Model-based adaptive detection of range-spread targets
Model-based adaptive detection of range-spread targets
复制标题
DOI：
10.1049/ip-rsn:20040157
复制
发表时间：
2004-02
期刊：
影响因子：
--
通讯作者：
G. Alfano;A. Maio;A. Farina
中科院分区：
文献类型：
--
作者：
G. Alfano;A. Maio;A. Farina
研究了在结构化扰动存在下距离分布目标的检测问题，该扰动被建模为参数未知的自回归高斯过程。重点是两种不同的情况。第一个假设来自测试单元的所有数据向量共享相同的协方差矩阵（齐次环境）。第二种是指以完全不同的协方差（异构环境）为特征的数据向量的情况。四个探测器利用渐近广义似然比准则的设计和评估。值得注意的是，它们确保了相对于干扰功率水平的恒虚警率（CFAR）特性，并且其中两个相对于干扰协方差矩阵渐进CFAR。最后，性能评估，也是基于真实的雷达数据，表明这些检测器实现，在一般情况下，令人满意的检测性能。
The authors consider the problem of detecting range-distributed targets in the presence of structured disturbance modelled as an autoregressive Gaussian process with unknown parameters. The focus is on two different scenarios. The first assumes that all the data vectors from the cells under test share the same covariance matrix (homogeneous environment). The second refers to the case of data vectors characterised by completely different covariances (heterogeneous environment). Four detectors exploiting the asymptotic generalised likelihood ratio criterion are devised and assessed. Remarkably, they ensure the constant false alarm rate (CFAR) property with respect to the disturbance power level, and two of them are asymptotically CFAR with respect to the disturbance covariance matrix. Finally the performance assessment, based also on real radar data, has shown that these detectors achieve, in general, satisfactory detection performances.