A Variational Bayesian Adaptive Kalman Filter for the Random Losses Problem of Sensor Packet

Abstract

In this paper, a variational Bayesian adaptive Kalman filter (VBAKF) was used to solve the impact of unknown non-Gaussian measurement noise (NGMN) and sensor measurement loss in Wireless Sensor Networks (WSN) communication. First, the inverse Wishart (IW) distribution was used as the conjugate prior distribution of multiple nominal noise covariance matrices, and a Gaussian mixture model (GMM) was introduced to construct a measurement likelihood probability density function (PDF) to model the effect of the NGMN. The sensor measurement loss problem was modeled using the Bernoulli distribution as a statistical property of the packet loss parameter. Second, the proposed algorithm achieves iterative refinement of latent variable estimates through the application of variational Bayesian (VB) methods, thereby adjusting the GMM weights and the probability of sensor measurement loss accordingly. Third, we present the floating-point operations of the algorithm and compare them with those of other mixture model algorithms. Finally, the effectiveness of the algorithm will be verified by experimental simulation.