• In response to the azimuth defocusing issues caused by target motion, a sparse recovery model capable of characterizing target phase errors is established, and an autofocusing module based on the maximum contrast criterion is proposed, achieving the estimation of azimuth phase errors. • Considering the low-rank characteristics of the THz-SAR moving target range profile matrix, the dispersed nature of the background range profile, and the sparsity of the moving target image, the concept of Robust Principal Component Analysis (RPCA) is introduced. By integrating the aforementioned autofocusing module, a THz-SAR moving target imaging algorithm based on low-rank and sparsity joint constraints, AF-RPCA-ADMM, is proposed, achieving the separation of moving targets from the background and focused imaging. • To accelerate the imaging algorithm, an efficient HR imaging network for moving targets, AF-RPCA-Net, based on a deep unfolding network, is designed. The network automatically learns the optimal hyperparameters required for each iteration process of the sparse recovery algorithm, resulting in well-focused THz-SAR moving target images efficiently. Terahertz-Synthetic Aperture Radar (THz-SAR) offers high frame rates and high resolution, making it particularly suitable for remote sensing applications, like dynamic monitoring of moving targets. However, due to the non-ideal motion of the airborne platform and the non-cooperative motion of targets, this phenomenon causes more severe defocusing compared with microwave band SAR. Traditional SAR imaging methods, if directly applied to image THz-SAR moving targets, often suffer from poor quality and low efficiency. To address this issue, this article proposes a moving target non-parametric learning imaging method based on the Deep Unfolding Network (DUN) framework. Firstly, an autofocusing module is derived based on the maximum imaging contrast and embedded within the Alternating Direction Method of Multipliers (ADMM) iterative solution process to achieve accurate compensation of azimuthal motion errors. Then, we introduce the concept of Robust Principal Component Analysis (RPCA) to achieve sparse recovery imaging of moving targets. Finally, based on the ADMM iterative solution process, we establish an imaging network, named AF-RPCA-Net, efficiently achieving model-data jointly driven moving target background separation and imaging. The proposed method is validated to be effective and efficient through experimental results derived from both simulated and measured data.

Detecting weak motion targets in complex environment by using radar sensors is of great importance for Internet of Things (IoT) applications. However, the complex environment with strong clutter and low-signal-to-clutter ratio (SCR) usually poses formidable challenges for achieving effective detection. To deal with this problem, different from the conventional energy-based method, this article proposes a novel detection technique based on the theory of information geometry (IG), which captures the nonlinear correlation feature (CF) of observed signals to effectively distinguish the target from clutter. Specifically, inherit the advantages of IG, we formulate a CF manifold and the geometric distances are derived to measure the dissimilarity of target and clutter. Then, a CF-based IG (CF-IG) detector is proposed. Moreover, we consider a multiframe detection strategy to further enhance the detection performance. A multiframe track-before-detect (TBD) method on the CF manifold is performed. Experimental results conducted on IPIX radar data and real measured drone target data validate the effectiveness and superiority of the proposed method.

Airborne terahertz (THz) synthetic aperture radar (SAR) exhibits unique potential for ground moving target imaging (GMTIm), owing to its high frame rate and high-resolution capabilities. However, the short wavelength of THz waves significantly increases Doppler sensitivity. When a ground moving target performs curvilinear motion such as turns, velocity inconsistencies among scattering points induce variations in Doppler centroid frequencies and chirp rates, leading to defocusing and geometric deformation. To address these issues, an effective curvilinear moving target refocusing method is proposed in this letter. Firstly, a local phase gradient autofocus (LPGA) method is employed to compensate for Doppler chirp rate inconsistencies. Secondly, the additional spatial domain information from a dual-channel system is utilized to correct geometric deformation. Finally, both simulated and measured data are analyzed to validate the effectiveness of the proposed method.

In this letter, a novel radar forward-looking imaging technique based on beam pattern modulation and beam scanning is presented. First, the chirp beam, which presents quadratic varying phases within the main lobe, is generated and scans as a chirp pulse propagating along the azimuth direction by differentially exciting each element of a uniform linear array (ULA). Second, the target distribution is successfully reconstructed using 2-D pulse compression, and a theoretical analysis of the azimuth resolution is conducted. Finally, the sparse representation (SR) technique is employed to enhance the imaging performance. Simulation and experimental results validate the effectiveness and potential of the proposed method for acquiring high-resolution forward-looking images. This work holds promise for advancing the development of radar forward-looking methods and systems.

Terahertz video synthetic aperture radar (THz-ViSAR) is emerging as a key technology for dynamic situational awareness and precise imaging of moving targets. However, the existing systems prioritize high frame rates at the expense of the dwell time, which degrades the azimuth resolution. To address this issue, we propose an improved video synthetic aperture radar (ViSAR) framework that can extend the synthetic aperture time and improve the image quality. First, the keystone transform (KT) is utilized to correct range cell migration, enabling accurate phase error estimation for high-resolution imaging. Moreover, unlike traditional methods without full-aperture imaging, our technique synthesizes a full-aperture phase error model by estimating subaperture phase errors, which can contribute to compensate for overall motion errors and generate high-resolution full-aperture SAR images. Airborne field experiments demonstrate the effectiveness of the proposed method, achieving the full-aperture high-resolution imaging while maintaining practical frame rates in dynamic surveillance scenarios.

In recent years, the application of passive millimeter-wave imaging (PMWI) in target detection, reconnaissance, and surveillance has drawn our attention again, especially passive interferometric millimeter-wave imaging (PIMI). However, PIMI suffers with high hardware and signal processing complexity, and passive interferometric millimeter-wave sensors (PIMSs) are very expensive. In this letter, a W/D-band millimeter-wave radiometer on unmanned aerial vehicles (UAVs) is first developed for target detection, reconnaissance, and surveillance, especially for ship detection. The W/D-band millimeter-wave radiometer has the advantages of low hardware and signal processing complexity and low cost. The W/D-band millimeter-wave radiometer is introduced, and a proof prototype of the W/D-band millimeter-wave radiometer is manufactured. Numerical simulations are performed to analyze the brightness temperature (TB) characteristic of the metallic ships. Outdoor experiments are performed to assess the feasibility of the metallic target detected by the W/D-band millimeter-wave radiometer on a UAV. Initial experimental results have demonstrated that metallic targets can be detected by the W/D-band millimeter-wave radiometer from UAVs, as expected.

