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This letter proposes a new method for focusing ground maneuvering targets and estimating the motion parameters with a synthetic aperture radar (SAR) system. In this method, the Hough transform is applied to estimate the cross-track velocity from the slope of the range walk (RW) trajectory, and the RW and Doppler centroid shift are compensated. The second-order Keystone transform is performed to correct the additional range curve caused by the along-track velocity and cross-track acceleration. Then, we adopt the adaptive polynomial Fourier transform to estimate the second- and third-order Doppler parameters from a 1-D parameter interval, and the corresponding motion parameters are calculated. Finally, the moving target is well focused after the motion parameters compensation because the second- and third-order Doppler parameters are efficiently eliminated. Both the simulated and real data processing results are presented to demonstrate the validity of the proposed algorithm.

Deconvolution technique can be utilized in the forward-looking radar (FLR). However, the forward-looking imaging performance degenerates greatly due to the effect of high-speed movement of the platform. In this article, an efficient Bayesian forward-looking superresolution imaging algorithm based on Doppler deconvolution in expanded beam space is proposed. First, the Doppler phase information caused by the high-speed platform is fully exploited and the Doppler matrix is integrated with the antenna pattern. The Doppler convolution model of the echo signal for forward-looking is derived in this article. Then, the Doppler phase information is adopted to perform the Doppler deconvolution. Moreover, an expanded beam space is constructed to enhance the sparsity of the imaging scene. The complex Gaussian distribution and the Laplace distribution have been used to model the distribution characteristics of noise and targets in the imaging scene, respectively. Finally, based on the Bayesian framework, the forward-looking imaging problem is converted into the convex optimization problem. The performance assessment based on simulated and experimental data, also in comparison to the conventional real beam, truncated singular value decomposition (TSVD), iterative adaptive approach (IAA) methods, has demonstrated the effectiveness of our proposed algorithm under high-speed platform scenarios.

To improve the life-detection radar resolution under certain hardware conditions, in this letter, a deep mutual learning generative adversarial network model (Deep Mutual GAN) is proposed. In the proposed model, the generator can improve the angular resolution of the input low-resolution radar image by five times, which is enough to meet our requirements for the resolution of life detection. We innovatively use two generators in GAN with the same network structure and make the two generators learn from each other. In this way, the learning process of a generator is not only achieved by its confrontation with the discriminator but also guided by another generator. As a result, the knowledge of the generator is no longer only obtained through its own learning; each generator learns knowledge from another generator while learning knowledge by itself. The proposed model can effectively make the convergence of GAN more stable and improves the super resolution effect. We also introduce the details of the network structure of generator and discriminator, in which residual learning and a symmetrical network structure are applied. The experimental results show that the proposed method can achieve state-of-the-art imaging effect, which is meaningful for subsequent target detection and recognition.

Forward-looking correlated imaging plays an increasingly important role in modern radar imaging systems. It overcomes disadvantages of traditional side or squint synthetic aperture radar (SAR) which is dependent on specific relative motion between the radar and target scene. A new microwave forward-looking correlated 3-D imaging method based on random radiation field combined with sparse reconstruction is proposed in this article. Firstly, phased array radar (PAR) is adopted to form different and random antenna patterns. Then, combined with the compressed sensing (CS) theory, the target image can be recovered with very few samples which can break through Rayleigh resolution limitation. Furthermore, the proposed method can achieve resolution at least 5.5 times higher than real aperture imaging. To raise computation efficiency of sparse reconstruction, an improved quasi-Newton iteration method based on graphics processing unit (GPU) platform is developed. Meanwhile, a GPU-based (NVIDIA Tesla K40c) accelerated computing method can significantly reduce the processing time compared with the time given by a personal computer (PC). Both simulation and field experiment verify the validity of the proposed method.

The rigid oscillation and flexible deformation baseline errors occur in dual-antenna unmanned aerial vehicle (UAV) SAR interferometry (InSAR). The errors caused by airflow disturbances and UAV platform mechanical oscillation will lead to interferometric phase undulation. Measuring the errors has high requirements for length and time accuracy for the equipment. In this paper, a time-varying baseline error (TBE) estimation and compensation method based on continuous time-domain subaperture data is proposed. Firstly, we model the TBE and derive its expression in each subaperture image focused by the chirp scaling dechirp (CS-dechirp) algorithm. Then it is possible to extract the estimated differential TBE (D-TBE) from the differential interferogram of overlapping scenes in subaperture images. Further, the full-aperture TBE can be obtained through an integration of the estimated D-TBE. Finally, the full-aperture compensation can be accomplished by a phase correction after the range variation estimation. Taking advantage of the time-domain subaperture, the D-TBE phases are sampled at each subaperture center time, and the proposed method can be well combined with a motion compensation algorithm in the processing flow for UAV InSAR. Furthermore, the case of low coherence is overcome. The results of simulation and real measured airborne single-pass dual-antenna data validate the proposed approach.

