The radar echo of a target can be modeled as a 2-D signal whose variables are the intra-pulse sampling time (fast-time) and the inter-pulse sampling time (slow-time). The fast-time instantaneous frequency (FIF) and slow-time instantaneous frequency (SIF) of the signal are modulated by the target's slant range. When the target undergoes complex motion, the radar echo becomes a 2-D signal with fast-varying instantaneous frequencies (IFs). IF analysis for such a signal is challenging. To solve this issue, an extending complex-lag time-frequency distribution (ECTD) is introduced. ECTD is a 3-D distribution for 2-D signals based on traditional complex-lag time-frequency distribution (CTD), and it inherits the good performance in handling fast-varying IFs. By introducing complex-lags in both the fast-time and slow-time dimensions, the ECTD can accurately estimate the SIF and FIF of a 2-D signal with fast-varying IFs. Finally, a reduced interference realization of the ECTD achieved by introducing the frequency domain filter is given. Numerical examples validate the effectiveness of the ECTD. The source code is provided athttps://github.com/XinhangZhu/ECTD.

Single-channel synthetic aperture radar (SAR) can be applied to reconstruct the 3-D structure of maritime targets with the advantages of concise hardware design and lower data rate. Nevertheless, two problems should be solved to enable it, which are: 1) 2-D radar image focusing and 2) scatterer tracking. In this article, an integral 3-D reconstruction algorithm is proposed for solving these problems. First, the radar image is refocused by using the hybrid SAR and inverse SAR (ISAR) imaging technique. Second, a novel brightest scatterer (BS) extraction method is proposed combined with a clustering algorithm, which is effective and easy to implement. Third, a fast and robust scatterer tracking algorithm (STA) is presented to obtain the complete scatterer trajectories and to effectively enable the factorization method. Moreover, the proposed STA is applicable also in the presence of numerous scatterers and of scatterer scintillation. Afterward, the factorization method is employed to reconstruct the 3-D structure of the ship target. Finally, the effectiveness of the proposed algorithm is shown with the results of simulated and real measured data.

Integrated sensing and communication (ISAC) is one of the core functions of 6G. Therefore, it is essential to develop sensing and imaging technologies for environmental reconstruction utilizing the existing communication infrastructure. This article proposes a novel tomographic bistatic 3-D imaging and coordinate reconstruction method based on the uplink communication process for the first time. First, this article presents the relationship between the imaging parameters and the communication system and the way to extract echo for imaging from that received by the communication system. Then, a scenario involving base station (BS)-building-user equipment (UE) imaging is proposed, and the model-based principle of the tomographic synthetic aperture radar (TomoSAR) elevation reconstruction is derived. Finally, due to the dense distribution of 6G BSs, different from the traditional spaceborne or airborne systems, the imaging targets are situated in near-field areas. Therefore, the 2-D backprojection (BP) imaging distorts the true spatial location of the targets, leading to differences between the actual positions and the image results. To address this, this article proposes a 3-D coordinate reconstruction algorithm based on BP near-field imaging. The core idea behind this is to transform the 3-D coordinate reconstruction problem into a nonlinear least-squares problem. The simulation experiments demonstrate the effectiveness of the proposed method.

The sparse aperture (SA) phenomenon, when encountered in the context of inverse synthetic aperture radar (ISAR) imaging, poses a formidable challenge in acquiring high-resolution ISAR images to establish ground truth. This challenge imposes limitations on the practical implementation of data-driven SA-ISAR imaging methods. In response to this issue, we introduce a novel deep learning approach called Parallel ISTA Net (PIN) to enhance the quality of SA-ISAR imaging and reduce the reliance on high-quality labeled data. The model is an interpretable deep unfolding model that achieves self-supervised training through a parallel architecture. The PIN model uses a multi-sampling matrix training strategy to enhance the robustness of the network through the symmetry constraints provided by the parallel framework. In addition, we introduce a weighting factor to adjust the loss function to improve imaging quality further. Real-measured data imaging results show that this method can achieve robust imaging performance comparable to supervised methods at low sampling rates of 50% and 25%.

Inverse synthetic aperture radar (ISAR) imaging can be compromised by sparse apertures (SAs) leading to diminished image quality. While deep learning models exhibit significant potential for SAs ISAR imaging, existing methods often expand network capacity by stacking layers to enhance imaging outcomes, thereby complicating the training process. Furthermore, the adaptability of the network may be constrained by the fixed computational graph. To address this problem, we propose the ResDEQ model, a novel SAs ISAR imaging method based on a deep equilibrium framework and a single-layer ResNet. ResDEQ decouples the forward and backward propagation processes using fewer parameters, enhancing network flexibility and adaptability to various imaging requirements. Experimental results indicate that ResDEQ surpasses traditional layer-stacked networks in imaging performance.

