The aerial targets with maneuvering trajectories can result in severe defocusing in inverse synthetic aperture radar (ISAR) images, while the low signal-to-noise ratio (SNR) from remote observations can also pose challenges to imaging. However, due to the stable motion state of satellites and their higher speeds compared to aerial targets, the accumulation time required for imaging is relatively short. This makes the impact of aerial target trajectory maneuvers on imaging effects relatively minor, providing a significant advantage for robust imaging of aerial targets with maneuvering trajectory under a spaceborne SAR/ISAR hybrid mode. This letter proposes an algorithm for robust imaging of aerial targets with maneuvering trajectory based on multioptimization strategies (OSs) under a spaceborne SAR/ISAR hybrid mode. In this algorithm, robust imaging is divided into two parts: 1) multistep optimal imaging time interval selection method based on OSs (MS-OITI-OSs). Through multiple optimization steps based on the target’s aerial trajectory and image information, the method achieves robust selection of the OITI by utilizing an “attitude first, quality later” optimization strategy, reducing computational complexity and increasing imaging success rates; and 2) joint translational motion compensation method based on OSs (JTMC-OSs). Characterizing motion parameters using a polynomial model, the method optimizes the motion parameters using image entropy as the objective function through the Grasshopper optimization algorithm (GOA). During the optimization process, a “high-order first, low-order later” optimization strategy is employed based on the impact of motion parameters on imaging quality to achieve robust translational motion compensation. The proposed algorithm enables robust imaging of aerial targets with maneuvering trajectory based on multi-OSs under a spaceborne SAR/ISAR hybrid mode. Extensive experimental validation confirms the effectiveness and robustness of the proposed method.

Measuring target angles is a crucial issue in airborne radar detection. Traditional dynamic programming track-before-detect (DP-TBD) often operates in low signal-to-noise ratio (SNR), posing challenges to angle measurement and localization. This letter presents an angle estimation technique for TBD processing. It interprets target angles across multiple frames using initial frame angle (IF-AG) and inter-frame angular rate of change (IF-ARC). Compensating for the latter and performing noncoherent accumulation to improve SNR before estimating the IF-AG enhance multiframe target angle estimation precision. The method initially formulates the cost function for multiframe angle measurement based on the raw data extracted from the multiframe array by DP-TBD. It subsequently estimates and compensates for IF-ARC, related to velocity, followed by IF-AG estimation, providing target angles for all frames concurrently. Simulation results validate the effectiveness of this method.

The acquisition of a well-focused and high-resolution inverse synthetic aperture radar (ISAR) image of a ship target is crucial for accurate target classification and recognition. In practical scenarios, ship targets exhibit complex maneuverability, and radar platforms demonstrate nonstationary behavior, which poses a serious challenge to conventional ISAR imaging algorithms. To address this problem, this article proposes a high-success-rate and fast ISAR imaging algorithm of nonstationary moving platform-attitude rapidly changing ship target (NSMP-ARCST) with Dempster-Shafer (DS) evidence and minimum entropy theory. The proposed algorithm employs multiple metrics to evaluate the imaging results and leverages the DS evidence theory to fuse these metrics for optimal imaging time interval (OITI) selection, aiming to enhance the success rate and robustness of ISAR imaging. Furthermore, this article introduces an improved fixed-point iterative minimum entropy phase-adjustment (IFPI-MEPA) method to optimize the ISAR imaging quality and computational speed under low signal-to-noise ratio (SNR) conditions, which contributes to an increased success rate of OITI selection and reduced computational complexity, thereby endowing it with substantial practical applicability. Experimental results using both simulated and real measured data illustrate the effectiveness and robustness of the proposed algorithm.

