A Bank of Sequential Unscented Kalman Filters for Target Tracking in Range-only WSNs

The paper is concerned with the target tracking in range-only wireless sensor networks (WSNs). To integrate the separated measurements from the WSN, a sequential fusion estimation method is presented in the sense of linear minimum mean squared error (LMMSE). Moreover, the unscented transformation is used to implement the recursion of means and covariances, and this kind estimator is termed as sequential unscented Kalman filter (SUKF). A bank of SUKFs are employed to improve the estimation accuracy and stability as a result of that the orientation of the target is not observable. Accordingly, a set of estimates are generated by the filter bank and the estimates are pruned and updated at each estimation instant. Finally, by simulations of a target tracking example, it demonstrated that in contrast to the single SUKF a better estimation accuracy and convergence speed can be obtained by the SUKF bank.