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There has been an increasing interest in addressing the issue of high-resolution inverse synthetic aperture radar (ISAR) imaging from sparse sampling data. Traditional compressed sensing (CS) and matrix completion (MC) methods are based on sparse and low-rank constraints, respectively, which do not make full use of the structure of ISAR data. In this article, a sparse ISAR imaging algorithm using a structured low-rank approach is proposed for enhanced imaging performance. Based on the observation that the structured Hankel matrix has better low-rank property, the proposed algorithm can outperform the group of conventional MC methods in terms of accuracy to data quality and quantity. Rather than using the traditional singular value decomposition (SVD) solution of nuclear norm minimization, the proposed algorithm restates the nuclear norm via an equivalent reformulation that the structured Hankel matrix can be decomposed into two disjointed parts to avoid the dimensional expansion of the Hankel matrix. Meanwhile, the alternative direction method of multipliers (ADMMs) is applied to effectively reduce the computational complexity. Finally, the effectiveness of the proposed algorithm is further validated using the experiments on simulated and measured data.

High speed, high maneuverability, and weak space-moving targets (SMTs) are major threats for space-borne radar (SBR) systems. First, the severe range migration (RM) and Doppler extension are induced by complex relative motion between the radar platform and non-cooperative moving targets, making moving target detection (MTD), and parameter estimation particularly difficult. Apart from this challenge, Doppler ambiguity and Doppler aliasing arise from the limited pulse repetition frequency (PRF) of a SBR system to ensure an adequate coverage rate, which may make the existing MTD algorithms deteriorate dramatically. To address these issues, we focus on the detection of high speed and high maneuverability targets based on the modified keystone transform matched filtering (MKTMF), whose Doppler frequency exceeds PRF as well as spans multiple PRFs. The proposed method is suitable for the weak MTD under a low signal-to-noise ratio (SNR) case since the nonlinear operation is not involved. Finally, some numerical results and real data results are provided to validate the superiority of the proposed method.

Eucommia ulmoides, a precious Chinese herbal medicine, lack of studies on the detection of its pesticide residues. A simple sample preparation based on the solid-phase extraction (SPE) procedure was established for the analysis of 16 kinds of pesticides in Eucommia ulmoides by gas chromatography mass spectrometry (GC-MS) with selective ion monitoring mode. Here, the type and volume of extraction solvent and eluent, the kinds of sorbents in SPE Cleanert column, were optimized. Under the optimal conditions, a good linear relationship was obtained in the range of 0.005 to 5.0 mg/L with correlation coefficient (r) higher than 0.9990, and the average recoveries (AR) of 16 pesticides ranged from 79.6 to 109.2% at the spiking levels of 0.01, 0.1, and 0.5 mg/kg. The relative standard deviations (RSD) were 0.78 to 9.56% (n = 6). The results show that the established method can not only fully meet the analytical requirements of various pesticides in Eucommia ulmoides, but also have the potential to be applied in the detection of pesticide residues in others Chinese herbal medicine.

Phase adjustment with the autofocus process is essential to inverse synthetic aperture radar (ISAR) imagery of manoeuvring targets. Conventional parametric and non-parametric autofocus methods usually degrade in front of error model mismatch and noise interference. Especially, the target manoeuvre also induces higher order phases that have the spatial-variant characteristic. In this study, a range-dependent semi-parametric autofocus algorithm for ISAR imagery is proposed. The range-variant phase is modelled semi-parametrically with a discrete cosine transform kernel. Implemented by the maximum sharpness optimisation, the algorithm well adapts to complex phase forms and enhances the robustness to strong noise. Accelerated solver to the optimisation is also introduced. Simulation and real data experiments confirm the advantages of the range-dependent semi-parametric autofocus algorithm compared with conventional algorithms.