The high orbit height leads to the ultralow signal-to-noise ratio (ULSNR) of geosynchronous (GEO) targets, so it is necessary to increase the coherent processing interval (CPI) to improve the coherent accumulation gain. The ultra-long CPI (ULCPI) causes wide rotational angles and complex signal modulation, which poses a serious challenge to conventional inverse synthetic aperture radar (ISAR) imaging algorithms. Moreover, the high orbit height and high-speed motion of GEO targets make the “stop-and-go” model no longer applicable, and intra-pulse motion errors must be taken into consideration. To address the problems, this article proposes a high-resolution ISAR imaging algorithm with joint intra-pulse and inter-pulse high-order motion compensation (JIPHOMC) and sub-aperture images fusion for GEO targets at ULCPI. In this technique, ULCPI is divided into several sub-apertures. Then, with respect to the motion compensation in sub-apertures, we innovatively propose a JIPHOMC algorithm with particle swarm optimization (PSO), which can correct the complex spatial-time-variant (STV) motion errors caused by wide rotational angles and high-speed motion, and eliminate the 2-D high-order coupling. By utilizing image overall constraints, JIPHOMC avoids signal source decomposition processing, thereby boosting algorithm efficiency and robustness. For the sub-aperture images obtained from different perspectives, we develop a sub-aperture image fusion algorithm (SAIF) based on non-negative matrix factorization (NMF) of structured weighted sparse enhancement, which can ensure the integrity of the target structure and enhance the image signal-to-noise ratio (SNR), so as to achieve high-resolution ISAR imaging of GEO targets at ULCPI. Extensive experiments based on both scattering point simulation data and electromagnetic calculation data corroborate that the proposed algorithm outperforms traditional ISAR imaging methods for GEO targets at ULCPI.

The$L$-type double baseline configuration (three antennas) radar system is commonly adopted to obtain 3-D images in the traditional interferometric inverse synthetic aperture radar (InISAR) imaging, making high demands on the complexity of hardware design and signal processing. In this letter, a novel InISAR imaging framework based on single baseline configuration (SBC) (two antennas) for maneuvering targets is proposed, which can acquire the range and azimuth coordinates of the targets through transmitting wideband signals and azimuth scaling. To address the problems of error transmission and insufficient robustness in the traditional cascaded motion compensation method, a joint motion compensation and azimuth scaling (JMCAS) algorithm is developed. By maximizing the image contrast (IC), this method can perform the optimal parameter estimation of translational and rotational motion of maneuvering targets, so as to simultaneously achieve the fine motion compensation and azimuth scaling. In addition, a non-coherent fusion image registration (NCFIR) algorithm is presented to achieve the image registration between the two antennas in a vertical direction. On this basis, the height coordinates of the targets can be obtained by means of interferometric processing. Extensive experimental results from both simulated and real data corroborate that the proposed algorithm can achieve high-precision 3-D imaging of maneuvering targets with low hardware complexity at a low cost.

Ultrawideband (UWB) radar can achieve ultrahigh-resolution inverse synthetic aperture radar (ISAR) imaging of noncooperative targets by transmitting UWB signals. However, the spatial-variant (SV) high-order migration through range cell (MTRC) and phase errors produced by the UWB radar system have seriously challenged the feasibility of conventional ISAR imaging algorithms. Moreover, maneuvering targets has exacerbated this problem compared with the steady ones. In this article, a UWB ISAR imaging algorithm of maneuvering targets with joint high-order motion compensation and azimuth scaling (JHOMCAS) is proposed. For the azimuth SV linear MTRC and 2-D SV high-order MTRC caused by the maneuvering rotational motion of the targets, the cascaded generalized keystone transform (GKT) is adopted for precise correction. It is worth noting that, when eliminating the SV MTRC by the cascaded GKT, the 2-D SV high-order phase errors induced by the maneuvering rotational motion must be accurately compensated, or MTRC correction will fail. The traditional autofocus methods usually only address the phase errors shared by the total target without due attention to the fine SV property. In response to this problem, this article first develops a joint 2-D SV autofocus and azimuth scaling algorithm (JSVAAS) to achieve the integration of SV high-order phase error compensation and azimuth scaling. A JHOMCAS algorithm is proposed to perform the joint processing of GKT and JSVAAS, “GKT-JSVAAS-GKT. ” This approach helps accomplish the high-precision UWB ISAR imaging of maneuvering targets, and the well-focused and scaled UWB ISAR images obtained will build a sound foundation for target classification and recognition. Extensive experiments based on both scattering point simulation data and electromagnetic calculation data verify that the proposed algorithm outperforms conventional ISAR imaging approaches in UWB ISAR imaging of maneuvering targets.

