Abstract
Inverse synthetic aperture radar (ISAR) imaging of a maneuvering target is a challenging task in the field of radar signal processing. The azimuth echo can be characterized as a multi-component polynomial phase signal (PPS) after the translational compensation, and the high quality ISAR images can be obtained by the parameters estimation of it combined with the Range-Instantaneous-Doppler (RID) technique. In this paper, a novel parameters estimation algorithm of the multi-component PPS with order three (cubic phase signal-CPS) based on the modified discrete polynomial-phase transform (MDPT) is proposed, and the corresponding new ISAR imaging algorithm is presented consequently. This algorithm is efficient and accurate to generate a focused ISAR image, and the results of real data demonstrate the effectiveness of it.
Highlights
High resolution inverse synthetic aperture radar (ISAR) imaging is a critical tool to generate a focused image of a moving target [1,2,3,4,5,6,7,8], and it is widely used in the fields of target recognition, space probing and aircraft traffic control, etc. [9,10,11]
The Inverse synthetic aperture radar (ISAR) imaging results for real data are provided to illustrate the effectiveness of the modified discrete polynomial-phase transform (MDPT) algorithm in this paper
The instantaneous ISAR images at time t 0.30 s based on the cubic phase signal model are shown in Figure 6a,b. respectively
Summary
High resolution inverse synthetic aperture radar (ISAR) imaging is a critical tool to generate a focused image of a moving target [1,2,3,4,5,6,7,8], and it is widely used in the fields of target recognition, space probing and aircraft traffic control, etc. [9,10,11]. The high resolution in the range coordinate is Sensors 2015, 15 achieved by transmitting a large bandwidth coded signal, and the high cross range resolution is achieved by the coherent integration of echoes collected at different viewing angles between the radar and target [18]. The primary step before ISAR imaging is the motion compensation, which includes the envelope alignment and the phase adjustment. The envelope alignment can compensate the translational motion of each scatterer after the range is compressed, and the phase adjustment can eliminate the phase errors between two adjacent echoes. The most commonly used methods for the envelope alignment include the accumulated form of maximum correlation algorithm [19], the global algorithm [20] and the minimum entropy algorithm [21]. The phase adjustment can be implemented efficiently by the constant phase error elimination algorithm [19]
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