Decentralized Quantized Kalman Filter with Limited Bandwidth

Abstract

Consider the decentralized estimation problem of dynamic stochastic process in a sensor network. Due to bandwidth constraints, 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. The main advantages of these proposed filters include two aspects, the first is to adapt the general vector system, and another is that the data quantization and transmission strategies are both adaptive. 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 two methods.