Although the bistatic forward-looking synthetic aperture radar (BFSAR) system can realize the imaging of the forward-looking area, the bistatic configuration brings new problems, such as synchronization errors. In this letter, the frequency synchronization error in the BFSAR system is discussed in detail, and its effects on target imaging are presented from theoretical analysis and simulation experiments. In addition, a refocusing algorithm based on the particle swarm optimization (PSO) method is proposed to compensate for the frequency synchronization error. Simulation results verify the feasibility of the proposed algorithm.

An optimal imaging time selection method is proposed based on the distributed interferometric inverse synthetic aperture radar (D-InISAR), aimed to achieve 3-D imaging for the ship targets. This study focuses on two main aspects in the imaging time selection, including the selection of the imaging moment and the determination of the available length of the imaging interval. First, the geometry and echoes model of distributed system with an “L” array are discussed, corresponding to different equivalent rotations of the ship targets, and the selection principle of the imaging projection plane (IPP) is proposed subsequently. Then, the top view images are generated by a novel optimal imaging time selection method based on the distributed system. Furthermore, the height information is obtained by the interferometry technique. Finally, all the theoretical analyses and derivations are verified by several simulation experiments in this letter.

Due to the large imaging scene of geosynchronous synthetic aperture radar (GEO SAR), multiple ship targets are more likely to appear in the same imaging scene. When the targets overlap in the range dimension, their echo signals after range compression will also overlap and interfere with each other. Besides, the stop-and-go assumption is no longer valid for GEO SAR, and a long coherent processing interval (CPI) will result in significant range migration. In such a scenario, essential imaging processes, including the signal division algorithm for eliminating the overlap interference and the focusing process for echo signal parameter estimation, are difficult to implement. To address these issues, a signal division and focusing algorithm based on multiscale fast-time delay fractional Fourier transform (MFDFrFT) is proposed in this article. To describe the relationship between slant range and propagation distance under the non-stop-and-go assumption, a conversion ratio is introduced into the slant range model. Then, the echo signal model can be characterized as a multicomponent 2-D quadratic phase signal with fast-time delay (2-D FD-QPS). In order to directly analyze the parameters of this signal, a linear reversible transformation named MFDFrFT is proposed. Then, the echo signal of each individual target can be divided, and the estimated parameters can be achieved. With the estimated parameters, the focusing process can be implemented to finally obtain the well-focused images of the targets. Simulation and equivalent experiments are provided to validate the effectiveness of the proposed algorithm.

Due to scatterer occlusion and missing during the observation time, the acquisition of the complete scatterer trajectory matrix is a hard task, which is the most important process in 3-D inverse synthetic aperture radar (ISAR) imaging of space targets based on 2-D ISAR image sequence. To cope with this problem, a novel fast scatterer trajectory association algorithm based on the cluster-based nearest neighbor standard filter (CNNSF) combined with the general Hough transform (GHT) is proposed. First, a modified dynamic equation for Kalman filter (KF) based on the scatterer projected trajectory model is derived. Then the state vector and covariance for the new dynamic equation are initialized by utilizing the GHT. Finally, the complete scatterer trajectory matrix is achieved efficiently by utilizing the proposed trajectory association algorithm based on the CNNSF algorithm. Simulation results demonstrate the correctness of the dynamic equation and the effectiveness of the proposed algorithm.

Bistatic forward-looking synthetic aperture radar (BFSAR) can create a highly focused image of a target in the flight direction of the receiver. However, most research on BFSAR imaging is aimed at stationary targets, with relatively few studies on ship targets. We propose DPEP, an estimate of Doppler parameters, based on particle swarm optimization (PSO) for ship target imaging in the BFSAR system. The relationship between the Doppler parameters and those of the motion of a ship target is derived from the geometry of the BFSAR system. PSO is adopted to accurately estimate the Doppler parameters, so as to construct the functions for compression. The proposed algorithm is validated and compared to traditional algorithms through simulations.

