论文标题
分布式联合检测和估计：一种顺序方法
Distributed Joint Detection and Estimation: A Sequential Approach
论文作者
论文摘要
我们研究了共同检验两个假设并根据数据依次观察到的分布式网络中传感器依次观察到的随机参数的问题。特别是，我们假设数据是从高斯分布中汲取的，高斯分布的随机平均值将被估算。在放弃对融合中心的需求时，处理该处理是在本地执行的，并且在共识+创新方法之后，传感器与邻居相互作用。我们在各个传感器上设计测试，以使性能度量（即误差概率和于点误差）不超过预定义的水平，而平均样本数则最小化。将约束问题转换为不受约束的问题并将随后的减少降低到最佳停止问题之后，我们利用动态编程解决了后者。该解决方案显示出以一组非线性钟形方程为特征，该方程式通过成本系数进行了参数，然后通过线性编程确定以满足性能规格。数值示例验证了提出的理论。
We investigate the problem of jointly testing two hypotheses and estimating a random parameter based on data that is observed sequentially by sensors in a distributed network. In particular, we assume the data to be drawn from a Gaussian distribution, whose random mean is to be estimated. Forgoing the need for a fusion center, the processing is performed locally and the sensors interact with their neighbors following the consensus+innovations approach. We design the test at the individual sensors such that the performance measures, namely, error probabilities and mean-squared error, do not exceed pre-defined levels while the average sample number is minimized. After converting the constrained problem to an unconstrained problem and the subsequent reduction to an optimal stopping problem, we solve the latter utilizing dynamic programming. The solution is shown to be characterized by a set of non-linear Bellman equations, parametrized by cost coefficients, which are then determined by linear programming as to fulfill the performance specifications. A numerical example validates the proposed theory.
