基于修正扩展卡尔曼序贯滤波的信息融合算法

A Novel Fusion Algorithm Based on Modified Extend Kalman Sequential Filtering

针对基于扩展卡尔曼滤波的融合算法存在滤波精度不高的问题,将修正扩展卡尔曼滤波算法与集中式序贯融合算法相结合,用于毫米波雷达和红外传感器目标融合跟踪。即先对毫米波雷达进行修正扩展卡尔曼滤波,再将滤波结果与红外传感器进行融合滤波。仿真结果表明该算法能够提高对机动目标的跟踪精度,增强跟踪系统对环境变化的适应能力。

A fusion algorithm for millimeter wave （MMW） radar and infrared （IR） sensor is discussed in this paper. Modified extend Kalman filter （MEKF） is simple yet very effective in accounting for the measurement of non - linearity. On condition of combining MEKF with sequential filter, the optimum state of maneuvering target is obtained. The target is tracked with MMW radar using MEKF, and then the filtering results are fused with information from IR sensor through sequential filter. In this way, the global state is updated at the fusion center. Simulation shows that the novel method can significantly improve the state estimation precision of composite guidance system and strengthens the adaptability of the tracking system to environmental changes.