Synthetic aperture radar (SAR) system is susceptible to electromagnetic jamming during imaging, which seriously affects the subsequent interpretation of SAR images. Aiming at the problem of active suppressive jamming, this paper proposes a suppression method of SAR suppressive jamming based on self-supervised complex-valued deep learning, which consists of a novel complex-valued jamming suppression network CV-MUNet++ and a self-supervised training strategy. CV-MUNet++ could fully use the amplitude and phase information of complex-valued SAR images. The network’s weights, activation functions, and convolution operations are designed for complex domain processing. The different information representations of target and jamming in amplitude and phase in SAR images are mined to achieve jamming suppression. The self-supervised training strategy is proposed to solve the problem of relying heavily on manually labeled samples in the traditional network training process and is suitable for application scenarios where ground truth is difficult to obtain under complex jamming. The experimental results show that the proposed method could effectively suppress the active jamming of complex backgrounds and has the ability to self-supervised intelligent jamming suppression.

It is well known that the bistatic synthetic aperture radar (BiSAR) can obtain high-resolution imaging in the forward direction of radar, which is not possible with the conventional monostatic SAR systems. However, most studies on BiSAR imaging mainly focus on stationary targets, and the research on moving targets and ship targets is relatively scarce. In this article, a monostatic-equivalent (ME) algorithm via Taylor expansion for BiSAR imaging of ship target is proposed. The monostatic equivalence method based on Taylor expansion is adopted to approximate BiSAR to monostatic model; besides, the method based on the midpoint of baseline is also analyzed for comparison. Then, the hybrid SAR-ISAR technology is utilized for ship target imaging under the ME model. Finally, the experimental results are given to substantiate the effectiveness and robustness of the proposed algorithm.

Bistatic synthetic aperture radar (SAR) has received increasing attention from researchers due to the advantage of achieving forward-looking imaging, which cannot be achieved by a monostatic SAR system. Most of the existing literatures focus on the airborne bistatic SAR imaging, while the research on satellite-missile borne bistatic SAR system is relatively scarce. In this paper, the characteristics of the satellite-missile borne bistatic forward-looking SAR (SMBFSAR) system is mainly studied. Firstly, the method of modeling a SMBFSAR system, including the establishment of the satellite orbit equation and coordinate system transformation, is described at length. Then, the two-dimensional resolution of the SMBFSAR system is redefined, and the relationship between the two-dimensional resolution and the configuration of SMBFSAR is discussed in detail. Finally, the analysis of two-dimensional resolution of SMBFSAR system is illustrated by the simulation results of a ship target in different configurations.

Underwater acoustic localization (UWAL) of the black box for a sunken airplane utilizing an autonomous underwater vehicle (AUV) is a useful technique in ensuring traffic safety. Aiming at improving localization precision, this article proposes a new path-planning algorithm based on the improved artificial potential field (APF). Compared with the conventional APF, we modify the conventional gravitation force and introduce a new localization precision force. Therefore, a balance between localization precision and obstacle avoidance is achieved, and the localization precision is significantly improved. The lake trial result validates the effectiveness of the proposed method.

Parameter estimation of micro-motion is essential to the feature extraction for uncooperative targets. Precession is a common form of micro-motion for space targets. However, the existing estimation methods for precession parameter need a long-term continuous observation causing a large consumption of radar system resources. Intermittent observation can improve the detection efficiency. Nevertheless, if the echo signal is discontinuous, the range variation caused by nutation motion will severely challenge the usability of parameter estimation methods that are based on pure precession conditions. Therefore, this letter puts forward a novel parameter estimation algorithm for precession targets with intermittent observation. First, the correlation method of echo signal’s envelope is employed to estimate the coning frequency and self-spinning frequency. Second, trigonometric function transformation is adopted for the phase unwrapping of discontinuous echo signal. Third, matching pursuit and 1-D search are applied to the reconstruction of sliding scattering center’s range history. Finally, effective estimation of precession parameter is realized based on a two-view range history analysis. The proposed algorithm is verified by the experiment that is based on the electromagnetic analysis data.

Inverse synthetic aperture radar (ISAR) is an effective detection method for targets. However, for the maneuvering targets, the Doppler frequency induced by an arbitrary scatterer on the target is time-varying, which will cause defocus on ISAR images, and bring difficulties for the further recognition process. It is hard for traditional methods to well refocus all positions on the target well. In recent years, generative adversarial networks (GAN) achieves great success in image translation. However, the current refocusing models ignore the information of high-order terms containing in the relationship between real parts and imaginary parts of the data. To this end, an end-to-end refocusing network, named Complex-valued Pix2pixHD (CVPHD) is proposed to learn the mapping from defocus to focus, which utilizes complex-valued (CV) ISAR images as input. A complex-valued instance normalization layer is applied to mine the deep relationship between the complex parts by calculating the covariance of them and accelerate the training. Subsequently, an innovative adaptively weighted loss function is put forward to improve the overall refocusing effect. Finally, the proposed CVPHD is tested with the simulated and real dataset, and both can get well-refocused results. The results of comparative experiments show that the refocusing error can be reduced if extending the pix2pixHD network to the CV domain and the performance of CVPHD surpasses other autofocus methods in refocusing effects. 1The code and dataset have been available online (https://github.com/yhx-hit/CVPHD).