Sequential ground moving target imaging (GMTIm) is an imperative and challenging task under terahertz video synthetic aperture radar (THz-ViSAR), which contributes to fine-grained situational awareness and moving target (MT) recognition. However, traditional GMTIm methods are usually designed for single-frame images, which involves repetitive parameter estimation for the sequential imaging problem and lacks the efficiency due to the parameter sensitivity. To tackle the aforementioned problems, this paper proposes a sequential GMTIm method based on hybrid THz-ViSAR-inverse SAR (ISAR) image formation. With respect to ViSAR processing, the sequential imaging results are firstly obtained. Considering the similarity of scene among inter-frame images, MTs can be detected based on target-level change detection after image registration and shadows left on the road. Following this, the defocused target region is transformed to obtain raw echoes, which is beneficial for parallel processing and reduce the computation amount. As for ISAR processing, the envelope alignment and auto-focus methods are employed to eliminate the residual motion errors and compensate for phase errors without constructing prior motion patterns. Thereafter, the ratio of equivalent rotational velocities between MTs and the scene is estimated to achieve the azimuth scaling. Finally, sparsity-based imaging enhancement is employed to further enhance the imaging quality. Simulations and airborne experiments are carried out to validate the effectiveness of the proposed method.

Ground moving target imaging (GMTIm) presents both challenges and opportunities for enhancing the fine-grained situational awareness and target recognition. However, the imaging quality of non-cooperative moving targets (MTs) fundamentally depends on their motion characteristics, which often fluctuate unpredictably during long dwell times in microwave bands. To address these limitations, this paper proposes a robust GMTIm scheme based on terahertz video synthetic aperture radar (THz-ViSAR). Rather than operating on complete echo data directly, the proposed method achieves GMTIm through the defocused region of interest (RoI) data inversion, which can reduce the computation burden. Benefiting from the short dwell time of THz-ViSAR, motion patterns of MTs can be simplified as the uniform motion in most cases. In response to range walk (RW) manifested by radial motion of MTs, the RW correction is employed based on the inclination of range profiles. Thus, considering the sparsity of MTs, motion errors caused by azimuth velocities from MTs, can be compensated by the minimum image entropy-based alternating direction method of multipliers (ADMM) autofocus framework, which can deal with the unknown phase errors and improve the imaging quality adaptively. Finally, the azimuth scaling is established by the azimuth spreading coefficient table, which is constructed based on the target velocity, rotating radius and center angle. Simulations and airborne field experiments are carried out to validate the effectiveness of the proposed method.

Coded-aperture imaging (CAI) is a promising radar imaging technology, which has the advantages of high resolution, forward-looking, and staring imaging. However, it still faces some challenges. In the actual imaging process, there are many kinds of errors, such as modulation error of the coded antenna, off-grid error, and imaging distance error, and these errors will affect the accuracy of the radiation field and the derivation of the imaging model. When the actual imaging model is inconsistent with the derived imaging model, the imaging result may deteriorate seriously or even fail to be imaged. In addition, the imaging efficiency needs to be improved urgently. Therefore, we propose a data-driven method based on equivalent measurement of radiation fields to tackle these problems. First, a new radiation field modeling method is proposed, which takes into account the accuracy and complexity of radiation field modeling. It can greatly improve the reconstruction quality of sparse and extended targets under system errors, and it also has the advantage of low workload and easy implementation. Then, an artificial neural network algorithm based on equivalent measurement of radiation field (ANN-EMRF) is designed to improve imaging efficiency. Simulation and laboratory experiments are carried out to demonstrate the feasibility of the proposed method under various system errors.

Array-based millimeter wave (MMW) holographic imaging technology has shown great application potential in nondestructive evaluation (NDE) of dielectric materials (such as composites). With the development of high-precision motion capture systems, the NDE of the internal structure of layered dielectric targets by handheld radar has become a new demand growth point. Since the elements of the handheld-radar-based array are randomly distributed in the 3-D space, the existing fast-imaging algorithms are difficult to be directly applied, which will restrict the development of this application. For this reason, this article proposes a fast-imaging algorithm for layered dielectric targets based on spatial 3-D nonuniform single-input single-output (SISO) array radar. The algorithm takes each element of the spatial 3-D random array as the center, expands it into a virtual uniform plane array, then transforms the echo data of the virtual array from the spatial domain to the wavenumber domain by using fast Fourier transform (FFT). After phase compensation, the wavenumber-domain data of all virtual arrays are added, to obtain the wavenumber-domain scattered echo data of the entire 3-D nonuniform SISO array radar. Finally, the imaging result of the target is obtained through the 2-D inverse Fourier transform (IFT) in azimuth direction and the segmented IFT in range direction. Numerical simulation and experimental measurements show that the proposed algorithm can significantly improve the imaging efficiency compared with the improved back-projection (IBP) algorithm which is also suitable for this scenario.

Airborne terahertz video synthetic aperture radar (THz-ViSAR) exhibits excellent superiorities in the dynamic situation awareness and moving targets detection. However, limited by the recording precision of the inertial navigation system (INS) or global positioning system (GPS), data-driven motion compensation (MOCO) methods are challenging to eliminate motion errors for THz band. Particularly, platform vibrations that are previously negligible in the microwave band, become non-negligible, drastically degrading the focusing performance. To conquer aforementioned problems, this paper proposes an echo-driven robust MOCO framework suited for THz-ViSAR. First, the airborne motion error model (MED) is re-derived and corresponding effects are analyzed in detail. Based on the derived MED, two parts are required to estimate from the echo: radial errors and vibration errors. Thus, a forward estimation of candidate signal of interests (SoIs) is proposed based on local peaks of the average range profile and the prominence. For each candidate, radial and vibration errors are estimated from the unwrapped phase of the SoI via the polynomial and multi-component sinusoidal fitting in turn. Finally, MOCO functions are constructed and evaluated based on the image entropy and image contrast, which determines the optimal SoI. Airborne field experiments are carried out to validate the effectiveness and practicability of the proposed method.

Rotating targets/components often carry beneficial information for identifying high-value targets in video synthetic aperture radar (ViSAR). However, unlike linear motion models, rotating motion models involve more parameters to be estimated, rendering parametric-based methods computationally intensive. Besides, such micro-motion features are relatively insignificant in the microwave band. To conquer the aforementioned problems, this article proposes the nonparametric indication and imaging of rotating targets under terahertz (THz) ViSAR. Taking rotating corner reflectors (RCRs) as the object, the Doppler prior and imaging characteristics (DPIC) are first determined and regarded as the theoretical basis. Combining DPIC and Doppler sensitivity of THz band, RCRs can be easily indicated by the nonparametric time–frequency transform. Thus, range cells of RCRs can be deduced intuitively by the visual saliency, followed by the separation of the residual background and defocused target area. After clutter suppression, two signal of interests (SoIs) of RCRs are extracted from salient points, where unwrapped phases and azimuth spectrums are utilized for fast estimation of phase errors and additional Doppler centroids (DCs), respectively. Finally, the refocused target image is stitched into the residual background. To validate the effectiveness of the proposed method, the airborne field experiment of RCRs is conducted in THz-band for the first time. Moreover, the anti-noise ability, relocation capability, and applicable speed interval of the proposed method are demonstrated, along with its extended application potential in terms of the ground moving target imaging.

