State Estimation of Moving Targets Using Sequential Range and Doppler Measurements With Swarm of UAV-Borne Radars

Estimation of moving target states is always needed in area situational awareness. This work presents a three-stage weighted least squares (WLS) approach that improves the reliability of the estimation of location, velocity, and acceleration of moving targets using sequential range and Doppler measurements obtained from swarm unmanned aerial vehicle-borne radars. First, the sequential locations of the target are accurately estimated using an improved plane approximation algorithm, which avoids second-order measurement errors. Second, the initial velocity and acceleration are estimated through WLS, with relaxed constraints to resolve Doppler measurement ambiguities. Finally, a joint optimization of target states is performed using a first-order Taylor approximation, further refining estimation accuracy. The Cramér–Rao lower bound of state estimation using spatiotemporal measurements is reformulated, demonstrating the theoretical benefits of the proposed method in improving state estimation performance. Simulation experiments show that the proposed approach provides superior target state estimates compared to other reported methods.