In a short observation time, after the range alignment and phase adjustment, the motion of a target can be approximated as a uniform rotation. The radar observing process can be simply described as multiplying an observation matrix on the ISAR image, which can be thought of as a linear system. It is known that the longer observation time is, the higher cross-range resolution is. In order to deal with the conflict between short observation time and high cross-range resolution, we introduce Kalman filtering (KF) into the ISAR imaging and propose a novel method to reconstruct a high-resolution image with short time observed data. As KF has excellent reconstruction performance, it leads to a good application in ISAR image reconstruction. At each observation aperture, the reconstructed image denotes the state vector in KF at the aperture time. It is corrected by a two-step KF process: prediction and update. As iteration continues, the state vector is gradually corrected to a well-focused high-resolution image. Thus, the proposed method can obtain a high-resolution image in a short observation time. Both simulated and real data are applied to demonstrate the performance of the proposed method.

The target detection of the passive multiple-input multiple-output (MIMO) radar that is comprised of multiple illuminators of opportunity and multiple receivers is investigated in this paper. In the passive MIMO radar, the transmitted signals of illuminators of opportunity are totally unknown, and the received signals are contaminated by the colored Gaussian noise with an unknown covariance matrix. The generalized likelihood ratio test (GLRT) is explored for the passive MIMO radar when the channel coefficients are also unknown, and the closed-form GLRT is derived. Compared with the GLRT with unknown transmitted signals and channel coefficients but a known covariance matrix, the proposed method is applicable for a more practical case whenthe covariance matrix of colored noise is unknown, although it has higher computational complexity. Moreover, the proposed GLRT can achieve similar performance as the GLRT with the known covariance matrix when the number of training samples is large enough. Finally, the effectiveness of the proposed GLRT is verified by several numerical examples.

Hidden Markov Model (HMM) is a commonly used modeling approach for the radar target recognition based on high-resolution range profile (HRRP). The HMM describes HRRP with the assumption of Gaussian observation probability, which results in the inaccurate description due to the non-Gaussian property of HRRP. To this end, we propose a hybrid HMM framework, which uses an extra model to fit the observation probability rather than making distribution assumptions. The extra model estimates the posterior probability of the state, and then can calculate the pseudo observation probability, which in turn modulates the other model parameters in HMM. The interplay between HMM and the extra model ultimately leads to a better description for observed data. In this paper, we use the support vector machine (SVM) and deep neural network (DNN) separately as the extra model and validate the superiority of our hybrid HMM in HRRP-based target recognition.

In microwave staring imaging, spatial-resolution of real aperture imaging is limited by actual antenna array aperture. In order to achieve high-resolution imaging of targets with sparse feature, this paper proposes a high-resolution imaging method based on temporal-spatial stochastic radiation field combining compressive sensing (CS) theory. Firstly, the formation and property of temporal-spatial stochastic radiation field are discussed. Then, signal model based on random radiation field is deduced in detail, and on this basis, high-resolution imaging method based on CS is discussed. The proposed method can distinguish targets within the beam coverage and higher quality image is achieved. Finally, numerical simulations and experiments in microwave chamber are performed to validate the method and its analysis.

Phase gradient autofocus (PGA) is widely used in estimating residue phase error due to its efficient and robust. However, its precision severely relies on sample quality. In this paper, an expediting phase gradient autofocus algorithm is proposed to solve the above problem. First, we extract valid echo data area from the received contaminated data. Second, the optimization of the azimuth window size is presented. It gets rid of the limitation that the traditional PGA depends on the experience value. Third, the phase error can be fast calculated without numerous IFFT and zero padding which decrease computational complexity. Furthermore, the computational cost is derived in detail. Finally, Numerical simulation and raw SAR data demonstrate that the new method can achieve better performance than conventional PGA.

