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As a complementary imaging technology, coincidence imaging radar (CIR) achieves high resolution for stationary or low-speed targets under the assumption of ignoring the influence of the original position mismatching. As to high-speed moving targets moving from the original imaging cell to other imaging cells during imaging, it is inaccurate to reconstruct the target using the previous imaging plane. We focus on the recovery problem for high-speed moving targets in the CIR system based on the intrapulse frequency random modulation signal in a single pulse. The effects induced by the motion on the imaging performance are analyzed. Because the basis matrix in the CIR imaging equation is determined by the unknown velocity parameter of the moving target, both the target images and basis matrix should be estimated jointly. We propose an adaptive joint parametric estimation recovery algorithm based on the Tikhonov regularization method to update the target velocity and basis matrix adaptively and recover the target images synchronously. Finally, the target velocity and target images are obtained in an iterative manner. Simulation results are presented to demonstrate the efficiency of the proposed algorithm.

A basic problem in the space-based automatic identification system (AIS) is the low probability of detecting messages because ships' messages arrive at the receiver in the same time slot. In this study, sparse linear array optimal beam synthesis (SLA-OBS) technology is proposed to improve the capture ability of AIS messages by forming a narrow beam pattern that points in the direction of the desired AIS messages. To capture the desired signal within the narrow beam pattern, the directions of arrival (DOA) and the number of sources from ships are first estimated. Then, the ideal narrow beam pattern and minimal number of array elements are achieved synchronously with the CPLEX optimal tool. The simulations show that the message detection probability with the proposed method is greater than 95%, even when the situations are very serious, whereas the number of sparse linear antennas is small (no more than six).

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