The high-resolution inverse synthetic aperture radar (ISAR) imaging of space target holds tremendous significance for recognition and on-orbit maintenance. The terahertz (THz) radar possesses natural superiority in terms of large bandwidth and short wavelength, enabling it to achieve higher resolution both in range and azimuth directions. However, the range cell migration (RCM) induced by rotation is severe in the THz band, even in small rotation angle. In order to achieve high-accuracy RCM correction, an imaging algorithm based on keystone transform (KT) and fast Gaussian gridding nonuniform fast Fourier transform (FGG NUFFT) is presented in this article. Nevertheless, to correct the second-order RCM and compensate the spatial-variant (SV) phase error, the rotation parameters of uncooperative target need to be estimated first. Moreover, by analyzing the ISAR images of space targets, it is found that the scattering intensity of certain components on the satellite body, such as the cavity structure of the antenna, is stronger than that of the solar panels. Considering scattering intensity of these components, the traditional estimation method under global image quality criteria may fail. To prevent getting trapped in a local optimum or finding an incorrect solution due to the influence of strong scattering points, a parameter estimation framework based on prior knowledge added sparrow search algorithm (PKSSA) under local maximum contrast criterion (LMCC) is brought forward. The proposed novel algorithm can not only fast estimate rotation parameters without being affected by strong scattering points but also obtain fine imaging results. Numerous electromagnetic simulation and real measured THz experiment results prove the correctness and effectiveness of the proposed algorithm.

As demand for precise detection of space targets and orbital maintenance increases, it is crucial to obtain high-resolution images of space targets. The terahertz (THz) radar with carrier frequency of 0.1-10 THz has the characteristics of larger bandwidth and shorter wavelength compared with microwave radar. Accordingly, under the same inverse synthetic aperture radar (ISAR) imaging conditions, it has advantages in both range resolution and azimuth resolution theoretically. However, for large-size space targets, the phenomenon of high-order range cell migration (RCM) and azimuth phase error are prone to occur during small-rotation-angle (3-5°) ISAR imaging, which results in severe imagery defocusing. In this article, a high-resolution ISAR imaging method based on defocused point spread function (PSF) is proposed. By means of third-order Taylor expansion performing on the Range-Doppler (RD) imaging model, the impact of high-order terms on imaging is analyzed. Through revealing the relationship between high-order terms and inclination of defocused PSF measured with Hough Transform (HT), the rotation velocity and range center deviation (RCD) are estimated accurately and independently in image domain. After parameters estimation, the first-order RCM is corrected by Keystone Transform (KT). Utilizing the estimated rotation velocity, the second-order RCM is eliminated by Fast Gaussian Gridding Nonuniform Fast Fourier Transform (FGG NUFFT). Then the spatial-variant phase error is compensated based on previous estimation. Lastly, azimuth resolution is improved by FGG NUFFT due to non-uniformly sampled data. Numerous simulations and real measured experimental results demonstrate the effectiveness and outperformance of proposed algorithm for large-size space target ISAR imaging in the THz band.

In this paper, the power spectrum information geometry (PSIG) detector, which inherits the performance advantages of matrix information geometry (MIG) detectors in heterogeneous clutter backgrounds, is proposed. The PSIG detector can address two urgent problems in applications of MIG detectors, which are the expensive computation expense and unavailable acquisition ability of target velocity. Specifically, the PSIG detector utilizes power spectrums instead of high dimensional covariance matrices to characterize sample data and employs subband filter bank to extend the detection from range cells to range-Doppler cells, thus it requires less computation expense and can obtain the target velocity information according to the Doppler cell. Experiments based on the real data show the advantages of the proposed PSIG detectors in comparison with competitive methods. Especially, in the experiments with the real-recorded airborne radar data, the proposed method can effectively suppress the heterogeneous main-lobe clutter without any prior knowledge and provides detection probability improvement of more than 30% to the competitive methods with low false-alarm ratios.

Terahertz (THz) inverse synthetic aperture radar (ISAR) imaging of space targets has the advantage of high resolution and all-day capability for spatial situational awareness (SSA). However, compared with microwave ISAR imaging, microvibrations of the radar platform and target can cause image defocusing and quality degradation in the THz band. Commonly, the existing algorithms considered vibration as simple harmonic motion in a rigid body and estimated the vibration parameters for phase error compensation. Nevertheless, these methods are dependent on the prominent points and cannot focus on the complex nonrigid vibration of the space target. Aiming at these problems, an ISAR imaging framework that can be leveraged for complex nonrigid vibration compensation is proposed. First, a compressed sensing (CS) model is established to compensate for the vibration phase error in ISAR imaging. Then, the alternating direction method of multipliers (ADMMs) is employed to optimize the CS model. Subsequently, considering that strong scattering points of space targets will cover up the weak scattering information in ISAR images, a sidelobe suppression constraint is appended. Thus, the weak scattering information is retained while removing the sidelobe and noise. It is worth mentioning that the ISAR imaging algorithm is derived to a 2-D matrix form to reduce memory usage and improve computational efficiency. Finally, simulated and measured data are utilized to verify the superiority of the proposed algorithm and prove the potential of ISAR imaging for practical space targets.

Radar 3-D forward-looking imaging has always been a difficult issue in the radar detection. In this letter, a forward-looking 3-D imaging method based on attribute scattering model (ASM) for extended targets is proposed. First, an imaging model based on point scattering model (PSM) with wavefront modulation technique is constructed to achieve 3-D forward-looking imaging. Second, considering the fact that PSM-based imaging model assumes that the target is composed of a set of discrete points, it is not suitable for reconstructing the structure feature of extended targets, i.e., line structure and surface structure. To extract more geometry information of the target, the ASM that includes point scatterers (PSs), line-segment scatterers (LSSs), and rectangular-plate scatterers (RPSs) is adapted to the 3-D imaging model. Solving the parameter sets of PSs, LSSs, and RPSs with the alternating direction method of multipliers (ADMMs) algorithm, the edge and surface structure of the extended target can be reconstructed. The simulation results based on electromagnetic (EM) calculation by FEKO verify the effectiveness of the proposed method.

In this letter, an effective parametric IF estimation method based on piece-wise sparse representation (PWSR) is proposed to enhance the precision of conventional time-frequency distribution (TFD)-based estimators. Firstly, the signal is divided into a series of short-time segments according to the piece-wise quadratic frequency modulation (QFM) model. Then, the corresponding QFM parameters of each segment are accurately estimated by solving a sparse representation (SR) problem. Moreover, a construction scheme enabling the QFM dictionary to vary adaptively with the analyzed short-time signal is also provided. Finally, the IF of each segment is reconstructed according to the SR solution individually and combined together to generate the complete IF estimates. A radar imaging example verifies that the proposed method achieves a notable improvement in estimation accuracy as compared to existing TFD-based approaches.

