论文标题
多个点状目标的新型参数估计和雷达检测方法：设计和比较
Novel Parameter Estimation and Radar Detection Approaches for Multiple Point-like Targets: Designs and Comparisons
论文作者
论文摘要
在这项工作中，我们制定并比较了两种创新策略，用于参数估计和雷达检测多点样目标。第一次出现的第一种策略共同利用了最大似然方法和贝叶斯学习来估计目标的参数，包括其在范围箱中的位置。第二种策略依赖于直觉，即对于高信噪比和噪声比值，包含投影到名义转向方向上的目标成分的数据的能量应高于仅受干扰影响的数据的能量。相对于干扰协方差矩阵的适应性也被认为是利用在测试窗口接近的近端收集的训练数据集。最后，另一个重要的创新方面涉及通过模型订单选择规则对目标数量数量的自适应估计。
In this work, we develop and compare two innovative strategies for parameter estimation and radar detection of multiple point-like targets. The first strategy, which appears here for the first time, jointly exploits the maximum likelihood approach and Bayesian learning to estimate targets' parameters including their positions in terms of range bins. The second strategy relies on the intuition that for high signal-to-interference plus-noise ratio values, the energy of data containing target components projected onto the nominal steering direction should be higher than the energy of data affected by interference only. The adaptivity with respect to the interference covariance matrix is also considered exploiting a training data set collected in the proximity of the window under test. Finally, another important innovation aspect concerns the adaptive estimation of the unknown number of targets by means of the model order selection rules.