A novel method for three-dimensional (3-D) imaging of the ship target is presented in this paper, combining the distributed inverse synthetic aperture radar (ISAR) and interferometry technique, which is named as the distributed interferometric ISAR (D-InISAR). This method enhances the azimuth resolution for the ISAR imaging procedure and improves the 3-D coordinate reconstruction accuracy for the ship target. A novel D-InISAR configuration with a rectangular array is presented firstly. Then, the phase correction method is proposed to preserve the complete phase information for interferometry. After that, the 3-D imaging algorithm base on D-InISAR is proposed and a novel evaluation criterion is proposed to quantify the reconstruction accuracy. Finally, the numerical simulations of the ship target are presented to verify the effectiveness and the robustness of the proposed algorithm.

Precession of the cone-shaped warhead is the typical micromotion of ballistic targets, and 3-D inverse synthetic aperture radar (ISAR) imaging of the precession warhead is of great significance for ballistic target identification. This letter proposes a novel method for the precession parameter estimation and the 3-D ISAR imaging of the cone-shaped target. First, the imaging model of the precession cone-shaped target is developed, and the relationship between the radial-range curve on the range–slow-time plane and the precession parameters is derived. Subsequently, the wideband measurements from two-aspect radars are jointly processed to solve the target precession parameters implicitly involved in the radial-range curve of the scatterer at the cone top. Finally, based on the estimation of precession parameters, the 3-D images of the target can be reconstructed, which reveal the structure characteristics and spatial attitude of the target. Simulation results verify the effectiveness of the proposed method.

For the target with 3-D rotation, the 3-D synthetic aperture radar (SAR) imaging is very important for the feature extraction and classification. To solve this issue, a novel 3-D imaging algorithm through the airborne SAR system with a single antenna is proposed in this article, which has great advantages for the simplification of system structure compared with the traditional interferometric system. The proposed 3-D airborne SAR imaging algorithm can be implemented with three steps: 1) the azimuth signal is modeled as multicomponent linear frequency modulation (LFM) signal due to the relative movement between the target and radar. 2) The scatterer height position can be obtained by estimating the frequency modulation rate (FMR) for the LFM signal. 3) The 3-D airborne SAR image is obtained via the range-Doppler (RD) algorithm. Furthermore, the reconstruction performance under different rotation patterns, including roll, pitch, and yaw, is analyzed. The availability of the presented novel technique is demonstrated by the results of simulated and real experimental data.

For the convenience of target feature extraction, classification, and recognition, it is necessary to research the imaging projection plane (IPP) of spaceborne hybrid synthetic aperture radar (SAR) and inverse SAR (ISAR) imaging results of ship target on the sea surface. In this article, a novel analysis approach of IPP is proposed, by which the expressions of IPPs can be derived under the condition of different rotation and ship’s bow directions. Then, the representation of radar image can be predicted in advance, i.e., top view, side view, or hybrid view of the target. Finally, the simulated and real measured data are processed to generate the radar images to verify the correctness of conclusions in this article.

Micro-motion parameter estimation is a significant part in feature extraction of non cooperative targets, and precession is the common micro-motion form of space targets. However, most of the existing precession parameter estimation algorithms are based on pure precession condition assuming a constant nutation angle. Moreover, they do not consider the problems caused by nutation, such as a worse estimation accuracy, difficulties in estimating some parameters, and ambiguous frequency judgment. At the same time, the echoes from different scattering attribute components of the actual target also challenge the applicability of the existing algorithms. Therefore, this article proposes a robust and unambiguous algorithm for multidimensional parameter estimation. First, according to the modulation characteristics of micro-motion, the echoes from different scattering attribute components of the target are identified and separated for the applicability of the estimation algorithm. Second, considering the model of range variation curve, a robust way for precession parameter estimation is proposed to avoid worse accuracy and difficult estimation. Third, unambiguous judgment criteria are established by analyzing frequency spectrum distribution. Finally, multidimensional parameter estimation is realized, including motion feature and structure feature of the target. The proposed algorithm is also verified by electromagnetic analysis data.