Video synthetic aperture radar (ViSAR) operating at low-frequencies generally suffers from low frame-rate and high latency due to its wide synthetic aperture and long dwell time. To conquer the problems mentioned above, this article proposes a ViSAR imaging processing framework based on the terahertz (THz) regime. The proposed method only needs a data repetition ratio of 0% rather than 80% for the microwave to reach the 0.2m imaging resolution and 5Hz frame rate with low latency thanks to the designed high pulse repetition frequency (PRF) system and short wavelength of THz wave. Thus, robust estimations of baseband Doppler centroid and Doppler rate are proposed based on the multi-core adaptive weighting and imaging knowledge prior, which handles the Doppler sensitivity of the THz regime effectively. Finally, airborne experiments are carried out to validate the superiority of dynamic situational awareness and the potential of moving targets indication in the THz-ViSAR.

This article deals with the problem of detecting maritime targets embedded in nonhomogeneous sea clutter, where the limited number of secondary data is available due to the heterogeneity of sea clutter. A class of linear discriminant analysis (LDA)-based matrix information geometry (MIG) detectors is proposed in the supervised scenario. As customary, Hermitian positive-definite (HPD) matrices are used to model the observational sample data, and the clutter covariance matrix of the received dataset is estimated as the geometric mean of the secondary HPD matrices. Given a set of training HPD matrices with class labels, which are elements of a higher dimensional HPD matrix manifold, the LDA manifold projection learns a mapping from the higher dimensional HPD matrix manifold to a lower dimensional one subject to maximum discrimination. In this study, the LDA manifold projection, with the cost function maximizing between-class distance while minimizing within-class distance, is formulated as an optimization problem in the Stiefel manifold. Four robust LDA-MIG detectors corresponding to different geometric measures are proposed. Numerical results based on both simulated radar clutter with interferences and real IPIX radar data show the advantage of the proposed LDA-MIG detectors against their counterparts without using LDA and the state-of-the-art maritime target detection methods in nonhomogeneous sea clutter.

Airborne distributed coherent aperture radar (ADCAR) coheres several flexible unit radars to form an equivalent large aperture radar for enhancing the signal gain, spatial resolution and system reliability. However, the ultra-long interelement spacing brings severe grating lobes, which will considerably deteriorate the performance of clutter suppression and target detection. To conquer this issue, we propose a stepped frequency signal based space-time adaptive processing (STAP) approach for clutter and grating lobes suppression. The increased transmitting signal frequency can expand the space frequency and Doppler frequency of both target echo and clutter echo. Therefore, the target and clutter can be distinguished through different expanded frequency ranges, and in turn the grating lobes can be suppressed. To further reduce the peak sidelobe ratio of target response, accumulated pattern synthesis approach is applied in both transmit and receive end. Furthermore, to guide practical implementation, the influence of the non-ideal factors including gain errors, phase errors, platform control errors and grid mismatch are analyzed to provide a feasible boundary for the algorithm. Simulations corroborate the superiorities of the proposed STAP approach for ADCAR.

This paper addresses the problem of detecting radar targets submerged into strong sea clutter background. In this study, a novel type of detection method based on matrix information geometry (MIG) is developed. Filtering process and manifold projection are incorporated into detector design. Firstly, a filtering scheme for correlation coefficients is established via subband decomposition, such that a subband Hermitian positive definite (HPD) manifold constructed by a set of subband HPD matrices is formulated. Accordingly, the detection is performed as discriminating the target and the clutter on the subband HPD manifold. Then, in order to enhance the discriminative power between the target and the strong clutter, a manifold projection method that maps the HPD manifold into a lower-dimensional and more discriminative one is devised. In this study, the manifold projection is formulated as an optimization problem on a Stiefel manifold according to the principle of maximizing signal-to-clutter ratio (SCR). Subsequently, a manifold projection based subband MIG detector is proposed. Extensive experiments based on simulated data and real radar data are carried out to verify the effectiveness of the proposed method. The experimental results demonstrate that the proposed method can efficiently suppress the strong sea clutter and achieve better detection performance than the competitors.

The restricted physical antenna aperture usually degrades the acquisition of target images with high azimuth resolution in radar forward-looking sight. To make breakthrough on this problem, this article offers a forward-looking radar imaging methodology by the illumination beam design and virtual aperture processing, which can improve the azimuth resolution. First, the imaging model is established with respect to the observation scenario, and the echo characteristics of the forward-looking virtual array are analyzed. Second, compared with the back-projection (BP)-based algorithm, the polar format imaging (PFI) algorithm with higher efficiency is proposed to obtain the focused images of target in the forward-looking locations. Finally, comprehensive simulations and real measured experiments are performed to corroborate the effectiveness of the proposals and show the imaging performance with different influencing factors.

In this letter, a novel microwave coincidence imaging (MCI) approach is proposed based on a multiple-input single-output (MISO) radar system to deal with the low signal-to-noise ratio (SNR) scenarios and off-grid problem. First, in coherent-detecting part, a linear frequency modulated (LFM) signal is transmitted, and dechirping processes are conducted to enhance the SNR of echoes. Then, in incoherent-modulating part, the post random phase-shifting modulations are conducted on the echoes, hence the temporal–spatial orthogonal reference radiation field of MCI is constructed, which provides the potential information of super-resolution. Further, to solve the off-grid problem of target’s scatterers in MCI, a new projecting-residual-based selection criterion is also proposed, combined with the preexisting signal subspace matching (SSM) method. The proposed method could largely eliminate the off-grid errors while conduct a reference matrix selection procedure, hence the reconstruction accuracy and computational complexity can be much improved and reduced, respectively. Finally, the validity of the proposed method and the super-resolution ability of MCI are verified by experiments.

Nonstationary moving target detection in spiky sea clutter is a challenging task due to the high-power, time-varying, and target-like properties of sea spikes. In this letter, we propose a time-frequency correlation (TFC)-based constant false alarm rate (TFC-CFAR) detection method on the time-frequency manifold, and apply it to the nonstationary moving target detection in spiky sea clutter. The data samples in each range cell are modeled as a TFC matrix that captures the correlation between two frequency components of the time-frequency distribution. The clutter covariance matrix is estimated by the geometric mean of a set of TFC matrices in reference cells. Three geometric metrics are employed to measure the dissimilarity between the clutter and target signals. Based on these geometric measures, three TFC-CFAR detectors are compared. Experiments performed on a real IPIX radar dataset confirm that the TFC can be used for identifying and eliminating sea spikes, while the TFC-CFAR detector achieves better detection performance than the conventional detectors.

