LAUSR

Traditional networked radar systems for target tracking usually suffer from a heavy data processing burden, and do not consider the sensor location uncertainties (SLUs), by assuming that radar locations are known perfectly, which is applicable only for static platforms. In this paper, considering sensors mounted on moving platforms, we propose a joint power allocation and measurement selection (JPAMS) strategy for multitarget tracking and data reduction in radar networks with the SLUs. The mechanism is to optimize the transmitted power and select the propagation paths with informative measurements, simultaneously. First, we adopt a distributed fusion architecture to estimate both states of targets and radars in clutter. Based on the distributed fusion architecture, the predicted conditional Cramér-Rao lower bound (PC-CRLB) considering the SLU and the measurement origin uncertainty is derived. Second, the JPAMS strategy is formulated as a bi-objective optimization problem, where the sum of weighted PC-CRLBs and the number of selected propagation paths are used as the performance metrics with respect to tracking and data reduction. The corresponding optimization is a NP-hard problem containing both continuous and binary variables. Third, to solve this nonconvex problem, we propose a sparsity-enhancing sequential convex programming algorithm. Finally, numerical simulations demonstrate the superiority of the proposed JPAMS strategy over the traditional allocation strategies.