An important prerequisite for the radar network detection is that the measurements from local radars are transformed to a common reference frame without systematic or registration errors. For the signal level alignment, only partial signals are available for global decision-making due to power and bandwidth limitations. In this paper, a low-communication-rate spatial alignment in range-Doppler domain is proposed for networked radars without the prior spatial information (positions and attitudes) of radars, which is different from the existing methods in the trajectory domain or echo domain for alignment. To reduce the radar-to-fusion-center communication-rate, the method of initial constant false alarm rate detection is used to censor the signals in range-Doppler domain from local radars. Based on the spatial alignment model for the networked radars in geometry, a maximization problem is formulated. The objective function is the cross-correlation between the range-Doppler domain signals from different local radars. The optimization problem is solved by a genetic algorithm. Simulation results show that the rotation matrix and translation vector are estimated, and the detection probability of the proposed algorithm is improved after alignment and fusion compared with state-of-art methods.

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 spatiotemporal alignment. Moreover, the registration error is reduced and the tracking accuracy is improved.

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