Spatio-Temporal Alignment and Trajectory Matching for Netted Radar without Prior Spatial Information and Time Delay

For netted radars, a new alignment algorithm without prior spatial information (i.e., locations and attitudes of radars) and time delay is proposed to keep the spatio-temporal alignment. The unknown parameters to be estimated include the rotation matrix, the translation vector and the delay between radar stations in this paper. The minimum error function in unified space and time coordinate system is established based on the target trajectory measured by each radar. Firstly, the alternating spatio-temporal alignment method is used to estimate the spatial and temporal parameters, and its statistical performance is compared to the Cramer-Rao bound. Even under severe conditions in which each radar in the network can only observe part of the trajectory, the proposed algorithm can still be adopted to estimate the alignment parameters and complete the trajectory. Then, a trajectory matching algorithm based on random sample consensus (RANSAC) is proposed for multitarget. The corresponding relationship between trajectories is established through minimizing sum of paired trajectory error, and multitarget trajectories from each radar are matched. Finally, the spatio-temporal parameters are refined by all matched trajectory pairs. Simulation results show that the tracking information from different radars is transformed into a unified coordinate after spatio-temporal alignment. Moreover, the registration error is reduced and the tracking accuracy is improved.