Distributed target tracking based on adaptive consensus UKF

As the traditional distributed methods of target tracking may suffer from performance degradation owing to mismatch between the noise distributions assumed as a priori and the actual ones, a distributed target tracking method was proposed based on adaptive consensus unscented Kalman filter to improve the accuracy and robustness of the tracking results. More specifically, at each time step, a distributed UKF (DUK) would be implemented to obtain the estimations of the moving target. Next, an online fault-detection mechanism was adopted to judge if it was necessary to update current noise covariance. If it was necessary, the estimations of the current noise covariance would be calculated according to the measurement information. By utilizing a weighting factor, the filter would combine the last noise covariance matrices with the estimations to obtain the new noise covariance matrices. Finally, the state estimations would be corrected according to the new noise covariance matrices and previous state estimations. The experiment results demonstrate that: in unknown noise environments the tracking errors of the proposed method are reduced by as much as 49.93% and 51.46% when compared with those of the distributed tracking methods based on the cubature information filter and DUK, respectively; in dynamic noise environments the tracking errors of the proposed method are reduced by as much as 40.67% and 40.06% when compared with those of the above two traditional methods, respectively. These results demonstrate that the proposed method performs well in terms of accuracy and robustness on distributed tracking with uncertain noise. © 2019, Science Press. All right reserved.