RIA: Distributed Signal Dectection in Uncertain Environments

为了检测噪声中的信号，BLUM将多个传感器分布在一定的区域内，这一点越来越受到人们的关注。关于这类方案设计的大部分研究工作都集中在信号检测问题上，其中已知对所考虑的环境进行分类的完整观测模型。这项研究涉及分布式检测方案，用于由于对操作环境的不完全了解而无法获得完整的观测模型的情况。这是一个非常实用的案例，在空中交通管制、雷达天气监测和其他雷达和声纳系统中都有应用。人们正在寻求具有替代假设的重要情况的稳健性结果，这些情况不是简单的、有限的观察样本大小、相依的观察和非相加的观察。构建这些方案的计划侧重于应用极小极大稳健统计理论。具有非平稳、可能非高斯背景环境和可能相关观测的情况也在调查中。

Blum Distributing multiple sensors over some region for the purpose of detecting a signal in noise is becoming increasingly attractive. The majority of the research work concerning the design of such schemes has focused on signal detection problems where a complete observation model which categorizes the environment under consideration is known. This research deals with distributed detection schemes for cases where a complete observation model is not available due to incomplete knowledge of the operating environment. This is an extremely practical case which has applications in air traffic control, radar weather monitoring, and other radar and sonar systems. Robustness results for important cases with alternative hypothesis which are not simple, finite observation sample sizes, dependent observations, and non-additive observations are being sought. The plan to construct these schemes focuses on applying the theory of minimax robust statistics. Cases with nonstationary, possibly non- Gaussian background environments and possibly dependent observations are also being investigated.