使用錯誤更正碼的分散式分類融合 (Distributed Classification Fusion using Error-Correcting Codes ,DCFECC) 己被提出應用在無線感測網路中用來解決感測器故障及通道干擾的問題。這種方法採取了最小漢明距離 (Minimum Hamming Distance, MHD) 的融合法則，使得其效能遠高於傳統的方法。接著，不同的融合法則陸續被提出來。其中一種是使用軟式決策解碼分散式分類融合法則(Distributed Classification fusion using Soft-decision Decoding, DCSD)。這種方法有較佳的效能表現。然而，當偵測結果不可靠時，資料在傳送通道被干擾的情況下，DCSD的效能也十分低落。這是因為感測器沒有評估偵測結果的可靠度就做出了決策，如此一來，更浪費了有限的電力來傳輸不可靠的資料。在這篇論文中分析DCSD的效能，並使用中央極限定理來推導出近似的效能分析，更一步推導出效能的上限。在這裡數值分析和模擬互相驗証。除此之外，這篇論文也提出了適應性重測法則來解決偵測不可靠的問題。在適應性重測演算法裡，觀察值得不可靠區間會先被設定。當感測器發現觀察的落在不可靠的區間時，感測器便會重新偵測，直到觀察值為可靠或是重測次數達到最大限制。同樣的，我們也提出了適應性重傳的演算法來改進無線通道的干擾效能。當融合中心收到的信號，會算出收到訊號的對數可能性函數(log-likelihood)與各個碼字的距離，當次小與最小的距離差小於可靠判定的設定值時，融合中心便會要求感測其重送決策，直到收到可靠的結果為止。通過模擬証明，在無線感測網路中，適應性重測和適應性重傳可有效的降低錯誤率。

Distributed Classification Fusion using Error-Correcting Codes
(DCFECC) has recently been proposed for wireless sensor networks. It adopts the Minimum Hamming Distance (MHD) fusion rule and performs much better than traditional classification approaches when the network has faulty sensors. Different fusion rules were proposed later. One of them is Distributed Classification fusion using Soft-decision Decoding (DCSD). The DCSD fusion rule has a considerably misclassification probability than the MHD fusion rule. However, the probability of misclassification using DCSD approach is high when the detection result is not reliable. Moreover, the transmission channel is highly noisy. Since the sensor makes its local decision without evaluating the reliability of the detection
result, it may waste its power to transmit an unreliable local
decision.

In this work, the performance of the DCSD fusion rule is analyzed. Asymptotic performance approximation of the DCSD fusion rule is derived based on the Central Limit Theorem. Furthermore, an asymptotic upper bound on the misclassification probability is obtained. The numerical simulations are conducted to verify our analysis results. Besides, this work proposes an adaptive redetection scheme to resolve this problem of the unreliable detection results. An unreliable range is set for each sensor. If the detection result of the sensor is not located in the unreliable range, the sensor makes a local decision. Otherwise, the sensor has
to make another detection. This work further proposes an adaptive retransmission scheme to reduce the misclassification probability in highly noisy channels. When a final decision made by a fusion center is unreliable, the sensor which has sent the local decision with the lowest channel reliability will be asked to retransmit its local decision by the fusion center. Performance analysis and simulation results show that the misclassification probability can be efficiently reduced through the adaptive redetection and retransmission.

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