基于传感器网络的在线决策融合目标检测方法

针对传感器网络(sensor network, SN)目标融合检测应用中融合中心无法精确地获得局部传感器节点检测性能参数的问题,建立了基于SN的目标融合检测系统,提出了一种非理想信道条件下在线决策融合的目标检测方法。该方法依据解调后数据构建了节点未知虚警概率、检测概率以及节点与融合中心信道平均传输错误概率等未知参数求解模型,并采用非线性最小二乘方法在线地估计出这些未知参数。进而通过选择性能优的节点参与融合,最大化融合检测系统检测概率。仿真结果表明:这种在线决策融合方法能够准确地估计出传感器节点的概率参数以及信道的平均传输错误率;相比于已知先验的最优似然比融合规则,在线决策融合规则检测性能相当。

Online decision fusion method for target detection based on sensor networKs

To solve the problem that the fusion center in a sensor network (SN) cannot completely obtain the local detection performance indices, a target detection model based on the SN is established. An online decision fusion method for target detection with the non ideal channel between local sensors and the fusion center is proposed. This method constructs the model of solving unknown parameters including local false alarm probabilities, local detection probablities and the average bit error probability of the non-ideal transmission channels. The nonlinear least square method is employed to estimate the unknown parameters. In order to maximize the system detection performance, the sensors with high detection performance are chosen to participate in the fusion. The simulation results show that the estimations tend to be with the true local probability values and the average bit error probability. Compared with the optimal likelihood ratio (LR) based fusion rule, the proposed online decision fusion method exhibits only slight performance degradation.