Moving targets in a heterogeneous environment can be extracted through traditional robust principal component analysis (RPCA). Competitive RPCA algorithms address a nonconvex constraint by relaxing it to a fixed rank. However, the solution does not necessarily reach a global optimum. To address this problem, a moving target detection method by RPCA in the topological space of low-rank matrices is proposed to obtain superior target detection performance. First, RPCA is considered in the topological space of low-rank matrices, which is the closure of the fixed-rank manifold. Then, combined with manifold optimization and the proximal gradient, the RPCA-PGTSLr algorithm is applied to solve the problem caused by a non-differentiable sparsity term, so that the target can be precisely extracted. Experiments performed on measured data demonstrate that the proposed method exhibits advantages in detection performance over competitive methods in a heterogeneous environment.

Terahertz coded aperture imaging (TCAI) is a promising radar imaging technology that leverages coded aperture antenna to achieve forward-looking imaging without relying on relative motion. For solving the target scattering coefficient, one typical approach is to impose generic sparsity prior constraint in the image domain. Although the method has made significant progress in TCAI, it is still challenging to achieve the high-resolution reconstruction of targets with different sparseness under compression measurements, and it is also limited by poor noise resistance performance. To tackle these problems, we propose a new imaging scheme using deep prior captured by the generator. In this letter, we first analyze and model the system based on a coherent detection array and propose a deep prior-based model for TCAI by modeling the target as being in the range of the generator. Then, the deep alternate minimization (Deep-AM) algorithm is designed to solve the model by projecting between the target space and the latent variable space in an alternating fashion. Finally, the simulation results demonstrate the robustness and effectiveness of the proposed approach under the compression measurements. Also, the acquisition and coding times of the system are greatly reduced by utilizing array receiving and deep prior information.

From the perspective of feature extraction and classification, deep neural networks are widely used in radar target detection. However, in heterogeneous environment, the traditional deep neural networks are difficult to extract a robust feature, which leads to the degradation of network detection performance. In order to address this problem, a radar target detection method with multi-task learning in heterogeneous environment is proposed. Considering the influence of heterogeneous data distribution, the proposed method designs a contrastive learning module added to a multi-task autoencoder. It can learn a compact and distinguishable feature representation, which enhances the feature separability between the clutter and the target. Simultaneously, a classifier is introduced to realize a binary detection in the feature representation. Comprehensive experiments are carried out to show that the proposed method guarantees a good detection performance in heterogeneous environment and solves the issue of over-fitting to a certain extent. Compared with some classical detectors, the proposed method shows better performance.

Radar forward-looking imaging has always been a difficult problem in the radar detection. Modulating the wavefront of radar transmitted signals provides a feasible method for radar forward-looking imaging. The existing high-resolution radar imaging algorithms assume that the target scattering coefficients are sparsely distributed. However, for complex targets, the scattering coefficients no longer satisfy the sparse prior. To solve the problems of forward-looking imaging for complex targets, in this letter, a sparse representation imaging method with dictionary learning is proposed. First, the principle and imaging model of microwave modulation are introduced to achieve radar forward-looking imaging. Second, the dictionary learning method is developed to learn adaptive transformation that exploit the edge features and structural information as well as provide a sparser presentation to further improve the quality of radar images. Third, the imaging performance for different types of complex targets under different signal-to-noise ratios are analyzed. The simulation results show that the proposed method can effectively reconstruct different types of complex targets.

Different from conventional radar systems, for the single-photon radar, a single-photon detector acts as the receiver to detect the echo signal with only a few photons, which can be used to achieve ultrahigh sensitivity for long-distance target detection. In this letter, the heterodyne single-photon radar at terahertz frequencies is studied, for the first time. First, the working principle of the developed heterodyne single-photon radar is presented, and the target echo signal is derived. Subsequently, the estimation method of the target distance and the Doppler frequency is proposed, and the performance is analyzed by probability density function (PDF) derivation. Simulation results show good agreement with the theoretical analysis. This work can pave the way to the development of a new radar paradigm.

Ship target detection in synthetic aperture radar (SAR) imagery is of great significance in the field of ocean monitoring. Classical constant false alarm rate (CFAR) detectors and emerging information geometry methods are model-driven essentially, requiring precise modeling of the sea clutter distribution. In the complex and changeable ocean scenes, the performance of these two types of detectors is limited. To solve this problem, a ship target detection algorithm in SAR imagery based on the maximum eigenvalue of the sample covariance matrix is proposed in this letter. Without seeking the distribution model of clutter backgrounds, the difference between the target and the clutter background is fully captured by constructing the sample covariance matrix, and its maximum eigenvalue is utilized as the test statistic. Experimental results on measured SAR images show that the proposed method achieves better detection performance and faster calculation speed compared with the existing typical methods.

Accurate time-frequency (TF) feature extraction of moving target is a challenging task due to the poor resolution and serious cross-terms of conventional TF analysis (TFA) methods. In this letter, an effective TFA algorithm based on the adaptive short-time sparse representation (ASTSR) is proposed to enhance the TF feature of moving target. Firstly, the limitation of the Fourier transform-based short-time TFA is revealed from the motion approximation perspective. Then, in order to achieve accurate motion approximation, the width of the analysis window is determined adaptively by minimizing the bandwidth of each short-time signal individually. Finally, the TF representation (TFR) with high energy concentration is obtained by utilizing the sparsity of these signal segments in the chirp dictionary. Comparisons indicate that the ASTSR provides high-resolution TFRs without producing interference terms at an acceptable computational cost while performing well in weak component expressing and signal denoising. Furthermore, a ISAR imaging example confirms the potential of the proposed method.

In this letter, a novel microwave coincidence imaging (MCI) method based on the attributed scattering model (ASM) is proposed. Unlike the classical MCI model which assumes the target as a set of discrete scatterers, the ASM-based MCI equation contains three kinds of reference matrices corresponding to the point-scatterers (PSs), the line-segment-scatterers (LSSs) and the rectangular-plate-scatterers (RPSs), respectively. Hence the ASM-based MCI could resolve richer information about the object geometries. By solving the imaging equation via the alternating direction method of multipliers (ADMM) algorithm, the scattering coefficients will be obtained and the target can be reconstructed according to the presetting parameter sets. Meantime, the ASM-based MCI also earns the superresolution ability like the classical MCI, which is brought in by the temporal-spatial orthogonal radiation field. Simulations and experiment are carried out to demonstrate the performance and superresolution ability of proposed method. The ASM-based MCI makes contributions to the progress of radar forward-looking imaging theory and technology.