In the problem of 3D imaging of air Target by shipborne ISAR, the traditional 3D ISAR imaging method based on interference technology not only has complicated hardware equipment, but also high-order phase terms caused by radar swing cannot be eliminated. A 3D imaging method for single-base shipborne ISAR was proposed. Due to the low efficiency of the conventional imaging method based on FRFT, a time-frequency analysis imaging method based on an improved FRFT is proposed. Firstly, the functional relationship is established on the high-order phase term of the single-base shipborne ISAR echo signal and the 3D coordinates of the scattering point. Then, the higher-order phase terms of the radar echo signal are estimated by an improved FRFT. Finally, Restore the coordinates of each scattering point of the target. The simulation results show that this algorithm can obtain 3D imaging of shipborne ISAR air Target with good efficiency and accuracy.

In order to reconstruct the quadratic frequency modulated (QFM) signal, such as radar signal due to the complexity. A novel method for the parameter estimation of multi-component QFM signal based on a modified product kernel function (MPKF) is proposed in this paper. The method has fewer external cross-terms and light computational burden because of non-iterative operations. Moreover, it is robust against additive noise. Finally, simulated and real data results confirm the proposed method.

Agility radar with the carrier frequency random hopping and the pulse repetition frequency (PRF) staggering from pulse to pulse achieves superior performance against the electromagnetic jamming. This novel scheme leads to the discontinuity of phase in a coherent processing interval, thus the fast Fourier transform-based method is no longer a valid way to estimate the velocity of a target. A novel sparse optimisation method based on compressed sensing is proposed for high-resolution range-Doppler reconstruction from random frequency hopping and PRF-jittering pulses. The performance of moving target detection of the proposed method for frequency agile and PRF-jittering radar is analysed by comparing it with parameters-fixed pulse Doppler radar. Both simulation and field experimental results demonstrate the effectiveness of the proposal.

This paper addresses the problem of ground moving target relocation (GMTR) for airborne dual-channel wide-area radar systems. The monopulse technique can be utilized to perform GMTR. However, in real conditions, the GMTR performance degrades greatly due to the effect of channel mismatch. To tackle this problem, prior knowledge of the antenna pattern information is fully utilized to improve the GMTR performance, and a knowledge-aided GMTR algorithm (KA-GMTR) for airborne dual-channel wide-area radar is proposed in this paper. First, the GMTR model for the two receiving channels is analyzed. The channel mismatch model is constructed, and its expression is derived. Then, the channel mismatch phase error is well estimated by exploiting the prior antenna pattern information based on the least squares (LS) method. Meanwhile, the knowledge-aided monopulse curve (KA-MPC) is derived to perform the direction of arrival (DOA) estimation for potential targets. Finally, KA-GMTR, based on the KA-MPC, is performed to estimate the azimuth offsets and relocate the geometry positions of the potential targets when channel mismatch occurs. Moreover, the target relocation performance is analyzed, and the intrinsic reason that degrades the target relocation accuracy is figured out. The performance assessment based on airborne real-data, also in comparison to the conventional GMTR method, has demonstrated that our proposed KA-GMTR algorithm offers preferable target relocation results under channel mismatch scenarios.

Communication is essential to negotiating safer sex, yet power dynamics make condom use difficult to manage. Guided by Dyadic Power Theory and the chilling effect, we predicted that men’s sexual re...

Chaotic-based random stepped frequency signal is applied in the multiple-input-multiple-output through-the-wall detection radar (MIMO-TWDR)recently. When the frequency modulation (FM) sequence of transmission signal is controlled by the chaotic signal, the single-frequency interference such as the power harmonics sneaking into the phase detector becomes periodical and therefore can be filtered in frequency domain. However, the target echo signal becomes random after chaotic modulation, where the matched filter usually is unable to be realised by Fourier transform and consequently the envelope of direct wave after the phase detector varies stochastically and is difficult to be eliminated by an analogue filter. The FM sequence of random disorganising cannot meet the demand of filtering out the single-frequency interference and direct wave simultaneously. Therefore, a new method of FM sequence optimisation of chaotic-based random stepped frequency signal based on genetic algorithm is proposed to solve these problems in this study. Simulations show that the optimised stepped frequency signal possesses the advantages of both chaotic-based random and linear stepped frequency signal. The proposed scheme achieves excellent performance on direct wave and single-frequency interference suppression and target detection. Moreover, it can avoid the interference between transmission antennas of MIMO radar.