For inverse synthetic aperture radar (ISAR) imaging of maneuvering targets, the Doppler frequency induced by an arbitrary scatterer on the target is time-varying, and hence, the traditional range-Doppler (RD) algorithm is not appropriate for obtaining focusing ISAR images. In such a scenario, a novel ISAR technique, called the range instantaneous Doppler derivative (RIDD) algorithm, has been recently proposed, and two different kinds of well-focused ISAR images, named range-Doppler centroid (RDC) frequency image and range-Doppler frequency rate (RDR) image, can be obtained. In order to improve the accuracy of the target classification and recognition, the scaling approach for the RIDD algorithm must be taken into consideration. In this article, the point spread function (PSF) representations and cross-range scaling factors of the RDC image and the RDR image obtained by the RIDD algorithm are demonstrated analytically. On the basis of the PSFs and cross-range scaling factors, a novel imaging and scaling approach for the RIDD algorithm in the presence of a low SNR environment based on smoothed integrated high-resolution time–frequency-rate representation (SIHR) combined with coherent integrated smoothed generalized cubic phase function (CISCF) is proposed. As the proposed algorithm has high estimation accuracy and good antinoise capability, precise parameter estimation of the received signal can be achieved. Hence, high-quality scaled RDC and RDR images of the target can be reconstructed by the proposed approach. Experimental results of simulated data and real measured data verify the correctness of the PSF representations and demonstrate the effectiveness of the imaging and scaling approach proposed in this article.

Interferometric inverse synthetic aperture radar (InISAR) has proven to be an effective tool for 3-D imaging of noncooperative targets. The traditional InISAR imaging algorithms are almost entirely based on the assumption of a constant amplitude polynomial phase signal (PPS) model. However, target’s maneuverability tends to cause the echo signal to exhibit the characteristic of time-varying amplitude (TVA) in practice. To remedy this problem, a novel InISAR imaging algorithm for maneuvering targets based on quadratic frequency modulated (QFM) signal model with TVA is presented. First, the echo of each range cell is modeled as a multicomponent TVA-QFM signal. Then, through the matrix derivation, the parameter estimation problem of this signal is converted to a convex optimization problem. Subsequently, with the help of the scaled Fourier transform (SCFT), an efficient iterative update approach based on alternative direction method of multipliers (ADMM) framework is proposed to solve this optimization problem. Furthermore, associated with the range instantaneous Doppler (RID) method and multichannel interference technology, 2-D ISAR images and 3-D space shape of the target can be generated. Finally, some simulation results are provided to evaluate the effectiveness and robustness of the proposed algorithm.

Underwater acoustic localization (UWAL) is widely applied in ocean exploration, and a key issue for the high-rate time-of-arrival (TOA)-based UWAL method is to select the direct signal in the multipath acoustic channel. However, the existing direct signal selection methods suffer from a deterioration of the selection accuracy since the received signals from adjacent periods are mixed with each other. Addressing the problem, we propose a high-rate direct signal selection algorithm in this article. First, we choose the amplitude, TOA, bandwidth, and Doppler frequency of the detected pulses as the input features. Second, a direct signal classifier is established utilizing the decision tree. Finally, the localization model is built based on the TDOA information of the direct signal, and the localization precision analysis evaluates the performance of the UWAL method. Sufficient experiments, including simulation, tank experiment, and sea trial, are implemented to verify the effectiveness of the proposed method.

Compared with the traditional microwave synthetic aperture radar (SAR), the terahertz SAR is much more sensitive to the high frequency vibration of the platform which needs to be compensated. This paper proposes a novel method for the terahertz SAR imaging in which compensation for these two motion errors (i.e., high frequency vibration and the traditional low frequency motion error) are considered simultaneously. The effectiveness of the proposed method is verified by measured terahertz SAR data.

In the high-resolution inverse synthetic aperture radar (ISAR) imaging of low-earth-orbit space targets, the received signal from one range bin can be modeled as a multicomponent cubic phase signal (CPS) after motion compensation. The chirp rates and the quadratic chirp rates of the multicomponent CPS need to be estimated with parameter estimation algorithms to obtain the focused ISAR image. In this article, a new parametric range-instantaneous-Doppler ISAR imaging method is proposed, which introduces a new algorithm for parameter estimation of multicomponent CPSs. The cross terms of the generalized cubic phase function (GCPF) are analyzed. By eliminating the cross terms in the ambiguity function domain, the coherent integrated smoothed GCPF (CISGCPF) is proposed to estimate the quadratic chirp rates. Numerical examples are provided to verify the accuracy of the parameter estimation and the effectiveness of the cross-term suppression for CISGCPF in processing multicomponent signals. Simulated and real data imaging results demonstrate the performance of the CISGCPF-based ISAR imaging method. Comparisons with the existing algorithms show that the proposed method is effective in reconstructing focused images with less fake scatterers.

