This paper is concerned with the distributed fusion Kalman filtering problem for networked systems with communication constraints. A dimensionality reduction strategy and a uniform quantization strategy are introduced to reduce communication traffic. To overcome the unboundedness of estimates/measurements in unstable systems, it is proposed to quantize the innovations that are sent to the fusion center through limited bandwidth channels. Then, a recursively distributed dimensionality reduction fusion Kalman filtering algorithm is developed by using a model uncertainty method to process quantization noises. Finally, a target tracking system is employed to demonstrate the effectiveness of the proposed methods.

本文关注具有通信约束的网络系统的分布式融合卡尔曼滤波问题。引入降维策略和统一量化策略以减少通信流量。为了克服不稳定系统中估计/测量的无限性，建议量化通过有限带宽通道发送到融合中心的创新。然后，利用模型不确定性方法处理量化噪声，开发了递归分布降维融合卡尔曼滤波算法。最后，采用目标跟踪系统来证明所提出方法的有效性。