Range difference based linear recursive robust target tracking for multiple UAV applications

This paper addresses the passive moving target tracking problem using the range difference (RD) information measured by cooperative UAVs. Apart from the conventional nonlinear filtering approaches, the RD based target tracking problem is newly formulated within the framework of linear robust state estimation. To do this, the uncertain linear measurement model which contains the stochastic parameter uncertainty is derived by using the noisy RD measurements. To cope with the performance degradation due to the stochastic parameter uncertainty, the recently developed non-conservative robust Kalman filtering technique is applied. The relationship between the UAV formation and the robust filter design parameters is investigated to tackle the implementation issue. For its recursive linear structure, it can provide more reliable performance tan the existing nonlinear filters. As well, it is suitable for real-time multiple UAV applications. Through the simulations, the quasi-optimality and fast convergence of the proposed method are demonstrated.