Microwave coincidence imaging (MCI) is a novel staring imaging technique with high resolution in azimuth. In MCI, the continuous imaging area is discretized into fine grids and the target-scattering centers are assumed to be exactly located at the centers of prediscretized grids. Recently, parametric methods are applied to MCI as target reconstruction algorithms with resolution enhancement and quality improvement. However, in practical applications, grid mismatch will severely degrade the imaging quality of parametric methods because the target-scattering centers will not totally locate at the grid centers no matter how fine the grids are. In this article, a reweighted-dynamic-grid-based MCI (RDG-MCI) method is proposed. In RDG-MCI, grids are evolving from coarse to dense iteratively rather than being fixed, and hence, off-grid errors can be eliminated gradually. Meanwhile, the reconstructed coefficients are used as weighting factors of grids in a form of weighting matrix in the next iteration and nonkey grids will be dropped out. Hence, the dynamic grids will be focused around the positions where target scatterers are most likely to exist. Furthermore, the matrix uncertain sparse Bayesian learning (MUSBL) algorithm is adopted to eliminate the residual off-grid errors. Finally, a preferable imaging result can be obtained based on the updated nonuniform grids. Also, the theoretical expected Cramér–Rao bound (ECRB) is also derived to evaluate the performance of the proposed method. The effectiveness of the proposed method, along with the super-resolution ability of MCI, is verified by simulations and outdoor experiments.

Due to a serious shortage of training data, the performance of adaptive clutter suppression suffers remarkable degradation in heterogeneous environments. To address this problem, a novel clutter suppression method via affine transformation on manifolds is proposed. First, training samples in heterogeneous environments are characterized on an established manifold in which the distribution properties are analyzed. Then, a clutter classification scheme is proposed, whereby the KL divergence decision rule is derived to identify the training data as either homogenous or heterogeneous samples. Afterward, based on the distribution properties of samples and the clutter classification scheme, an affine transformation on manifolds is proposed for sample augmentation by transporting heterogeneous samples into the region of homogeneous samples. Finally, the clutter in the area of interest is suppressed on the manifold, which combines the transformed samples with the homogeneous samples, such that superior performance is obtained. Experiments on both simulated and real data validate the superiority of the proposed method in highly heterogeneous environments.

Terahertz (THz) regime has shown superior performance in terms of reflecting the details of target structures. However, the extended structures (ESs) may be discretized into endpoints in synthetic aperture radar (SAR) images if the observation aperture deviates from the specular orientation of ESs, which deteriorates subsequent image interpretation and intelligent imaging. Taking the tank barrels as the object, this paper proposes a novel solution to detect and reconstruct the critical ES by effectively exploiting the component prior and imaging characteristics (CPIC). The core of the proposed method lies in converting the phase matching of the multi-views methods into object detection based on CPIC and deep neural network. Firstly, the CPIC of tank barrels is analytically determined and regarded as the theoretical basis of datasets annotation. Thus, the modified multi-scale object detection network is constructed to enhance the detection performance and estimate the orientation of barrels. Finally, comprehensive simulation, anechoic chamber and field experiments are carried out to validate the effectiveness of the proposed methods. The results show that the proposed method can outperform the existing object detection networks in terms of the detection accuracy of multi-scale objects, and outperform the existing multi-view methods in terms of time need with comparable accuracy.

The electromagnetic (EM) vortex imaging has demonstrated superior performance in target detection and imaging with azimuthal super-resolution. However, the restricted elevation resolution degrades the acquisition of target spatial information, which limits the development of the radar imaging technology based on orbital angular momentum (OAM). This article offers a solution to achieve the 3-D EM vortex imaging, by effectively utilizing relative motion between radar and target in the line-of-sight (LOS) direction. First, the forward-looking radar imaging scenario is presented, the 3-D echo model is derived, and the characteristics are analyzed as well. Second, the imaging method, based on the back-projection (BP) and spectrum estimation method, is proposed to obtain the target’s 3-D focused image. Furthermore, the influence factors about the elevation resolution are analyzed by the point spread function (PSF). Finally, simulations are carried out to verify the effectiveness of the theoretical analyses.

Recently, the matrix information geometry (MIG) detector, which characterizes sample data as a Hermitian positive definite (HPD) matrix located on the HPD manifold, was rapidly developed and demonstrated extraordinary performance in numerous applications, especially in heterogeneous clutter backgrounds. In this paper, the geodesic normal coordinate (GNC)-based manifold filter is proposed to improve the detection performance of the MIG detector in strong clutter backgrounds. Using the GNC system, the distribution of target echoes and clutter on the high-dimensional manifold can be visualized and analyzed. Moreover, by exploiting the information concerning the distribution of matrices, the manifold filter is proposed to enhance target echoes and suppress strong clutter. Then, the manifold-filter-based MIG detector is designed, and its superiority is theoretically analyzed. The actual clutter data is utilized to verify the effectiveness of the proposed method. The results show that the proposed manifold filter achieves a signal-to-clutter ratio improvement of more than 5 dB over the existing MIG detectors.

Raw signal simulation is an important tool to test the effectiveness of the system parameters and imaging algorithms. A fast raw signal simulation method for multi-circular synthetic aperture imaging is proposed based on the inverse processing of the imaging algorithm and the nonuniform fast Fourier transform. The analytical description and processing procedure are presented. Simulation results of an ideal point target and a human model demonstrate the validity and effectiveness. Compared with the common time-domain method, the proposed method can achieve equivalent performance with higher efficiency.

This letter proposes an enhanced matrix constant false alarm rate (CFAR) detection method that works on the lower-dimensional Riemannian manifold. Motivated by general matrix CFAR detection method and dimensionality reduction scheme of the Riemannian manifold, this method obtains a mapping by maximizing the geometric test statistic. Dimensionality reduction is formulated as an orthonormal constraint optimization problem on the Grassmann manifold. Moreover, an explicit mapping is obtained by solving the optimization problem via conjugate gradient approach. Performances of the proposed method are evaluated on the lower-dimensional Riemannian manifold. Experiments on simulated data and real sea clutter data demonstrate that our method leads to the robustness to outliers and the improvement of detection performance over classical methods.

Convolutional neural networks (CNN) have successfully been employed to tackle several remote sensing tasks such as image classification and show better performance than previous techniques. For the radar imaging community, a natural question is: Can CNN be introduced to radar imaging and enhance its performance? This letter gives an affirmative answer to this question. We first propose a processing framework by which a complex-valued CNN (CV-CNN) is used to enhance radar imaging. Then we introduce two modifications to the CV-CNN to adapt it to radar imaging tasks. Subsequently, the method to generate training data is shown and some implementation details are presented. Finally, simulations and experiments are carried out, and both results show the superiority of the proposed method on imaging quality and computational efficiency.

