Jibin Zheng

ISAR Imaging of Targets With Complex Motions Based on the Keystone Time-Chirp Rate Distribution

In inverse synthetic aperture radar (ISAR) imaging of targets with complex motions such as fluctuating ships with oceanic waves and high maneuvering airplanes, the azimuth echo signals can be modeled as cubic phase signals (CPSs). In this letter, a new ISAR imaging algorithm based on the keystone time-chirp rate distribution (KTCRD) is proposed for the targets with complex motions. Compared with the recently published algorithms for the CPSs, the KTCRD can estimate the parameters of multicomponent CPSs without searching procedures and can acquire high antinoise performance with a relatively low computational load. With the estimated motion parameters, high-quality ISAR images can be obtained. Several simulation examples on the synthetic model are shown to validate the effectiveness of the new algorithm presented in this letter.

ISAR Imaging of Targets With Complex Motion Based on the Chirp Rate–Quadratic Chirp Rate Distribution

In inverse synthetic aperture radar (ISAR) imaging of targets with complex motion such as fluctuating ships with oceanic waves and high maneuvering airplanes, the azimuth echo signals can be modeled as cubic phase signals (CPSs) after the migration compensation. The chirp rate (CR) and the quadratic chirp rate (QCR) are two important physical quantities of the CPS, which deteriorate the azimuth focusing quality due to the Doppler frequency shift. With these two quantities, other parameters can be estimated by using the fast Fourier transform (FFT). Therefore, the CPS can be uniquely determined by both CR and QCR. In this paper, based on the proposed generalized keystone transform and the parametric instantaneous autocorrelation function, a novel distribution of the CPS, known as the CR-QCR distribution (CRQCRD), is presented and applied in a newly proposed ISAR imaging algorithm for targets with complex motion. The CRQCRD is simple and only requires the FFT and the nonuniform FFT (NUFFT). Owing to the application of the NUFFT, the computational cost is saved, and the searching procedure is unnecessary for the nonuniformly spaced signal. Compared to other four representative methods for CPSs, the CRQCRD, which can acquire higher antinoise performance and no error propagation, is searching-free and more suitable for the situation of multitargets. Several simulation examples, analyses of the antinoise performance, and ISAR images validate the effectiveness of the CRQCRD and the corresponding ISAR imaging algorithm.

ISAR Imaging of Nonuniformly Rotating Target Based on a Fast Parameter Estimation Algorithm of Cubic Phase Signal

In inverse synthetic aperture radar (ISAR) imaging of nonuniformly rotating targets, such as highly maneuvering airplanes and ships fluctuating with oceanic waves, azimuth echoes have to be modeled as cubic phase signals (CPSs) after the range migration compensation and the translational-induced phase error correction. For the CPS model, the chirp rate and the quadratic chirp rate, which deteriorate the azimuth focusing quality due to the Doppler frequency shift, need to be estimated with a parameter estimation algorithm. In this paper, by employing the proposed generalized scaled Fourier transform (GSCFT) and the nonuniform fast Fourier transform (NUFFT), a fast parameter estimation algorithm is presented and utilized in the ISAR imaging of the nonuniformly rotating target. Compared to the scaled Fourier transform-based algorithm, advantages of the fast parameter estimation algorithm include the following: 1) the computational cost is lower due to the utilization of the NUFFT, and 2) the GSCFT has a wider applicability in ISAR imaging applications. The CPS model and the algorithm implementation are verified with the real radar data of a ship target. In addition, the cross-term, which plays an important role in correlation algorithms, is analyzed for the fast parameter estimation algorithm. Through simulations of the synthetic data and the real radar data, we verify the effectiveness of the fast parameter estimation algorithm and the corresponding ISAR imaging algorithm.

Radar High-Speed Target Detection Based on the Scaled Inverse Fourier Transform

In this paper, by employing the symmetric autocorrelation function and the scaled inverse Fourier transform (SCIFT), a coherent detection algorithm is proposed for high-speed targets. This coherent detection algorithm is simple and can be easily implemented by using complex multiplications, the fast Fourier transform (FFT) and the inverse FFT (IFFT). Compared to the Hough transform and the keystone transform, this coherent detection algorithm can detect high-speed targets without the brute-force searching of unknown motion parameters and achieve a good balance between the computational cost and the antinoise performance. Through simulations and analyses for synthetic models and the real data, we verify the effectiveness of the proposed coherent detection algorithm.

FAST PARAMETER ESTIMATION ALGORITHM FOR CUBIC PHASE SIGNAL BASED ON QUANTIFYING EFFECTS OF DOPPLER FREQUENCY SHIFT

For the chirp rate and its change rate estimation of cubic phase signal (CPS), conventional algorithms cannot achieve a trade-ofi between low computational cost and high performance. In this paper, by utilizing the numerical computational method (NCM), efiects of Doppler frequency shift are quantifled, and the relationships of the optimal signal length with the chirp rate and change rate of chirp rate are obtained. Then a fast parameter estimation algorithm (DMNUFFT), based on dechirp method (DM) and nonuniform fast Fourier transform (NUFFT), is proposed. Compared with existing algorithms, DMNUFFT can achieve high performance with relatively low computational cost. The performance analyses and an application to inverse synthetic aperture radar (ISAR) imaging are shown to validate the efiectiveness of DMNUFFT.

