Kernel Variational Approach for Target Tracking in a Wireless Sensor Network
Hichem Snoussi, Paul Honeiné, Cédric Richard · 2015
The functions performed by wireless sensor networks have to adapt to the constraints of digital communications and energy limitations. The problem of monitoring a mobile object is resolved in a Bayesian framework based on a state model. The state model contains two equations: an equation reflecting the a priori that is already available about the trajectory of the target and a second equation linking the unknown state of the system to the observations that the sensors are collecting. The target tracking problem could be resolved in a Bayesian framework. This chapter presents the technical aspects of the local construction of a linear and Gaussian likelihood model by exploiting the measured data between sensors with known positions. It illustrates the effectiveness and robustness of the kernel VBA (DD-VF) algorithm for monitoring a moving target in a wireless sensor network, comparing it to the traditional variational filter with a known observation model.