In order to compensate for the shortcomings of inverse synthetic aperture radar (ISAR) imaging in target recognition, a three-dimensional (3-D) interferometric ISAR (InISAR) imaging technique is developed. The traditional InISAR imaging with three antennas usually assumes that the baselines are orthogonal to each other and the target moves steadily. However, in practical applications, due to the existence of system error and the unknown moving condition of the noncooperative targets, the baselines used for interference are difficult to be absolutely orthogonal and the movement of the target also tends to be complicated. In this article, a practical maneuvering target 3-D imaging algorithm based on the InISAR of an arbitrary three-antenna configuration is investigated. The main contributions of this article are as follows: 1) abstracted from an actual application scenario, a three-antenna InISAR system model with the baselines being nonorthogonal is established; 2) a joint translational motion compensation strategy, involving a joint accumulated cross correlation method for the envelope alignment and a joint Doppler centroid-tracking approach for the phase correction, is devised for the image coregistration in the range dimension; 3) a complicated movement model of the maneuvering target is described, and a fast high-resolution ISAR imaging algorithm maximum likelihood (ML)-fractional Fourier transform (FRFT), which incorporates the ML with the FRFT, is proposed; and 4) a coordinate correction technique is developed to eliminate the distortion of the 3-D image caused by the nonorthogonality of baselines. The effectiveness and performance of the proposed algorithm are evaluated by several simulations at the last of the article.

Cross-range scaling is an important procedure for inverse synthetic aperture radar (ISAR) imaging since the cross-range resolution is always unknown due to the noncooperative targets. In fact, the essential problem of scaling is to estimate the rotation velocity of the target from echoes. Considering that most of the scaling methods have limitations on the requirement of some prior knowledge of the target’s motion, difficulties in handling some complicated-structure targets, and low-signal-to-noise ratio (SNR) conditions, we propose a novel scaling approach based on the multidelay discrete polynomial-phase transform (DPT) combined with keystone transform. Different from the second-order-DPT-based method, the proposed method separates the different components on the 2-D spectrum of time and delay time, which avoids the interference of cross terms and enhances the anti-noise ability. The experiments on linear frequency-modulated (LFM) signals, simulated ISAR echoes, and real-measured data verify the effectiveness and robustness of the proposed method.

3-D interferometric inverse synthetic aperture radar (InISAR) imaging of ship targets has always been a hot and difficult issue in research, and how to preserve the interferometric phase while performing high-resolution 2-D ISAR imaging of the complex echoes is the key to 3-D InISAR imaging. In this letter, a novel 3-D InISAR imaging algorithm of the ship target based on joint cross S-method is proposed. First, the azimuth short-time Fourier transform (STFT) is applied to the 1-D range profiles after motion compensation and image coregistration. Then, extract the interferometric phase matrices after performing the joint cross S-method transformation of the STFT results along the two baselines. Besides, the S-method transformations are implemented recursively for the joint cross S-method transformed results to obtain high-resolution ISAR images. Finally, the 3-D InISAR imaging can be achieved by combining the phase matrices and ISAR images at the same time. Simulation results verify the effectiveness and superiority of the proposed algorithm.

A new moving target tracking method based on an improved Gaussian mixture probability hypothesis density (GMPHD) filter in a circular synthetic aperture radar (SAR) system is proposed. In order to obtain the temporal dynamic behavior of moving targets in a SAR scene, a multiple-target observation is first extracted from a sequence of clutter-suppressed subaperture circular SAR images. Then, a new multiple-target tracking model for the circular SAR system is established based on the dynamic behavior of moving targets. An improved labeled GMPHD filter with an adaptive target birth intensity is proposed to realize the moving target detection and tracking in a strong ground clutter environment, which is suitable for the circular SAR system. This method can not only provide accurate estimates for the number and states of moving targets but also can extract their trajectories. The effectiveness of the proposed method is verified through simulations and experimental Gotcha data.

