We address the Bayesian sensor fusion approach for distributed location estimation in the wireless sensor network. Assume each sensor transmits local calculation of target position to a fusion center, which then generates under a Bayesian framework the final estimated trajectory. We study received signal strength indication-based approach using the unscented Kalman filter for each sensor to compute local estimation, and propose a novel distributed algorithm which combines the soft outputs sent from selected sensors and computes the approximated Bayesian estimates to the true position. Simulation results demonstrate that the proposed soft combining method can achieve similar tracking performance as the centralized data fusion approach. The computational cost of the proposed algorithm is less than the centralized method especially in large scale sensor networks. In addition, it is straightforward to incorporate the proposed soft combining strategy with other Bayesian filters for the general purpose of data fusion.

我们解决了用于无线传感器网络中分布式位置估计的贝叶斯传感器融合方法。假设每个传感器将目标位置的本地计算结果传送到融合中心，然后在贝叶斯框架下生成最终的估计轨迹。我们研究了使用无味卡尔曼滤波器为每个传感器计算基于接收信号强度指示的方法来计算局部估计，并提出了一种新颖的分布式算法，该算法结合了从选定传感器发送的软输出并计算出近似的贝叶斯估计到真实位置。仿真结果表明，所提出的软合并方法可以实现与集中式数据融合方法相似的跟踪性能。该算法的计算量比集中式方法少，尤其是在大规模传感器网络中。另外，很容易将拟议的软合并策略与其他贝叶斯滤波器合并，以实现数据融合的一般目的。