The mapping of the radar echo dataset into a graph signal offers a novel perspective for solving the radar target localization problem. However, the published graph-based methods are mostly applicable to the uniform array configuration. In this paper, we propose an enhanced graph-based target localization method that can be applicable to the non-uniform frequency diversity array radar to fill this gap. Following the previous studies, we establish a space-domain graph model for the echo signal acquired from a non-uniform frequency diversity array radar. Subsequently, we employ the graph signal processing method to solve the target localization problem. Numerical simulations demonstrated that the proposed graph-based localization method provides a high resolution and accurate estimation, surpassing conventional methods.
Frequency diversity array (FDA) radar can automatically scan an area of interest without phase shifters by utilizing its range-angle-dependent beampattern, which is more convenient in terms of system implementation relative to the conventional phased array (PA) radar. However, the FDA cannot track a target continuously as the PA does because of the periodicity characteristic shown in the FDA’s beampattern. We address the problem of stable tracking of multiple moving targets, aiming at pursuing a high-resolution target imaging approach. First, we establish an inverse synthetic aperture radar (ISAR) imaging model applicable to multiple repeated subpulses based FDA ISAR (MRS-FDA-ISAR) radar. In the procedure of moving target imaging, the proposed MRS-FDA-ISAR scheme is able to not only avoid the problem of range-angle-coupling but also achieve high-level energy accumulation by compensating the phase of the target. Finally, the back projection algorithm is utilized to achieve high-resolution two-dimensional imaging of multiple moving targets. Numerical experiments demonstrated the effectiveness of the proposed approach and it is shown that this approach is superior to conventional ISAR imaging methods due to its high-level energy utilization and relatively low hardware overhead.
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