Millimeter-wave (MMW) radar hand gesture recognition technology is becoming important in many electronic device control applications. Currently, most existing approaches utilize the radical and micro-Doppler features from single-channel MMW radar, which ignores the different importance of the information contained in the micro-Doppler feature background or target areas. In this paper, we propose an algorithm for hand gesture recognition jointly using multi-channel signatures. The algorithm blends the information of both micro-Doppler features and instantaneous angles (azimuth and elevation) to accomplish hand gesture recognition performed with the convolutional neural network (CNN). To have a better features fusion and make CNN focus on the most important target signal regions and suppress the unnecessary noise areas, we apply the channel and spatial attention-based feature refinement modules. We also employ gesture movement mechanism-based data augmentation for more effective training to alleviate potential overfitting. Extensive experiments demonstrate the effectiveness and superiorities of the proposed algorithm. This method achieves a correct classification rate of 96.61%, approximately 5% higher than that of the single-channel-based recognition strategy as measured based on MMW radar datasets.

The azimuth multichannel spaceborne SAR system can achieve the high-resolution and wide-swath simultaneously. In a high-resolution and wide swath (HRWS) SAR system, the Doppler centroid (DC) frequency estimation is an important step for Doppler ambiguity signal reconstruction, which should be precisely estimated. In this paper, we propose a novel Doppler centroid frequency estimation method for HRWS-SAR system based on modified shuffled frog leafing algorithm (MSFLA). Firstly, the cost function is designed based on the average power difference between ambiguities and useful signal. Then, the MLSFA algorithm is proposed to accomplish the DC estimation. Finally, a good HRWS-SAR image is obtained based on the estimated DC. Compared with the traditional time-domain based DC estimation methods, the proposed method is not affected by the troubled cross-terms induced by high-order moments. The effectiveness of the proposed method is verified by the simulation experiments and real-measured SAR data.

Narrow-band interference (NBI) is a major threat for synthetic aperture radar (SAR) system, which degrades the imaging quality severely. In this paper, a new type of narrow-band interference (NBI) is investigated, i.e., periodicity transmitted NBI (PT -NBI). Compared with the conventional NBI, PT -NBI is intermittently emerged in one azimuth pulse, which shows periodic property in the range time domain and broadband characteristic in the range frequency domain, resulting in the severe performance degradation by using the existing NBI suppression methods. The distinctions and characteristics between the NBI and PT -NBI in different transform domains are analyzed firstly. On this basis, a PT -NBI suppression method is proposed by applying the joint time domain detection and Eigensubspace (ESP) filtering in this paper, which can effectively realize the interference suppression for PT - NBI.

Compared with microwave synthetic aperture radar (SAR), terahertz SAR (THz-SAR) is easier to achieve ultrahigh-resolution image due to its higher frequency and shorter wavelength. However, higher carrier frequency makes THz-SAR image quality very sensitive to high-frequency vibration error of motion platform. Therefore, this paper proposes a novel high-frequency vibration error estimation and compensation algorithm for THz-SAR imaging based on local fractional Fourier transform (LFrFT). Firstly, the high-frequency vibration error of the motion platform is modeled as a simple harmonic motion and THz-SAR echo signal received in each range pixel can be considered as a sinusoidal frequency modulation (SFM) signal. A novel algorithm for the parameter estimation of the SFM signal based on LFrFT is proposed. The instantaneous chirp rate of the SFM signal is estimated by determining the matched order of LFrFT in a sliding small-time window and the vibration acceleration is obtained. Hence, the vibration frequency can be estimated by the spectrum analysis of estimated vibration acceleration. With the estimated vibration acceleration and vibration frequency, the SFM signal is reconstructed. Then, the corresponding THz-SAR imaging algorithm is proposed to estimate and compensate the phase error caused by the high-frequency vibration error of the motion platform and realize high-frequency vibration error estimation and compensation for THz-SAR imaging. Finally, the effectiveness of the novel algorithm proposed in this paper is demonstrated by simulation results.

