基于稀疏重构的混叠脉冲序列的周期估计

doi:10.7527/S1000-6893.2018.22054

Abstract

Abstract: Previous methods have the shortcomings in overcoming the period estimation of aliased pulse signals with large jitter and high missing rate. This paper proposes a new method for hidden sub-period estimation of aliased pulse sequences based on sparse reconstruction. The method first interpolates and re-samples the pulse sequence obtained by mixing multiple pulse trains with different periods, and then establishes the sparse representation model of the aliasing pulse sequence by using the periodic dictionary constructed by Ramanujan. Finally, the joint l_2,0 mixing norm algorithm is applied to obtain the periodic estimation of the aliasing pulse sequence. The method has the advantages of strong anti-noise ability and anti-pulse ability, and is not affected by the change of primary phase. Shorter pulse data length is needed to obtain an accurate periodic sparse solution. Simulation results show that the proposed method has better estimation performance.

Key words: period estimation, joint l_2,0 mixed norm, Ramanujan Sum dictionary, radar pulse, sparse reconstruction

CLC Number:

XU Chengwei, TAO Jianwu. Period estimation of aliasing pulse sequences based on sparse reconstruction[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2018, 39(7): 322054-322054.

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Period estimation of aliasing pulse sequences based on sparse reconstruction

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