The conventional range-Doppler (RD) technique uses fast Fourier transformation (FFT) to generate focused images. However, the spectrum of FFT has high sidelobes and wide main lobes and the resolution of RD is limited by the radar parameters so that the RD method cannot generate super-resolution images especially with sparse aperture (SA) data. A super-resolution SA inverse synthetic aperture radar (SA-ISAR) imaging algorithm via the multiple signal classification (MUSIC) technique is proposed in this paper. The proposal uses the MUSIC algorithm to estimate the location and employs the least-squares technique to calculate the intensity of each scatterer. Then, the scatterers can be precisely depicted in the images without the interference of sidelobes and wide main lobes. The resolution of the proposal depends less on the radar parameters, and it can be further improved by using a smaller search step. Experiments obtained by processing of simulated and raw data demonstrate that the proposal can efficiently generate super-resolution images with full aperture (FA) or SA data.

A novel algorithm for 3-D interferometric inverse synthetic aperture radar (InISAR) imaging of ship target with complex motion via orthogonal double baseline is presented. For the ship target with a certain translational velocity and 3-D rotation, the distance between any scatterers on the target and the radar is analyzed in detail, and the keystone transform is used to reduce the impact of migration through resolution cell of ship target with big size. Then, the fractional Fourier transform is adopted to achieve the 2-D ISAR image of the target, and the mismatch of the ISAR images achieved by the three radars is solved by the image coregistration method according to the 1-D range profile. Finally, the 3-D InISAR image of the ship target is achieved with the interferometric operation with the three ISAR images. The effectiveness of the proposed method is proved by some simulation results, and the influence of different motion parameters on the 3-D imaging of ship target is analyzed simultaneously in this paper.

In this paper, a novel 3-D imaging method achieved by combining the inverse synthetic aperture radar (ISAR) technique and a multiple-input-multiple-output (MIMO) radar system is presented. The high-resolution image of a target can be obtained within a limited imaging time interval, and the computational load can be reduced simultaneously because the time samples obtained by the ISAR technique can be used to play the role of space samples needed in the MIMO radar system. The adopted process of time selection method can help to realize the exact combination of the signals received by different antenna elements without producing nonequivalence between the time samples and the space samples which need to be substituted. Besides, the 2-D smoothed (2-D SL0) method, which is especially designed for 2-D signals in the compressed sensing (CS) technique, is used to realize the high-resolution imaging when there are gaps in the global observation angle without increasing the computation load or introducing the false estimated data. Furthermore, the algorithms for range alignment and velocity estimation under the 3-D imaging radar configuration are described explicitly. Finally, the simulation results are provided to prove the effectiveness of the algorithms proposed in this paper.

In this letter, a novel method which employs the Radon transform (RT) and inverse Radon transform is proposed to estimate the parameters of the hybrid linear frequency modulation (LFM)-sinusoidal frequency modulation (SFM) signal. By adding a parameter called width, we extend the original RT to a three-parameter transform, which we named as extended Radon transform (ERT). Compared with the original RT, the ERT has shown more capacities, such as estimating the parameters of a strip in a plane. As the time–frequency representations (TFRs) of the hybrid LFM-SFM signal is a strip where there is a sinusoidal pattern embedded, the parameters of the signal are related to the strip; herein, we employ the ERT to estimate several parameters of the signal. With the estimated parameters values, the signal can be dechirped to be a SFM signal, whose parameters can be estimated by some existing methods. As it is based on the TFRs, the proposed method has strong robustness under low signal-to-noise ratios. Numerical results and analysis are provided to demonstrate the effectiveness of the proposed method.

This study presents a novel algorithm for radar imaging of high‐speed manoeuvering target. It is based on the fact that the azimuth part of the echo is modelled as the superposition of multiple quadratic frequency modulated (QFM) signals, and the range–Doppler algorithm is inappropriate to generate a focused radar image in this case. In this study, the improved version of product high‐order ambiguity function (IPHAF) is proposed to estimate the parameters of multiple QFM signals. This estimator requires only one‐dimensional (1D) maximisations without the scaling operation compared with the traditional product high‐order ambiguity function. The radar image quality can be improved by the IPHAF technique, and the experimental results validate the effectiveness of the method presented in this study.

For synthetic aperture radar (SAR) imaging with the high-frequency band, such as the terahertz band, the platform vibration influence cannot be neglected. In general, the vibration of the platform can be viewed as a simple harmonic motion, and thus, the received signal in each range bin can be considered as a sinusoid frequency modulation (SFM) signal for the terahertz SAR imaging. This will introduce the images to be defocused in the azimuth processing. In this letter, a novel algorithm for the analysis of the SFM signal based on the adaptive chirplet decomposition is proposed. This algorithm is accurate, efficient, and easy to be implemented. Then, the high-frequency vibration of the platform can be compensated by the corresponding signal decomposition results. The simulated results demonstrate the effectiveness of the novel algorithm proposed in this letter.