Using the array technique, the multi-channel synthetic aperture radar (SAR) has the capability of high-resolution imaging, detection, and location of the ground moving targets, which is a powerful tool during the remote sensing of smart city. Recently, the theory of compressive sensing has been applied to radar imaging with data of compressive sampling, which can effectively reduce the burden of current radar system. In this paper, we focus on SAR moving targets imaging from sparse aperture (SA) data with accurate target motion compensation. The procedure of motion compensation is decomposed into two steps: the SPECAN processing in the range frequency and azimuth time domain is first applied and then followed by the residue component correction embedded into sparse imaging. To overcome the SA and target motion, a novel parametric sparse imaging approach is proposed by addressing the problems of Doppler ambiguity and phase errors. In the scheme, a parametric and dynamic dictionary is used to include these two important issues. Then, a modified orthogonal matching pursuit method is presented for high-quality imaging with Doppler ambiguity number estimation and phase error correction, which can deal with the case of multiple moving targets. Finally, experimental analysis is performed to confirm the effectiveness of the proposed algorithm.

Due to the high frequency band and wide bandwidth of Terahertz imaging radar, the high frequency generally performs unstable in system. In detail, the linearly frequency modulated (LFM) signal shows nonlinearity and the frequency center fluctuates randomly with time. It will potentially lead to the degradation of Terahertz inverse synthetic aperture radar (THz-ISAR) imaging quality. To address this problem, this paper presents a frequency nonlinearity and fluctuation correction method based on minimum entropy (ME). Following the error model, both the error can be corrected by applying the ME metric to the range and azimuth direction. The feasibility and adaptability of this proposal have been validated by simulation and real data experimental result.

Being capable of enhancing the flexibility and observing ability of synthetic aperture radar (SAR), squint mode is one of the most essential operating modes in SAR applications. However, processing of highly squinted SAR data is usually a challenging task attributed to the spatial-variant range cell migration over a long aperture. The Omega-k algorithm is generally accepted as an ideal solution to this problem. In this paper, we focus on using the wavenumber-domain approach for highly squinted unmanned aerial vehicle (UAV) SAR imagery. A squinted phase gradient autofocus (SPGA) algorithm is proposed to overcome the severe motion errors, including phase and nonsystematic errors. Herein, the inconsistence of phase error and range error in the squinted wavenumber-domain imaging is first presented, which reveals that even the motion error introduces very small phase error, it causes considerable range error due to the Stolt mapping. Based on this, two schemes of SPGA-based motion compensation are developed according to the severity of motion error. By adapting the advantages of weighted phase gradient autofocus and quality phase gradient autofocus, the robustness of SPGA is ensured. Real measured data sets are used to validate the proposed approach for highly squinted UAV-SAR imagery.

Clutter suppression is always problematic in synthetic aperture radar (SAR) imaging. Especially when clutter is strong, inappropriate threshold on image amplitude may lead to failure to completely separate the background and the region of interest (ROI). Therefore, this paper firstly presents an adaptive threshold estimation method from the view of coherence. To protect weak scatterers, neighbored points around the extracted centers are also preserved. With the strong sparsity of the ROI in the image, the resolution of the extracted target region is enhanced by the sparse signal processing technique. Experimental results on simulated and real data both confirm its practicability.

There are two square-root terms in the range history of a return signal from a bistatic synthetic aperture radar (BiSAR). The transfer function for imaging in the 2-D frequency or range Doppler domain using the principle of stationary phase cannot be analytically derived. To address this problem, we approximated the stationary phase of the 2-D spectrum with an expansion of the Taylor series on the azimuth frequency and called the approximation as the derivatives of an implicit function (DIF). After algebraic manipulation, the DIF and 2-D spectrum were obtained for a generally configured BiSAR. With the DIF method, we dissolved one square-root term out of the two for an azimuth-invariant BiSAR, which is particularly advantageous in the implementation of an imaging algorithm. Then, a modified range Doppler algorithm was developed to process the BiSAR data. Promising results were obtained.

