多水听器分布式扩展Kalman滤波融合算法

Fusion algorithm of distributed Kalman filtering based on multiple hydrophones

数据融合是信号处理领域非常引人注目的问题。在极坐标系下改进了直角坐标系下的扩展Kalman滤波算法,利用多传感器融合的基本理论,提出了用于水下目标的多水听器分布式扩展Kalman滤波融合算法。分别在目标不发生规避和目标发生规避两种情形下进行了仿真实验。结果表明,所提出的融合算法具有很好的跟踪性能,非常适合于工程的实际应用。

Data fusion has been a widely investigated question over the past twenty years. The extended Kalman filtering algorithm in cartesian coordinates is improved in polar coordinates. Subsequently, the fusion algorithm of distributed Kalman filtering based on multiple hydrophones is proposed using multi-sensors theory. The corresponding experiments are made under two conditions: non-evasive target and evasive target. It is shown that the algorithm has a good tracking performance, and it is also suited to engineering applying.