Due to the range gate hopping and Doppler frequency modulation, the coherence of the high-speed targets radar echoes received via dechirp sampling is disrupted, leading to a significant decrease in the accuracy and robustness of radar imaging. To address this issue, a new method for echo coherence recovery in dechirp sampling radars is proposed. Assuming the range gate hops with a fixed step size, the radar’s reference range can be accurately estimated by combining range alignment (RA) and range unwrapping technique. Simultaneously, the target’s range and velocity can also be estimated by using polynomial smoothing. By deriving and analyzing the accurate dechip signal model, the coherence of the echo is restored by compensating for high-speed Doppler modulation, residual video phase (RVP) terms, and residual motion errors through the construction of corresponding functions. Finally, the effectiveness of this method is validated by simulation experiments.

The Fast-Imaging Formula (FIF) enables rapid radar image simulation through One-Shot ray-tracing. However, the classical Imaging Formula is approximate results under the conditions of small angle and bandwidth. The application of FIF to wideband high-resolution radar images is subject to discussion. This study constructs simulations by employing the '6+6' Degrees of Freedom (DoF) motion model, determining the imaging plane. Utilizing the Discrete Fourier Transform (DFT), the range profile formula for large bandwidth is obtained, and the result is frequency dependent. According to the principle of spectrum correction, the integration is modified to obtain the image primitive suitable for high-resolution image simulation. The simulation results of the ship target model and the range profile analysis of the ray tube verify the effectiveness of the proposed method.

Translational motion compensation plays an important role in the inverse synthetic aperture radar (ISAR) imagery. In this study, a new translational motion compensation algorithm for ISAR imaging under low signal-to-noise ratio (SNR) conditions is proposed. This method is formed based on the optimization of dynamic image sharpness metric, by which the translational parameters are accurately estimated from the returned signals. The important properties of the locally and globally optimal points of dynamic image sharpness function are proved and discussed by first using the dominant point-targets model. These properties are employed in the scheme to search for the globally optimal point and prevent the optimization being trapped at a locally optimal point. Based on the properties of optimal points and Gauss–Newton method, the algorithm to estimate the translational parameters by dynamic image sharpness metric optimization (DISMO) is devised. The DISMO can find the accurate translational parameters corresponding to the globally optimal point without being affected by local optima under low SNR conditions with high efficiency. Further, the translational compensation is completed based on the estimates. The proposed method is applied to simulated and real data. The processing results confirm the effectiveness of this new algorithm.

The microwave photonic (MWP) radar has the capability of generating ultrawideband (UWB) signals. It is a challenge to realize accurate extraction of attributed scattering centers (ASCs) from MWP signals. This manuscript presents a scattering parameter estimation method in the image domain for UWB MWP signals. The polar-to-rectangular resampling is required for UWB MWP signals. Therefore, a range-azimuth decoupled representation based on the ASC model is formed. The model parameter estimation is converted into an optimization problem, where the statistics of the target signal and the features of interest are modeled to provide prior information. The distribution spread maximization (DSM) and peak magnitude maximization (PMM) principles in the optimization embody this prior information. The particle swarm optimization (PSO) is utilized to search for the parameters of each ASC in the image domain. Moreover, the orthogonal matching pursuit (OMP) algorithm is introduced to avoid repeated computation. Experimental results conducted on the simulated data, XPATCH data, and real data confirm the effectiveness of the proposed method. The proposed method takes into account the specific features of UWB MWP signals, which are neglected in the existing studies. Therefore, the proposed method performs better in extracting ASC parameters from UWB MWP signals in terms of accuracy and more complete sets.

The microwave photonic (MWP) radar technique is capable of providing ultrawide frequency bandwidth waveforms to generate ultrahigh-resolution (UHR) inverse synthetic aperture radar (ISAR) imagery. Nevertheless, conventional ISAR imaging algorithms have limitations in focusing UHR MWP-ISAR imagery, where high-precision high-order range cell migration (RCM) and phase correction are crucially necessary. In this article, a UHR MWP-ISAR imaging algorithm integrating rotation estimation and high-order motion terms compensation is proposed. By establishing the relationship between parametric ISAR rotation model and high-order motion terms, an average range profile sharpness maximization (ARPSM) is developed to obtain rotation velocity by using nonuniform fast Fourier transform (NUFFT). Second-order range-dependent RCM is corrected with parametric compensation model by using the rotation velocity estimation. Furthermore, the spatial-variant high-order phase error is extracted to compensation by the entire image sharpness maximization (EISM). A new imaging framework is established with two one-dimensional (1-D) parameter estimations: ARPSM and EISM. Extensive experiments demonstrate that the proposed algorithm outperforms traditional ISAR imaging strategies in high-order RCM correction and azimuth focusing performance.

A suitable regularization parameter plays an important role in sparse ISAR imaging algorithms. With a proper regularization parameter, the quality of ISAR images improves. In this paper, the Homotopy re-weighted ℓ1-norm minimization is applied to ISAR imaging. This method is able to choose the accurate regularization parameter for each point in ISAR image with high efficiency. As a result, the imaging results processed by this method contain more details of the target and less artificial points. Both simulated and real data experiments validate the feasibility of the proposed method.