In order to reconstruct the linear frequency modulated (LFM) signal, such as radar signal due to the complexity. A novel parameter estimation method based on a modified convolution kernel function (MCKF) is proposed for multi-component LFM signal in this paper. The method has fewer external cross-terms and light computational burden because of non-searching operations. Moreover, it is robust against additive noise. Finally, simulated and real data results confirm the proposed method.

African American women are vulnerable for sexual health risk; thus, condom use is essential. Guided by research linking goals to communicative content, this study explored women's use of I-, you-, we-, and hedging language during condom negotiation. Female participants (N = 193) engaged in a condom negotiation role play with male confederates, where language use measures were coded. I-language was used the most frequently. Language use differed as a function of men's tactics, such that women primarily used I-language in response to verbal attacks, you-language in response to seduction, and I- and we-language in response to information seeking. Women who engaged in more recent condom use were more likely to use you-language, and when confederates attacked, they responded with more you-language and less hedging.

Bi-functional meta-surfaces capable of simultaneously controlling polarization states and wave-fronts of electromagnetic fields are introduced into the design of Cassegrain system for the synthesis of multi-beams. More specifically, electromagnetic fields reflected by the secondary meta-surface with tailored diverged wave-fronts would be collimated by the primary meta-surface into multi-beams with transformed polarization states that can directly go through the secondary meta-surface without any blockage. Especially, we show that such a polarization-twisting meta-surface Cassegrain system can possess much more compact configuration by properly devising the phase distribution over the secondary meta-surface, and can also achieve high-purity polarized multiple radiations when we enlarge the secondary meta-surface as a radome. The present approach of integrating two bi-functional meta-surfaces into the design of Cassegrain system for the generation of multi-beams should pave the way for building up more advanced meta-surface based architectures with specific characteristics.

In [1] , the result of Fig. 13(b) was incorrectly provided. Now, we provide the corrected result, as shown in Fig. 1 .

For the purpose of target parameter estimation of the orthogonal frequency-division multiplexing (OFDM) radar, a high-resolution method of joint estimation on range and direction of arrival (DOA) based on OFDM array radar is proposed in this paper. It begins with the design and analysis of an echo model of OFDM array radar. Since there is no coupling between range and angle parameter estimation for a narrow-band signal, a method which exploits the data of one snapshot to estimate the range and angle of the target by means of multiple signal classification (MUSIC) based on virtual two-dimensional spatial smoothing in range and angle dimensions is devised. The proposed method is capable of joint estimating the range and DOA of the target in a high resolution under a single snapshot circumstance. Simulation experiments demonstrate the validity of the proposal.

In airborne case, synthetic aperture radar images generally suffer from the deterioration because of the unknown phase error caused by unstable platform and atmosphere perturbation. To obtain the phase error, a novel autofocus algorithm referred to as blind homomorphic deconvolution autofocus algorithm is proposed in this study. In this method, a wavelet scaling function is used to construct a smooth subspace that is orthogonal to noise subspace. Then, the phase error can be separated and reconstructed by the generated smooth subspace based on the differences of the smoothness properties between phase error and image reflectivity. Compared with the traditional autofocus methods, the proposed method does not require an iterative estimation. Thus, the computational complexity can be significantly reduced. Simulation and real data processing results validate the effectiveness of the proposed method.

Buck-IPT system is composed of a front power regulation buck circuit and a behind inductive power transfer circuit (IPT), which is higher-order, multi-operating-mode, compound nonlinear system. Aiming at dynamic problems such as large inductor current and slow response in the start-up progress, this paper proposes a dynamic characteristic optimization control method. Firstly, this paper proposes a reduced second-order time-varying model considering the inverter's equivalent load characteristic. And then, Based on this model, a sliding-mode control using the exponential reaching law is applied to optimize the start-up process of the Buck-IPT system. Finally, the simulation and experiment results have verified that the proposed method can realize good dynamic performance with fast response, limited-current of front inductor, no overshoot of front capacitor voltage.

In airborne/stationary bistatic-synthetic-aperture-radar imaging, translational invariance was no longer valid. After range cell migration correction, the range-compressed signal under the same range gate exhibited azimuth-dependent FM rates that made the motion-induced phase error difficult to separate from the echoes. To solve this problem, an azimuth-dependent phase gradient autofocus (PGA) algorithm was proposed. Different from the conventional PGA, the residual quadratic phase arising from the azimuth-dependent FM rates was additionally estimated and compensated. As the influence of the azimuth-dependent FM rates was greatly reduced, a phase gradient estimator was subsequently applied for accurate phase error retrieval. Acquired raw data were analyzed to verify the proposed algorithm.

