Aiming at the problem of limited resource allocation in netted radar system, this paper extends the idea of distributed multiple-input multiple-output (MIMO) radar to netted radar detection, and proposes a multi-target imaging resource scheduling algorithm for netted radar based on single-input multiple-output (SIMO) technology. The algorithm estimates the target size based on the target feature recognition, and uses the compressed sensing principle to calculate the pulse resources needed for target imaging. Secondly, the radar is selected according to the target size, and a reasonable resource scheduling model is established. Finally, the effectiveness of the algorithm is verified by simulation, and compared with the conventional netted radar algorithm, the scheduling success rate is improved and the consumption of pulse resources is reduced.
Aiming at optimizing the allocation problem of limited resources in a radar network, a resource scheduling algorithm combining pulse interleaving with preallocation is proposed for multitarget inverse synthetic aperture radar imaging. The imaging method adopts compressed sensing, which only needs to emit a small number of pulses so that we can set the algorithms to schedule the allocation of the pulses over a period of time. The authors point out the problem of pulse conflict, which is ignored in the process of the scheduling algorithm and proposes a preallocation method to avoid the occurrence of the conflict. Meanwhile, pulse interleaving is added to increase the positions of the dispatchable pulses. Moreover, the combined algorithm can perform adaptive scheduling on the radar time resource according to the feature parameters after target feature cognitive. Finally, the feasibility of the combined algorithm is verified by the simulation, and two performance indicators, the hit value rate and the pulse utilization rate, are improved by the proposed algorithm.
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