Distributed filtering in network is a fundamental problem in the field of network signal processing. Each node estimates or tracks some unknown state relying on the private observation and the fusion information from the network. Network fusion is generally a way of interaction over network, by which nodes can learn from each other and make decision mutually. Unlike conventional methods, we construct a distributed filter using Bayesian network game as a fusion tool, where all the nodes exchange their best strategies instead of exchanging local estimators. The proposed algorithm is a coalition of signal processing and game theory in network, which can be extended to more general signal processing and decision making models.
['Muyuan Zhai', 'Hui Feng', 'Yuanyuan Tan', 'Bo Hu']
Kalman filters with Bayesian quadratic game fusion in networks