Online spatial alignment and fusion for networked radars on moving platforms only using target position information

Abstract

Spatial alignment is a prerequisite for cooperative detection in networked radars, even minor biases in spatial alignment can result in large errors in the converted target geolocation. Existing spatial alignment algorithms commonly rely on the Global Positioning System (GPS) and Inertial Measurement Unit (IMU) to provide positional data and attitude angles. To overcome this limitation, we formulate the spatial alignment relationships between radars as an optimization function based on a sliding window mechanism. This function is then solved recursively using a combination of Tikhonov regularization and recursive least squares (RLS) to obtain accurate spatial alignment estimates. To provide criteria for the selection of reference radars before multi-radar alignment, a dynamic preselection strategy is put forward. This strategy creates a prior advantage for parameter estimation by analyzing the correlations between target trajectories from different radars. Considering the coupling between alignment and fusion processes, we present a feedback adjustment method to further improve the accuracy of alignment and fusion. Simulation results show the effectiveness of the proposed algorithm and its superior performance compared with traditional algorithms under the same conditions.