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.

A new miniature linear frequency-modulated continuous-wave radar which mounted on an unmanned aerial vehicle is presented. It allows the accomplishment of high resolution synthetic aperture radar imaging in real-time. Only a Kintex-7 field programmable gate array from Xilinx is utilized for whole signal processing of sophisticated radar imaging algorithms. The proposed hardware architecture achieves remarkable improvement in integration, power consumption, volume, and computing performance over its predecessor designs. The realized design is verified by flight campaigns.

In this paper, we demonstrate a new three- dimensional (3-D) radar imaging mechanism borrowing the concept of optical ghost imaging (GI). High range resolution is achieved by transmitting wideband electromagnetic (EM) signal, while high resolution in the orthogonal plane is obtained by correlating the echoes with radar antenna's stochastically fluctuating radiation pattern which can be generated with a 2-D phased array. This new concept of radar imaging takes no advantage of relative motion between the target and radar platform. Moreover, by combining the compressive sensing (CS) theory, our proposed imaging system is capable of realizing super-resolution with fewer data samples. Numerical simulation is given to validate the proposal.

High-frequency (HF) over-the-horizon radar (OTHR) works in a very complicated electromagnetic environment. It usually suffers performance degradation caused by transient interference. In this paper, we study the transient interference excision and full spectrum reconstruction of maneuvering targets. The segmental subspace projection (SP) approach is applied to suppress the clutter and locate the transient interference. After interference excision, the spectrum is reconstructed from incomplete measurements via compressive sensing (CS) by using a redundant Fourier-chirp dictionary. An improved orthogonal matching pursuit (IOMP) algorithm is developed to solve the sparse decomposition optimization. Experimental results demonstrate the effectiveness of the proposed methods.

Frequency agility radar, with randomly varied carrier frequency from pulse to pulse, exhibits superior performance compared to the conventional fixed carrier frequency pulse-Doppler radar against the electromagnetic interference. A novel moving target detection (MTD) method is proposed for the estimation of the target's velocity of frequency agility radar based on pulses within a coherent processing interval by using sparse reconstruction. Hardware implementation of orthogonal matching pursuit algorithm is executed on Xilinx Virtex-7 Field Programmable Gata Array (FPGA) to perform sparse optimization. Finally, a series of experiments are performed to evaluate the performance of proposed MTD method for frequency agility radar systems.

Ship target ISAR imaging has significant meaning in maritime surveillance and traffic management in vast sea areas. This paper propose a novel approach for long observation time of ship target from airborne radar. First, a second-order generalised keystone formatting method is used to compensate for the range curvature induced by long integration time. Secondly, estimate the target motion parameter by virtue of time frequency techinque(WVD) and reomve the quadratic term of slow time, then use the second- order keystone formatting again to eliminate the residual range walk. So the coupling of the range and azimuth term are completely removed. At last, the clear ISAR image can be obtained. Both numerical and experimental results are provided to demonstrate the performance of the proposed method. (4 pages)

In Airborne radar imaging of ship targets on the river (or the sea), itpsilas difficult to image each targets because each individual target may have its own motion characteristic different from others. Aim at this problem, this paper proposed a Airborne hybrid SAR/ISAR method imaging of muti-ship targets. By this method, the raw data is processed by SAR algorithm firstly, then, extract and recover of selected ship data from SAR image. Finally, ISAR image of selected targets. the effectiveness of the proposed method is verified by the real data processing.

Radar simulator is an effective tool for performance assessment of radar systems by accurately reproducing echo signals from complicated environment. This paper presents a design of fast multi-waveform radar echo generation based on deconvolution method. First, scene information is retrieved from outfield data based on improved conjugate gradient algorithm. Then, the new radar echoes are generated through convolution of new transmitted signal and restored scene information. A fast and area-efficient field programmable gate array realization is provided to meet the real-time requirement of radar echo simulation. Finally, a series of experiments are performed to evaluate the effectiveness of proposed radar simulation instrument.

In the above paper (ibid., vol. 10, no, 6, pp. 1290-1294, Nov. 2013), there is an error in equation (5). The correction is presented here.