Under high sea state, ship targets make complex maneuvering motions due to strong disturbances such as sea waves and sea winds. Selecting the optimal imaging time interval to shorten the coherent processing interval (CPI) can reduce the complexity of motion errors, but the imaging resolution is affected as well, i.e., the motion error complexity and imaging resolution are constrained by each other. To address this problem, this article proposes a novel inverse synthetic aperture radar (ISAR) imaging framework for complex maneuvering ship targets to achieve the integration of super-resolution (SR) ISAR imaging and fine motion compensation (ISRFMC) under high sea state. With regard to the complex maneuvering motion of ship targets, we analyze the motion errors caused by the time-variant rotational velocity and imaging projection plane (IPP) on the echo signals, respectively, and a fine phase error model is established to uniformly represent the dual time-variant characteristic (DTVC) of complex maneuvering ship targets. Moreover, a deformed Akaike information criterion (DAIC) is developed to realize the adaptive selection of the phase error model with the image sharpness as the objective function. Underpinned by the Bayesian compressive sensing (BCS) theory, the SR ISAR imaging can be realized by solving a sparsity-driven optimization problem via a modified quasi-Newton solver. Particularly, the fine phase errors are constructed as the model errors of image reconstruction, and the particle swarm optimization algorithm (PSO) is utilized to solve the maximum image sharpness optimization problem in order to perform the joint fine motion compensation and azimuth scaling (JFMCAS). ISRFMC or the integration of SR ISAR imaging and fine motion compensation can be achieved through alternate iteration, so as to obtain well-focused and scaled high-resolution ISAR images of complex maneuvering ship targets under high sea state. Extensive experiments based on both simulated and real data verify that the proposed algorithm is capable of addressing the conflict between imaging resolution and motion error complexity under high sea state.

The high orbit height and long coherent processing interval (CPI) of geosynchronous (GEO) targets lead to the problems of ultralow signal-to-noise ratio (ULSNR) and complex signal modulation, posing great challenges to the traditional radar target detection and imaging algorithms. To address the problems, this article proposes a novel model-data codriven integration algorithm of detection and imaging for GEO targets with wideband radar. In this technique, underpinned by the transformation relationships between multiple spatial coordinate systems and the orbit prior information of GEO targets, we deduce the analytical expressions of the effective rotational vector of GEO targets so as to accomplish the model-driven optimal subaperture selection for integration of detection and imaging (OSASIDI). This considerably improves the processing performance and algorithm efficiency compared with traditional data-driven methods at ULSNR. In addition, we derive the radar equation of GEO targets for integration of detection and imaging in detail, which guides OSASIDI by analyzing the impacts of different parameters on detection and imaging performance. Aiming at the complex signal modulation problem caused by ultralong CPI (ULCPI) during the optimal subaperture (OSA) at ULSNR, we innovatively propose a model-data codriven integration of detection and imaging algorithm (MDCDIDI), which can eliminate the complex spatial-time-variant motion errors caused by the dual time-variant characteristic (DTVC) of effective rotational vector, so as to realize the focus-before-detection and obtain the well-focused inverse synthetic aperture radar (ISAR) images. Extensive experimental results from simulated data, which are generated from actual GEO parameters and the computer-aided-design (CAD) model of the Tiangong-I (TG-I) satellite, corroborate the effectiveness of the proposed algorithm.

