A fusion machine approach for pulse train deinterleaving

For the classification of radar emitters, it is important to be able to separate pulses in the interleaved pulse train in terms of their source. In practical scenarios, the received pulse train may miss a number of pulses. The missing pulses can degrade the performance of the existing TOA-based pulse train deinterleaving methods. However, this degradation is not identical for different methods. If multiple pulse train deinterleaving methods, which work with different principle, are used in fusion, it may be possible to have better performance than performance of any of pulse train deinterleaving methods used alone. In this paper, a framework to fuse multiple pulse train deinterleaved methods is proposed for separating individual pulse train included in the received pulse train. Under this framework, a Fusion Machine Approach (FMA) is proposed and the implementing steps of FMA are discussed in detail. Two types of FMA are employed in simulation. The results of simulation show that the proposed FMA has better performance than existing pulse train deinterleaving methods. The FMA has the best robustness to missing pulse and PRI jitter, and can effectively deinterleave the received pulse train with PRI stagger and jittered PRI.