Kalman filter based on adaptive quantized information

When dealing with decentralized estimation problem of dynamic stochastic process in a sensor network, it is important to reduce the cost of communicating the local information due to bandwidth constraints. Thus, only quantized messages of the original information from local sensor are available. For a class of vector state-vector observation model, an adaptive quantization strategy and sequential filter technique are introduced to design fusion algorithms in this paper. According to different forms of original information, two suboptimal Kalman filters are presented based on quantized measurements (KFQM) and quantized innovations (KFQI) respectively. In contrast, the latter has better estimation accuracy under the same bandwidth constraints because of the less information loss while quantizing innovations. Computer simulations show the effectiveness of both methods.