Recently, millimeter-wave (MMW) 3D holography techniques employing a scanning 1D multiple input multiple output (MIMO) array have shown several superiorities for short-range applications than traditional single input single output (SISO) ones. However, current imaging algorithms for this emerging regime are not satisfied, either too slow as a back projection (BP) manner is used or of poor quality since several steps of approximations are introduced. In this paper, two fast fully-focused imaging algorithms are developed towards fixing these drawbacks. Both algorithms are based on the assumption that the receivers are evenly distributed. The first algorithm further hypothesizes that the transmitters are also evenly located, while the second algorithm needs looser restrictions that the transmitters can be arbitrarily positioned. The frequency domain expressions of the modified Kirchhoff method are also derived and used to promote the precision of the proposed algorithms. In addition, several implementation issues including resolution, sampling criteria and computational complexity are discussed. Finally, both simulation and experimental results validate the effectiveness of the proposed methods on reconstruction quality and computational efficiency.

This study explores the application of geometric measures‐based means and medians on the Riemannian manifold of Hermitian positive‐definite (HPD) matrix to target detection problems in radar systems. Firstly, the slow‐time dimension of radar received clutter data in each cell is modelled and mapped to HPD matrix space, which can be described as a complex Riemannian manifold. Each point of this manifold is an HPD matrix. Then, several geometric measures are presented for measuring closeness between two HPD matrices. According to these measures, the means and medians of a finite collection of HPD matrices are deduced, and various matrix constant false alarm rate (CFAR) detectors are designed. The principle of target detection is that if a location has enough dissimilarity from the geometric mean or median estimated by its neighbouring locations, targets are supposed to appear at this location. Moreover, different distance measures adopted in detector can result in different performance of detection. These differences are owing to different measure structure, reflected by the anisotropy of a location on the Riemannian manifold. Numerical experiments are given to demonstrate the relationship between anisotropy of the geometric measures and the detection performance of their corresponding matrix CFAR detectors.

Recently, the electromagnetic waves carrying orbital angular momentum are applied to radar imaging, which has the ability of azimuth resolution without motion limitation. The image reconstructed by Fourier transform method has low quality due to the high sidelobes and the duplicate of real target. In this paper, we reveal the reason in two cases with the help of point spread function. Then the least-squares (LS) method is adopted to get the high-quality imaging. The parameterized imaging model is detailed and several factors affecting the solution are discussed. Numerical experiments show that the results produced by using LS method are much nicer than that by Fourier technique when the noise and model error are not too serious. Moreover, the LS method can lower the limit of elevation angle. Finally, the imaging quality are analyzed quantitatively when the noise and model error are present.

In this paper, novel knowledge‐aided space‐time adaptive processing (KA‐STAP) algorithms using sparse representation/recovery (SR) techniques by exploiting the spatio‐temporal sparsity are proposed to suppress the clutter for airborne pulsed Doppler radar. The proposed algorithms are not simple combinations of KA and SR techniques. Unlike the existing sparsity‐based STAP algorithms, they reduce the dimension of the sparse signal by using prior knowledge resulting in a lower computational complexity. Different from the KA parametric covariance estimation (KAPE) scheme, they estimate the covariance matrix using SR techniques that avoids complex selections of the Doppler shift and the covariance matrix taper. The details of the selection of potential clutter array manifold vectors according to prior knowledge are discussed and compared with the KAPE scheme. Moreover, the implementation issues and the computational complexity analysis for the proposed algorithms are also considered. Simulation results show that our proposed algorithms obtain a better performance and a lower complexity compared with the sparsity‐based STAP algorithms and outperform the KAPE scheme in presence of errors in prior knowledge.

A novel 3-D surface imaging method based on terahertz (THz) dual-frequency interferometry is proposed for the detection and imaging of metal objects. The interferometer is formed first in the range direction with two azimuth–elevation images at different THz frequencies. Then, the 3-D surface image can be obtained by interference processing of two azimuth–elevation imaging results. Compared with the traditional wideband 3-D imaging, the proposed method can avoid the nonlinear error caused by the signal nonlinearity. In addition, the image registration for the interference processing is evitable, when the frequency difference satisfies certain conditions. Based on the electromagnetic calculation data, the 3-D surface imaging results of both rough cone and rough cube validate the effectiveness of the proposed method. Furthermore, the reconstruction error increases slightly with the increasing of the error of frequency difference, which indicates that the proposed method has a good tolerance with respect to frequency instability.

This letter presents an improved polar format algorithm for nonlinear aperture and widefield spotlight synthetic aperture radar imaging. In the proposed algorithm, the chirp $z$ transform is used for uniform range resampling, while the fast Gaussian grid nonuniform fast Fourier transform is employed to focus the nonuniform samples in azimuth. Additionally, the space-variant postfiltering is incorporated for eliminating the geometric distortion and marginal defocusing effects induced by wavefront curvature. The effectiveness of the proposed algorithm is validated by simulation and real large scene Gotcha data set.

Jamming in defence applications is increasingly difficult because of advanced signal processing countermeasures. In this study, task‐dependent power‐constraint optimal jamming techniques are investigated. To prevent the target from being detected, a novel jamming technique is proposed to minimise the signal‐to‐interference‐plus‐noise ratio (SINR) of the radar for extended known and stochastic target. To impair the parameter estimation performance, another jamming technique is proposed which minimises the mutual information (MI) between the radar return and the stochastic target impulse response. The optimal jamming spectrum is obtained assuming that the jammer has intercepted the radar waveform generally. However, the precise characteristic of radar waveform is impossible to capture in practice. To model this, it is considered that the waveform spectrum lies in an uncertainty class confined by known upper and lower bounds. Then, the minimax robust jamming is designed based on the SINR and MI criteria, which optimises the worst‐case performance. Results demonstrate that the two criteria lead to different optimal jamming results but they have a close relationship from the Shannon's capacity equation which provides useful guidance on jamming power allocation for different jamming tasks. However, their behaviour with respect to the waveform uncertainty is the same.

In this study, a novel sparsity‐based space–time adaptive processing algorithm based on the complex‐valued Homotopy technique is proposed for airborne radar applications. The proposed algorithm firstly extends the existing standard real‐valued Homotopy method to a more general complex‐valued application using the gradient approaches. By exploiting the sparsity of the clutter spectrum in the whole spatiotemporal plane, the proposed algorithm recovers the clutter spectrum via the proposed complex Homotopy algorithm and then uses it to estimate the clutter covariance matrix, followed by the space–time filtering and the target detection. Furthermore, the implementations of the proposed algorithm are detailed. The computational complexity analysis shows that the proposed algorithm has a lower‐computational complexity than the existing complex‐valued Homotopy algorithm. Simulation results show that the proposed algorithm converges at a very fast speed (only 4–6 snapshots in the authors simulations) and provides both excellent detection performance and easy parameter settings.