The curved trajectory can lead to severely 2-D spatial-variance in spaceborne synthetic aperture radar (SAR). The azimuth-variance makes the traditional frequency domain imaging algorithms for the straight trajectory based on the assumption of azimuth translational invariance invalid. To correct the severely 2-D spatial-variance in curved trajectory spaceborne SAR, this paper studies a frequency imaging algorithm based on an optimal linear range walk correction (LRWC) preprocessing and 2-D singular value decomposition (SVD). Before the correction of the 2-D spatial-variance, an optimal LRWC preprocessing is introduced to minimize the azimuth-variance. Subsequently, a range block-SVD is proposed to correct the range-variance and, thus, achieves the accurate range cell migration correction. Finally, the azimuth tandem-SVD method is used to correct the azimuth-variance and, thus, accomplishes the azimuth compression for the whole azimuth scene. Processing of the simulated data validates the effectiveness of the proposed algorithm.

Due to the very high orbit, the apparent features of geosynchronous synthetic aperture radar (GEOSAR) are the curved trajectory and long integration time, which can lead to severe coupling between the azimuth and the range directions and, therefore, complicates the resolution evaluation. The traditional analytical approach based on the 2-D division may produce large resolution error, and the numerical approach may suffer from huge computation burden. Therefore, an analytical resolution evaluation approach for GEOSAR based on the local feature of the ambiguity function is studied in this paper. The proposed approach is validated with simulation data to be of high efficiency and accuracy. In addition, the proposed approach is also demonstrated to be capable of evaluating the resolution for other complex platforms, and of evaluating the 3-D resolution of a SAR system.

The available focusing algorithms for medium-Earth-orbit (MEO) SAR are all based on the complex nonhyperbolic range equation, which may make it more difficult in imaging processing. In this paper, we model the range equation as the standard hyperbolic form based on the motion compensation (MOCO) principle. However, the conventional two-step MOCO may introduce azimuth spectrum expansion due to the potential large motion error, which can lead to severe azimuth ambiguity. To resolve this problem, we develop an omega-K algorithm based on a modified two-step MOCO and an adaptively straight equivalent (ASE)-velocity model. The algorithm is implemented through three-step processing: 1) the modified two-step MOCO does not compensate for the quadratic motion error (the main factor for the spectrum expansion); 2) an ASE-velocity model is introduced to compensate for the quadratic motion error; and 3) an extended Stolt mapping is proposed to perform the accurate range cell migration correction, and the tandem singular value decomposition-nonlinear chirp scaling algorithm is to correct the azimuth-variant phase error and to perform the azimuth compression. Processing of simulated data and airborne SAR real data validates the effectiveness of the proposed algorithm.

In a synthetic aperture radar (SAR) system, the radar platform may move with a curved trajectory due to the existence of vertical velocity and acceleration, which may result in the failure of the conventional imaging methods. In order to deal with this problem, this paper proposes an improved wavenumber-domain imaging algorithm for high-resolution-high-squint SAR with a curved trajectory. It mainly includes three aspects. First, a modified equivalent range model for a curved trajectory is derived. Second, an improved wavenumber domain imaging algorithm based on the proposed range model is analyzed in detail. Finally, the imaging distortion caused by vertical velocity and acceleration is corrected via geometry and inverse projection. Simulated results and Ku-band real SAR data processing are used to validate the proposed model and imaging algorithm.

Because the anisotropy characteristic shows some properties of scattering centers, an algorithm for extracting anisotropy characteristic of scattering centers when wide-angle SAR is used is proposed in this paper. Firstly, the echo from a single scattering center is analyzed. Based on the analysis, the estimation of anisotropy characteristic is transformed into an inverse problem of solving a single scattering center's amplitude by using identity matrix as orthonormal basis. Then the inverse problem is solved under the constraint that amplitude of scattering centers should be continuous. Finally anisotropy characteristic is extracted from the solution of the inverse problem. Estimating results of both Matlab simulated and electromagnetic computation data validate that the algorithm is precise and robust. Moreover, the proposed method is more efficient in comparison with traditional methods.

For high-squint synthetic aperture radar (SAR) with curved trajectory, the traditional hyperbolic range model (HRM) is inaccurate, and the variation of velocity caused by constant acceleration in azimuth dimension cannot be ignored. Thus, the traditional omega-K imaging algorithms based on HRM are no longer available. For this problem, this letter proposes an improved range model, which can precisely take account for the curved trajectory. Then, a modified omega-K algorithm based on this new range model is proposed for high-squint SAR imaging. Simulation results validate the effectiveness of the improved range model and imaging algorithm.

The imagery of highly squinted synthetic aperture radar mounted on maneuvering platforms with nonlinear trajectory is a challenging task due to the existence of acceleration and the cross-range-dependent range migration and Doppler parameters. In order to accommodate these issues, a frequency-domain imaging algorithm based on tandem two-step nonlinear chirp scaling (TNCS) with small aperture is proposed. For the cross-range-dependent range cell migration (RCM) caused by the linear range walk correction and acceleration, the first-step NCS is introduced to suppress this dependence and realize the unified RCM correction. Based on the differences between full-aperture and small-aperture data in the cross-range processing, the second-step NCS is introduced in frequency domain to equalize the cross-range-dependent Doppler parameters, for cross-range processing is more sensitive to the cross-range dependence than range processing. Furthermore, a novel geometric correction method based on inverse projection is utilized to eliminate the negative effects caused by the imaging processing. Simulation results and real data processing are presented to validate the proposed approach.