基于卡尔曼的无线传感器网络时空融合研究

Research on spatial-temporal fusion for WSNs based on Kalman filter

无线传感器网络(WSNs)因其传感节点数目多,且节点易受环境干扰出现故障或失效的特点,对融合技术提出了新的要求。引入中值滤波,利用其良好的抑制脉冲噪声能力,结合卡尔曼滤波开发适用于WSNs的融合算法。采用时空分级融合减少集中计算量,使网络具有实时处理能力。算法具有容错能力,可提高网络鲁棒性。仿真结果表明了算法的有效性。

For the large number of sensors are easily influenced and even broken by environment interference, new advanced fusion technique is requested for WSNs. Median filter is introduced,its nicer ability to restrain pulse noise is made the use of, Kalman filter is combined with to research new fusion algorithm adapted to WSNs. Take spatial-temporal fusion to reduce the cost of computing, which makes the network have real time ability. The algorithm can process the data with mistakes, so that it can improve the robot of the network. The validity of the algorithm is shown by simulation.