於未知本地感測器偵測機率下具通道知覺之分散式二元偵測

Abstract

在無線感測器網路系統下，現存有關於整合通道資訊之偵測法設計皆假設融合中心(Fusion Center)具有本地感測器偵測機率的資訊，但在實際狀況下，本地感測器對事件發生的偵測機率多隨時間和環境不同而變化。本論文中，假設本地感測器偵測率未知的情況下，吾人透過二元對稱通道(BSC)傳送本地之一位元判斷報告給融合中心。為解決此類存有未知的問題，傳統上多利用廣義似然比檢驗法(GLRT)，然而此方法並無法達到最佳效能，且難以分析。本論文針對此兩缺點，先是提出了最大似然估計法(ML estimate)的化簡，接著根據此化簡，為融合中心設計出較GLRT簡單之判斷法，並加以分析其效能。吾人由效能公式中觀察出通道效應對整體效能的影響，更進而設計出分配本地感測器傳送功率之方法。吾人所提出的判斷法輔以傳送功率之分配，和GLRT相比，不但大幅降低複雜度，效能更有顯著的改善，甚至接近偵測系統所能達到的理論最佳值。

In the field of wireless sensor networks, existing works of channel-aware fusion rule design assume that the fusion center (FC) knows the local sensor detection probabilities. However, this paradigm ignores the possibility of unknown sensor alarm responses to the event occurrences. This work focuses on the case where the local detection probability is unknown and assumes sensors transmit their one-bit reports through binary symmetric channels to FC. Traditionally, Generalized Likelihood Ratio Test (GLRT) can tackle this scenario, but it does not guarantee optimal performance and is too complicated to analyze. To solve these problems, a simpler fusion rule is proposed based on the simplified ML estimate, and its performance is analyzed. By investigating the channel effects, a power allocation scheme is then proposed to further improve the performance. Being far less complicated than GLRT, the proposed fusion rule with power allocation outperforms GLRT significantly and can even achieve the performance of LRT, which is the optimal rule for any possible detectors.