Nonlinear fusion using quantized measurements and cubature particle filter

Consider the nonlinear estimation fusion problem for dynamic stochastic process in sensor networks. Due to bandwidth or energy constraints, only quantized messages of the original information from local sensor are available. For a class of vector state-vector observation model, a quantized cubature particle filter (CPF) method is presented in this paper. Firstly, each sensor quantizes each component of the measurement verbatim and sends to fusion center (FC). Subsequently, FC compresses the quantized messages from local sensors in best linear unbiased estimation (BLUE) fusion rule. Finally, CPF is used to obtain a state estimation. Computer simulations show effectiveness of the developed method.