基于卡尔曼过滤的多传感器协作数据重构算法研究

Collaboration data reconstruction algorithm based on Kalman filtering for multi-sensor

针对多传感器系统存在的数据易丢失、易受干扰和易失真等问题,在卡尔曼过滤的基础上结合协同合作方式提出了一种协作数据重构算法。该算法从一阶线性化、二次加权突变平滑和随机统计特征等方面出发,建立了适用于多传感器系统的卡尔曼过滤算法,然后基于中继转发信号和自适应感知时间间隙,给出了协作数据重构多传感器系统协同过程控制算法。数学分析和实验结果表明,所提算法与压缩感知重构算法相比在监测数据准确度、均方差、功耗和能耗等方面表现出优势。

Multi-sensor system for data easily lost, vulnerable to interference and ease of distortion and other issues, this pa- per proposed a cooperative data reconstruction algorithm combines collaborative manner on the basis of the Kahnan filter. The algorithm from an order linearization, quadratic weighted random mutation smoothing and statistical features and other aspects of starting to establish a system for multi-sensor Kalman filter algorithm, and then relaying signals based on time and adaptive perception gap, gave the collaboration cooperative muhi-sensor system data reconstruction process control algorithms. Mathe- matical analysis and experimental results show that the proposed algorithm is compared with the compressed sensing reconstruc- tion algorithm accuracy in monitoring data, have shown the advantages of variance, power and energy and so on.