The quality of inverse synthetic aperture radar (ISAR) images suffers seriously from the two-dimension (2-D) resolution and noise. The motion errors arising from translational and rotational motion further aggravate the image defocusing. For the limited bandwidth and short aperture (LB-SA) signal, this letter proposes a novel 2-D joint super-resolution (2D-JSR) ISAR imaging with joint motion compensation and azimuth scaling (JMCAS) algorithm. In this technique, a 2D-JSR signal model is established, enabling the 2-D high-resolution ISAR image to be generated by solving a sparsity-driven optimization problem with a modified quasi-Newton solver. In addition, a new JMCAS algorithm is developed to enhance the focusing performance of image. Not only can this algorithm jointly correct the range shift and phase error caused by translational motion, it can also complete the azimuth scaling and range spatial-variant phase error (RSVPE) compensation simultaneously. Through the iterative processing of 2D-JSR reconstruction and JMCAS, the well-focused and scaled high-resolution ISAR image can be obtained. Both simulated and real data experiments are provided to verify the effectiveness of the proposed algorithm.

In the actual scene of interferometric inverse synthetic aperture radar (InISAR) imaging, the noncooperative targets may make a nonuniform 3-D rotational motion (3-D-RM), which contributes not only to the time-variant Doppler modulation but also to the spatial-variant wave path difference (SVWPD). This, in turn, seriously degrades the 3-D geometry reconstruction accuracy of the targets. Furthermore, it is an enormous challenge to realize InISAR imaging from sparse frequency band and sparse aperture (SFB-SA) signals. This article seeks to address the problems of fine image registration and 2-D joint sparse reconstruction (2-D-JSR) for InISAR imaging with SFB-SA signals. With regard to the maneuvering targets with 3-D-RM, a novel SVWPD signal model is established. Moreover, a new algorithm, named joint wave path difference compensation (JWPDC) algorithm, is developed to perform fine image registration. It can not only combine multiple channels to achieve image registration but also jointly compensate for the non-SVWPD (NSVWPD) and SVWPD. A joint multichannel 2-D-JSR (JMC-2-D-JSR) ISAR imaging algorithm is also proposed according to the SFB-SA signal model to produce high-resolution ISAR images. Underpinned by the Bayesian compressive sensing (BCS) theory, the JMC-2-D-JSR ISAR imaging can be realized by solving a sparsity-driven optimization problem via a modified quasi-Newton solver. Through iterative processing of JMC-2-D-JSR and JWPDC, the high-quality 3-D InISAR images of maneuvering targets with 3-D-RM can be obtained. Extensive experimental results based on both simulated and real data corroborate the effectiveness of the proposed algorithm that outperforms other available InISAR imaging frameworks in 2-D imaging, 3-D imaging, and motion compensation.

With regard to the multifunction radar transmitting sparse stepped-frequency-modulation (SSFM) signal for inverse synthetic aperture radar (ISAR) imaging, the received echo signal is usually sparse in two dimensions, i.e., sparse stepped-frequency-modulation and sparse aperture waveforms (SSFM-SAWs), and there are translational and rotational motion errors between subpulses. The two problems seriously challenge the feasibility of conventional 1-D sparse reconstruction algorithms. This article proposes a novel high-resolution ISAR imaging and motion compensation with the 2-D joint sparse reconstruction (2D-JSR) algorithm. In this technique, a 2D-JSR dictionary is established according to the SSFM-SAW signal model. Based on the Bayesian compressive sensing (BCS) theory, the 2D-JSR is then transformed into solving a sparsity-driven optimization problem with l1-norm constraint. With the accommodation of a modified quasi-Newton solver, the exact recovery of SSFM-SAW can be achieved. In addition, a new algorithm, named joint translational motion compensation and range spatial-variant autofocus (JTSVA) algorithm, is also developed to realize motion parameters by a two-step estimation. Integrating with 2-D coupling information of echo signal and the efficient and robust motion compensation algorithm, the accurate motion parameters together with well-focused and scaled high-resolution ISAR images can be obtained. Extensive experiments based on both simulated and real data demonstrate that the proposed algorithm is capable of the precise reconstruction of ISAR images and the effective suppression of both motion errors and noise.