The theory of compressed sampling (CS) indicates that exact recovery of an unknown sparse signal can be achieved from very limited samples. For inversed synthetic aperture radar (ISAR), the image of a target is usually constructed by strong scattering centers whose number is much smaller than that of pixels of an image plane. This sparsity of the ISAR signal intrinsically paves a way to apply CS to the reconstruction of high-resolution ISAR imagery. CS-based high-resolution ISAR imaging with limited pulses is developed, and it performs well in the case of high signal-to-noise ratios. However, strong noise and clutter are usually inevitable in radar imaging, which challenges current high-resolution imaging approaches based on parametric modeling, including the CS-based approach. In this paper, we present an improved version of CS-based high-resolution imaging to overcome strong noise and clutter by combining coherent projectors and weighting with the CS optimization for ISAR image generation. Real data are used to test the robustness of the improved CS imaging compared with other current techniques. Experimental results show that the approach is capable of precise estimation of scattering centers and effective suppression of noise.

In general, conventional error correction for inverse synthetic aperture radarimaging consists of range alignment and phase adjustment, which compensate range shift and phase error, respectively. Minimum entropy-based methods have been proposed to realize range alignment and phase adjustment. However, it becomes challenging to align high-resolution profiles when strong noise presents, even using entropy minimization. Consequently, the subsequent phase adjustment fails to correct phase errors. In this article, we propose a novel method for translational motion correction, where entropy minimization is utilized to achieve range alignment and phase adjustment jointly. And, a coordinate descent algorithm is proposed to solve the optimization implemented by quasi-Newton algorithm. Moreover, a method for coarse motion estimation is also proposed for initialization in solving the optimization. Both simulated and real-measured datasets are used to confirm the effectiveness of the joint motion correction in low signal-to-noise ratio situations.

The techniques of accurate cross-range scaling and motion compensation both play significant roles in inverse synthetic aperture radar (ISAR) imaging. This paper proposes a new method to estimate motion parameters for ISAR targets scaling as well as motion compensation. With a time-varying autoregressive (TVAR) model, the pole histories of dominant scatters can be obtained. According to the time-frequency relationship, motion parameters including the effective rotational velocity and the coefficients of phase error are extracted. Using these parameters, we can not only scale the image but also compensate phase error to focus the image. Simulation and real-data experiments illustrate its feasibility and robustness. (6 pages)

Spotlight synthetic aperture radar (SAR) is an effective way to obtain finer azimuth resolution than the achievement in strip-map mode with the same physical antenna. In general, for spotlight SAR imaging, high azimuth resolution requires long synthetic aperture length. However, in practical airborne applications, because of the inevitable atmospheric turbulence during the long flight trajectory, more complicated motion error is induced that severely degrades the focusing quality of the SAR image. It makes motion compensation (MOCO) for high-resolution spotlight (HR-Spotlight) SAR imagery more difficult than that for other SAR systems. To tackle the HR-Spotlight SAR data contaminated by the complicated motion error, a novel MOCO algorithm based on entropy minimisation is proposed, which is named by minimum entropy MOCO (ME-MOCO). In this approach, by fitting a polynomial to the motion error, the entropy of a focused image is utilised as the optimisation function of the polynomial coefficients. Attributed to damped Newton method, a modified strategy is designed, which results in that the data-driven ME-MOCO algorithm estimates high order polynomial parameters accurately and efficiently. Besides, the proposed algorithm is efficient for the capability of exploiting the fast Fourier transform through the processing chain. Real data experiments and comparisons demonstrate the effectiveness and superiority of the proposal.

For inversed synthetic aperture radar (ISAR) imaging of remote manoeuvring targets via linearly stepped-frequency modulated waveform, a digital stretch technique and a phase coherence compensation method are proposed to achieve high-resolution range profile (HRRP) synthesis. By using this digital stretch technique, the sampling rate can be dramatically reduced and thus the data amount can be significantly decreased. Since the motion estimation error is inevitable for conventional parameters estimation methods, the residual Doppler will give rise to the phase discontinuity at the juncture between the neighbouring sub-pulses, which may blur the HRRP to some extent. A phase coherence compensation method is proposed to further eliminate the phase difference. Owing to the adaptively and convenience for residual Doppler compensation of the proposed method, a good HRRP can be obtained by means of time-domain sub-pulses synthesis technique. Finally, a well-focused ISAR image can be achieved after the conventional range alignment and autofocusing. Experimental results confirm the effectiveness of the proposed methods.