The problem is handled via the Generalized Likelihood Ratio (GLR) criterion (both one-step and two-step) where decision statistics leveraging the Maximum Likelihood Estimates (MLEs) of the parameters are compared with a detection threshold. If crossed, target presence is declared and the MLEs of the aforementioned displacements directly provide target angular position with respect to the pointing direction. From the analytic point of view, ML estimation involves a constrained fractional quadratic optimization problem whose optimal solution can be found via the Dinkelbach's algorithm or approximated through a fast-converging procedure based on a Coordinate Descent (CD) optimization. The performance analysis of the proposed architectures as well as the corresponding discussion is developed in terms of computational complexity, Constant False Alarm Rate (CFAR) behavior, detection performance, and angular estimation accuracy, also in comparison with some counterparts available in the open literature and theoretical benchmark limits.

中文翻译：

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多通道相控阵雷达中的单脉冲同时目标检测和角度估计

本文的重点是使用多通道相控阵雷达同时进行目标检测和角度估计。求助于围绕波束指向方向的阵列控制向量的线性化表达式，该问题被表述为复合二元假设检验，其中，在替代假设下，未知数包括相对于阵列标称粗指向方向的目标方向余弦位移. 该问题通过广义似然比 (GLR) 标准（一步和两步）处理，其中利用参数的最大似然估计 (MLE) 的决策统计数据与检测阈值进行比较。如果穿越，声明目标存在，并且上述位移的 MLE 直接提供相对于指向方向的目标角位置。从分析的角度来看，ML 估计涉及一个受约束的分数二次优化问题，其最优解可以通过 Dinkelbach 算法找到，或者通过基于坐标下降 (CD) 优化的快速收敛过程来近似。所提出的架构的性能分析以及相应的讨论是在计算复杂性、恒定误报率 (CFAR) 行为、检测性能和角度估计精度方面进行的，还与公开文献和理论基准限制。ML 估计涉及一个受约束的分数二次优化问题，其最优解可以通过 Dinkelbach 算法找到，或者通过基于坐标下降 (CD) 优化的快速收敛过程来近似。所提出的架构的性能分析以及相应的讨论是在计算复杂性、恒定误报率 (CFAR) 行为、检测性能和角度估计精度方面进行的，还与公开文献和理论基准限制。ML 估计涉及一个受约束的分数二次优化问题，其最优解可以通过 Dinkelbach 算法找到，或者通过基于坐标下降 (CD) 优化的快速收敛过程来近似。所提出的架构的性能分析以及相应的讨论是在计算复杂性、恒定误报率 (CFAR) 行为、检测性能和角度估计精度方面进行的，还与公开文献和理论基准限制。