Abstract
In inverse synthetic aperture radar (ISAR) imaging of targets with complex motion, the azimuth echoes have to be modeled as multicomponent cubic phase signals (CPSs) after motion compensation. For the CPS model, the chirp rate and the quadratic chirp rate deteriorate the ISAR image quality due to the Doppler frequency shift; thus, an effective parameter estimation algorithm is required. This paper focuses on a parameter estimation algorithm for multicomponent CPSs based on the local polynomial ambiguity function (LPAF), which is simple and can be easily implemented via the complex multiplication and fast Fourier transform. Compared with the existing parameter estimation algorithm for CPS, the proposed algorithm can achieve a better compromise between performance and computational complexity. Then, the high-quality ISAR image can be obtained by the proposed LPAF-based ISAR imaging algorithm. The results of the simulated data demonstrate the effectiveness of the proposed algorithm.
Highlights
The inverse synthetic aperture radar (ISAR) imaging technique for moving targets has attracted the attention of many radar researchers in the past three decades due to its significance in both civil and military applications.[1,2,3,4,5,6] Generally, in order to obtain a well-focused ISAR image, the first procedure is to implement motion compensation, which includes range alignment and phase adjustment.[6]
With regard to the parameter estimation of cubic phase signals (CPSs), numerous algorithms, including the cubic phase function (CPF),[15,16] the higher-order ambiguity function,[17] the product generalized CPF (PGCPF),[18] the product high-order matched-phase transform,[19] and the modified version of CPF (MCPF),[14] have been addressed, but all of them involve multilinear transformation and discrete Fourier transform for the nonuniformly spaced signal sample, which result in extensive crossterms under multi-CPSs and undesirable computational complexity.[10,11,12,13,14,18,19,20,21,22,23]
In order to further validate the effectiveness of the local polynomial ambiguity function (LPAF)-based ISAR imaging algorithm for targets with complex motion, we provide the results of the LPAF-based ISAR imaging algorithm under a range of different signal-to-noise ratio (SNR) in Fig. 8, where the scope of SNRin is from 2 to −8 dB with an increment of −2 dB
Summary
The inverse synthetic aperture radar (ISAR) imaging technique for moving targets has attracted the attention of many radar researchers in the past three decades due to its significance in both civil and military applications.[1,2,3,4,5,6] Generally, in order to obtain a well-focused ISAR image, the first procedure is to implement motion compensation, which includes range alignment (the translational and rotational migrations) and phase adjustment (the Doppler phase caused by the translation).[6]. With regard to the parameter estimation of CPS, numerous algorithms, including the cubic phase function (CPF),[15,16] the higher-order ambiguity function,[17] the product generalized CPF (PGCPF),[18] the product high-order matched-phase transform,[19] and the modified version of CPF (MCPF),[14] have been addressed, but all of them involve multilinear transformation and discrete Fourier transform for the nonuniformly spaced signal sample, which result in extensive crossterms under multi-CPSs and undesirable computational complexity.[10,11,12,13,14,18,19,20,21,22,23] In Ref. 20, the parameter estimation algorithm based on the local polynomial Wigner distribution (LPWD) is proposed and has been successfully applied to ISAR imaging.[21,22] after compensating the third-order term with the estimated parameter, the LPWD algorithm estimates the secondorder coefficient using CPF method, which does lead to the heavy computational burden due to the Fourier transform with respect to the nonuniformly spaced data. The application of the LPAF-based ISAR imaging algorithm and the conclusion are given in Secs. 6 and 7, respectively
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