High resolution inverse synthetic aperture radar (ISAR) imaging of targets with complex motion is a main topic in the radar imaging domain. In fact, the traditional range‐Doppler algorithm is not appropriate to generate a focused ISAR images because of the time‐varying Doppler shifts caused by the target's complex motion. In this study, the azimuth received signal is modelled as multi‐component amplitude‐modulated and frequency‐modulated (AM–FM) signal, and a novel algorithm for the cubic Chirplet decomposition based on generalised cubic phase function is proposed to investigate the AM–FM signal analytically. Then, the corresponding ISAR imaging algorithm associated with the range‐instantaneous‐Doppler technique is proposed. Results of simulated and real data demonstrate the effectiveness of the presented algorithm.

High‐resolution inverse synthetic aperture radar (ISAR) imaging of ship target has attracted the attention of many scholars all around the world. For the complex motion of the ship, the Doppler frequency shifts of the scatterers are actually time‐varying, and the received signals in a range bin can be characterised as multi‐component amplitude‐modulated and frequency‐modulated (AM–FM) signals. In this study, the AM–FM signals are approximated as a weighted sum of multiple Chirplets, and a novel approach for high‐resolution ISAR imaging of ship target based on the product cubic phase function (PCPF) for Chirplet decomposition is proposed. The PCPF algorithm can reduce the cross‐terms between different Chirplet components, and combined with the range‐instantaneous‐Doppler technique, the high‐resolution instantaneous ISAR images can be obtained. Results of simulated and real data demonstrate the effectiveness of the method proposed.

A novel approach for inverse synthetic aperture radar (ISAR) imaging of three-dimensional (3-D) rotation target is presented in this study. It is based on the assumption that the manoeuverability is not too severe, and the received signal in a range bin can be characterised as multi-component linear frequency modulated (LFM) signal. The two-order match Fourier transform (TMFT) is used to estimate the parameters of the LFM signal, and the asymptotic statistical performance is analysed simultaneously. Combined with the Range-instantaneous-Doppler (RID) algorithm, the high-quality instantaneous ISAR images can be obtained. The results of simulated and real data demonstrate the effectiveness of the method proposed.

For inverse synthetic aperture radar (ISAR) imaging of a maneuvering target, the Doppler frequency shift for the received signal in a range bin is time varying. In this letter, the received signal is modeled as a multicomponent cubic phase signal, and a new ISAR imaging algorithm based on the product generalized cubic phase function (PGCPF) is proposed. The PGCPF algorithm can estimate the parameters of a multicomponent cubic phase signal, and combined with the range–instantaneous-Doppler technique, high-quality instantaneous ISAR images can be obtained. Results of real data demonstrate the effectiveness of the new method presented in this letter.

In inverse synthetic aperture radar (ISAR) imaging of a maneuvering target, the received signal in a range bin can be characterized as a multicomponent polynomial phase signal (PPS) after motion compensation. The traditional discrete Fourier transform algorithm is not appropriate in analyzing the PPS. In this paper, the L-class of a fourth-order complex-lag polynomial Wigner–Ville distribution (PWVD) is presented to generate a high-resolution time–frequency distribution (TFD) for the multicomponent PPS. For a signal with polynomial phase up to order four (this is accurate enough in ISAR imaging), the cross-terms between different components can be reduced by the convolution in the frequency domain of the L-class of the fourth-order complex-lag PWVD. The new TFD is used in the ISAR imaging of the maneuvering target, and high-quality instantaneous ISAR images are obtained. Results of simulated and real data demonstrate the effectiveness of the method above.

Inverse synthetic aperture radar (ISAR) imaging of a ship target is very important compared with the plane target, and the imaging condition of the ship target is more complicated than that of the plane target due to the complexity of the ship's movement. In this letter, the received signal of a ship target is modeled as a multicomponent cubic phase signal, and the product high-order matched-phase transform is proposed to estimate the parameters of each component. Then, the instantaneous ISAR images can be obtained. Results of real data demonstrate the effectiveness of the new method proposed.

A novel algorithm for estimating the rotation angle in inverse synthetic aperture radar imaging by generally considering the received signals as cubic phase signals is presented. The rotation angle is determined by the coefficients of the cubic phase signals, and the results of real data demonstrate the validity of the algorithm.