ISAR Imaging of Targets With Complex Motions Based on a Noise-Resistant Parameter Estimation Algorithm Without Nonuniform Axis

In inverse synthetic aperture radar (ISAR) imaging of targets with complex motions, such as highly maneuvering airplanes and ships fluctuating with oceanic waves, azimuth echoes of a range cell have to be modeled as multicomponent cubic phase signals (CPSs) after the range alignment and the phase adjustment. Due to the time-varying Doppler frequencies of scatterers, ISAR image obtained with the standard range-Doppler algorithm is blurred, and the range-instantaneous-Doppler (RID) technique is required to improve the image quality. In this paper, by employing a novel parametric autocorrelation function and the generalized scaled Fourier transform, an effective parameter estimation algorithm is proposed for multicomponent CPSs and applied to reconstruct the RID image for targets with complex motions. Analyses of the implementation, the cross-term, the anti-noise performance, and the computational cost demonstrate that, compared with other three representative estimation algorithms, the proposed algorithm can eliminate the brute-force searching procedure and acquire a higher anti-noise performance without the nonuniform axis. Through simulations and analyses for synthetic models and the real radar data, we verify the effectiveness of the proposed estimation algorithm and the corresponding ISAR imaging algorithm.

Radar High-Speed Target Detection Based on the Frequency-Domain Deramp-Keystone Transform

In this paper, we propose a coherent detection algorithm for high-speed targets by employing the parametric symmetric autocorrelation function and the frequency-domain deramp-keystone transform (FDDKT). This coherent detection algorithm is an extension of the scaled inverse Fourier transform (SCIFT)-based detection algorithm. However, compared to the SCIFT-based detection algorithm, the proposed coherent detection algorithm can acquire a better antinoise performance and higher peak to sidelobe ratios along the Doppler frequency and the scaled range cell. Simulations and analyses for synthetic models and the real radar data are provided to verify the effectiveness of the proposed coherent detection algorithm.

Ground Maneuvering Target Imaging and High-Order Motion Parameter Estimation Based on Second-Order Keystone and Generalized Hough-HAF Transform

This paper proposes a new method to focus a ground moving target with complex motions and estimate its motion parameters in a synthetic aperture radar (SAR) system. In this method, the second-order Keystone transform is applied to correct the range curvature. Then, the Hough transform is applied to estimate the slope of the range walk trajectory, from which the target cross-track velocity is obtained. Finally, a generalized Hough-high-order ambiguity function (GHHAF) transform is applied to transform the target signal into a 2-D time-frequency plane and estimate its slope associated with the third-order Doppler parameter. Compared with the conventional SAR imaging methods using the second-order phase model, the proposed method can obtain better imaging quality since the third-order Doppler frequency migration is effectively eliminated. Both simulated and real data processing results are provided to validate the effectiveness of the proposed algorithm.

ISAR Imaging of Targets With Complex Motions Based on Modified Lv’s Distribution for Cubic Phase Signal

For targets with complex motions, such as highly maneuvering airplanes and ships fluctuating with oceanic waves, the Doppler frequencies of scatterers are actually time-varying and azimuth echoes of a range cell have to be modeled as multicomponent cubic phase signals (CPSs) after the range alignment and the phase adjustment. In inverse synthetic aperture radar (ISAR) imaging based on the CPS model, the chirp rate and the quadratic chirp rate are identified as causes of the image defocus. In this paper, by employing a novel parametric symmetric self-correlation function and the keystone transform, an effective estimation algorithm, known as the modified Lvs distribution (MLVD), is presented for the CPS and applied to ISAR imaging of targets with complex motions. The MLVD is simple and can be easily implemented using the complex multiplication, the fast-Fourier transform (FFT), and the inverse FFT (IFFT). The implementation, the cross-term, the antinoise performance, and the computational cost are analyzed for the MLVD. Compared to three representative estimation algorithms for the CPS, the MLVD can acquire a higher antinoise performance and eliminate the brute-force searching without the interpolation. Through simulations and analyses for synthetic models and the real radar data, we verify the effectiveness of the MLVD and the corresponding ISAR imaging algorithm.

ISAR Imaging for Fluctuating Ships Based on a Fast Bilinear Parameter Estimation Algorithm

For inverse synthetic aperture radar (ISAR) imaging of ships fluctuating with oceanic waves, azimuth echoes of a range cell have to be modeled as cubic phase signals (CPSs) after the range alignment and the phase adjustment. In ISAR imaging based on the CPS model, the chirp rate and the quadratic chirp rate are identified as causes of the target image defocus and need to be estimated with an effective algorithm. In this paper, a fast bilinear parameter estimation algorithm is proposed and applied in ISAR imaging for fluctuating ships by employing the cubic phase bilinear function, the nonuniform fast Fourier transform (NUFFT), and the parameter space switching method. Compared to two existing representative parameter estimation algorithms for the CPS, the advantages of this proposed estimation algorithm are: the computational cost is lower due to the NUFFT and the parameter space switching method and the bilinearity and the energy accumulation operation guarantee a higher anti-noise performance and a better suppression on cross-terms. Through simulations on synthetic models and the real radar data, we verify the effectiveness of this fast bilinear parameter estimation algorithm and the corresponding ISAR imaging algorithm.