Motivated by classical coincidence imaging which has been realized in optical systems, an instantaneous microwave-radar imaging technique is proposed to obtain focused high-resolution images of targets without motion limitation. Such a radar coincidence imaging method resolves target scatterers based on measuring the independent waveforms of their echoes, which is quite different from conventional radar imaging techniques where target images are derived depending on time-delay and Doppler analysis. Due to the peculiar features of coincidence imaging, there are two potential advantages of the proposed imaging method over the conventional ones: 1) shortening the imaging time to even a pulse width without resolution deterioration so as to improve the performance of processing noncooperative targets and 2) simplifying the receiver complexity, resulting in a lower cost and platform flexibility in application. The basic principle of radar coincidence imaging is to employ the time-space independent detecting signals, which are produced by a multitransmitter configuration, to make scatterers located at different positions reflect independent waveforms from each other, and then to derive the target image based on the prior knowledge of this detecting signal spatial distribution. By constructing the mathematic model, the necessary conditions of the transmitting waveforms are analyzed for achieving radar coincidence imaging. A parameterized image-reconstruction algorithm is introduced to obtain high resolution for microwave radar systems. The effectiveness of this proposed imaging method is demonstrated via a set of simulations. Furthermore, the impacts of modeling error, noise, and waveform independence on the imaging performance are discussed in the experiments.

To have a further understanding of the recently developed space-time adaptive processing (STAP) methods based on sparse representation (SR-STAP), this letter details the clutter sparsity observed by STAP radar systems. First, we review the principle and discuss the existing problems about clutter sparsity of the SR-STAP-type algorithms. Then, a theoretical analysis on clutter sparsity for a side-looking uniform linear array with constant pulse repetition frequency, constant velocity, and no crab is performed. Some important conclusions are obtained, and simulations are used to validate the correctness of them.

This paper presents a detection algorithm for micromotion targets, such as rotating antennas, in the synthetic aperture radar (SAR) geometry. It utilizes target's range cell migration characteristics, i.e. a micromotion target takes on a sine cure in the slowtime-range image, but with incontinuity or gaps when clutter (and part of target energy) is suppressed. Two-stage detection, including MTI and the extended Hough transform, is used to suppress clutter and to extract the sine curve. Quasi-real SAR data of Isleta Lake demonstrate high SNR gains and good detection performance.

A novel algorithm is proposed for detecting and estimating vibrating targets in synthetic aperture radar (SAR) data based on a pulse-repetition-interval (PRI) transform. Azimuthal signals of vibrating targets can be modelled as sinusoidal frequency-modulated (SFM) ones. The algorithm utilises the resemblance between the Doppler spectrum of vibrating-target SFM signals (or ghost image) and a pulse train, and applies to the spectrum the PRI transform originally used for estimating PRIs of pulse trains. The algorithm can detect SAR vibrating targets under moderate signal-to-noise/clutter ratios, and is also capable of accurately estimating the vibration frequencies even if there are multiple targets in a single range cell. The algorithm proposed has been successfully applied to both simulated and quasi-real data, and compared with that of the autocorrelation method, showing its superiority.

There are different kinds of multiple classifiers in complex recognition systems in pursuit of better recognition capabilities. To exploit the classifiers' potential as individual ones sufficiently and enable them to work cooperatively for the best classification results, they need to be considered as a whole and be dynamically managed according to the changing recognition occasions. In this paper, we present the conception of distributed Multiple Classifiers Management (MCM) and a self-adaptive recursive MCM model based on Mixture-of-Experts (ME). A control subsystem is consisted in the model, which allows the classification progress to be controlled by the systems' priori information when necessary. The model adjusts its parameters dynamically according to the current recognition state and gives the recognition results by combining the current individual classifiers' results with the previous combination result under priori information's control. An algorithm based on one step error correction is presented to acquire the model's parameters dynamically. It takes the previous times' ensemble classification results as true and corrects the current weights of the classifiers. At last, an experiment on the recognition of space objects is simulated. The experiment results show that the MCM model in this paper is effective for complex recognition system containing heterogeneous classifiers on improving the recognition rate and robustness.

In inverse synthetic aperture radar (ISAR) imaging of targets with complex motion such as the high maneuvering airplanes and fluctuating ships with oceanic waves, the azimuth echo signals can be modeled with cubic chirps after translational motion compensation, and then, the azimuth focusing quality will be deteriorated by the time-varying chirp rate. In this paper, a parameter estimation method of cubic chirps is proposed based on the discrete chirp Fourier transform (DCFT), which is generated from DCFT for quadratic chirps. Several properties of DCFT for cubic chirps are derived, and we show that the modified DCFT (MDCFT) is more appropriate to deal with the practical applications (e.g., ISAR imaging) than the original DCFT. Therefore, we put forward the imaging algorithm based on MDCFT, and then, simulation results confirm the validity of the proposed algorithm.

Fast raw-signal simulation is of considerable value for missile-borne synthetic aperture radar (SAR) algorithm development. On the basis of the two-dimensional (2-D) Fourier simulation method for stripmap SAR, we present a fast echo simulation method suitable for missile-borne SAR diving with constant acceleration. The analytical expression for the 2-D signal spectrum is derived and then converted to a stripmap one. Simulation results for a point target and a real scene demonstrate its validity and effectiveness.

This paper analyzes the influence of typical target micromotions on synthetic aperture radar (SAR) images, azimuth resolution limit, SAR/ground moving target indication (GMTI), and MTI. According to the micromotion periods contained in the coherent processing interval, a new range model expansion and a generalized paired echo principle are proposed and applied to underlie the analysis. Several new kinds of image characteristics including gray strips, ghost points, and fences are reported, which are sheerly distinct from those of slow movers. Micromotion will also cause a prominent range cell migration even if its amplitude is far smaller than the range resolution. SAR/GMTI and MTI techniques will, in general, become invalid for micromotion targets. The influence is eventually demonstrated by the simulated data in the airborne single-channel geometry, and it can be used for SAR image interpretation as well as passive jamming.

The range Doppler algorithm (RDA) is based on Taylor series expansion for the transfer function (TF) phase component, resulting in increasing phase error as the range frequency or the squint angle increases. We introduce Legendre orthogonal polynomials into synthetic aperture radar data processing steps to replace Taylor series expansion used in approximating the TF phase. A novel range Doppler imaging approach based on Legendre expansion is addressed with an extended RDA as example, and the analytical expressions of the new phase multiplication factors are then derived. The simulation results show that the proposed method provides better focusing performance than the conventional RDA in the same squint case and is more suitable for the large squint mode while there is negligible increment of computation load.