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Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) technology is widely used in integrated radar and communication systems (IRCSs). Moreover, index modulation (IM) is a reliable OFDM transmission scheme in the field of communication, which transmits information by arranging several distinguishable constellations. In this paper, we propose a sparse reconstruction-based joint signal processing scheme for integrated MIMO-OFDM-IM systems. Combining the advantages of MIMO and OFDM-IM technologies, the integrated MIMO-OFDM-IM signal design is realized through the reasonable allocation of bits and subcarriers, resulting in better intercarrier interference (ICI) resistance and a higher transmission efficiency. Taking advantage of the sparseness of OFDM-IM, an improved target parameter estimation method based on sparse signal reconstruction is explored to eliminate the influence of empty subcarriers on the matched filtering at the receiver side. In addition, an improved sequential Monte Carlo signal detection method is introduced to realize the efficient detection of communication signals. The simulation results show that the proposed integrated system is 5 dB lower in the peak sidelobe ratio (PSLR) and 1.5 × 10 5 lower in the number of complex multiplications than the latest MIMO-OFDM system and can achieve almost the same parameter estimation performance. With the same spectral efficiency, it has a lower bit error rate (BER) than existing methods. [ABSTRACT FROM AUTHOR]

Compared to traditional single orthogonal frequency division multiplexing (OFDM) radar and OFDM communication systems, integrated OFDM radar and communication systems have the advantages of improved sharing of scarce spectrum resources, a simple hardware structure and a reduced interference between signals. In this paper, a constraint relaxation-based robust OFDM multiplexing waveform (ROW-CR) design method for integrated radar and communication systems is proposed. Considering the influence of correlated clutter and jamming signals, transmission and reception models of different system platforms are established by allocating subcarrier complex weights of different antenna signals so as to determine radar conditional mutual information and communication channel capacity parameters. Meanwhile, a restricted closed model is introduced through the limit range of the target frequency response. Then, a robust OFDM waveform optimization problem for integrated radar and communication systems is constructed by the minimax criterion, and the closed-form solution is obtained by adopting an improved method based on trace function properties and constraint relaxation, resulting in a better radar and communication performance trade-off. In addition, a parameter hierarchical optimization-based robust OFDM waveform (ROW-PHO) design method is further explored to reduce the computational complexity, and this method can also ensure a low system performance loss. Finally, the numerical simulation results verify the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Randomized stepped‐frequency (RSF) radar has the potential of realizing joint high‐resolution range‐Doppler estimation. However, estimating multiple targets' two‐dimensional parameters at high efficiency based on RSF waveforms is still a difficult issue. In this study, a novel algorithm, which estimates multiple targets' velocities by using different frequencies pulses and then generates their high‐range‐resolution profiles (HRRPs), is proposed for RSF radar. In the velocities estimation, by setting a certain difference‐frequency parameter, a set of signals with constant range‐phase terms and changing velocity‐phase terms is constructed and then used to generate a new generalized Doppler spectrum called the difference‐frequency Doppler spectrum (DFDS). To suppress rising noise floors caused by sidelobes and cross‐terms in the DFDS, DFDSs corresponding to different difference‐frequency parameters are non‐coherently integrated to obtain a so‐called accumulated DFDS. From the accumulated DFDS, targets are detected and their velocities are estimated. In the HRRPs generation, a special dictionary matrix is structured by using the estimated velocities, and each target HRRP, without interference from other targets, is generated via the compressed sensing (CS) technique. The proposed algorithm avoids huge computation burden by separately performing Doppler and range process, and guarantees the performance in a multi‐target scenario via the noise floors suppression and CS. [ABSTRACT FROM AUTHOR]

Sharing a multiple-input multiple-output (MIMO) radar, the single platform can achieve dual functions of radar and communication (DFRC) within the same frequency spectrum, via the same transmit waveforms. In this paper, a space-time coding scheme is developed for transmit beamforming of DFRC and embedding communication information, without their cross-interference. For transmit beampattern design of DFRC, the shape approximation and integrated power approximation criteria are adopted respectively for waveforms optimization with the constant-envelope constraints of transmit waveforms and the equivalent signal in the communication direction. Based on the space-time coding scheme, the direct constellation mapping (DCM) and phase-rotation constellation mapping (PRCM) methods are proposed to embed information symbols. It turns out that the proposed space-time coding scheme for information constellation mapping can prevent missing information symbols and have better performance in bit-error rate (BER), compared to the existing information-embedding techniques. Moreover, the scheme can reduce the dependence of the communication data rate on radar pulse repetition frequency (PRF). Simulation results are presented to demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Radio frequency interference (RFI) suppression is a hot topic in synthetic aperture radar (SAR) imaging. Mathematically, the RFI suppression problem can be considered as an underdetermined signal separation problem to extract the signal of interest (SOI) from the RFI contaminated raw data. The regularization-based method can exploit both the prior knowledge of RFI and SOI and, therefore, has the advantage of solving the underdetermined problem and preserving the information of SOI. Current regularization methods make use of the RFI prior well by exploiting low-rank representation (LRR) or sparse representation (SR), but the prior knowledge of SOI has not been sufficiently studied and used. In some literature, the sparsity of the raw data or range profile was exploited to formulate the regularization term, which we found to be inadequate in describing the SOI property. In this article, we explore the features of SAR images and propose an RFI suppression model with a combination of multiple image domain regularizations to preserve different types of targets. An efficient solution to the optimization problem is proposed based on the alternating direction multiplier method (ADMM). The proposed method can accurately recover both the sparse strong targets, and the nonsparse regions in the illuminated area and its performance is validated by measured data. [ABSTRACT FROM AUTHOR]

Randomized stepped-frequency (RSF) radar, which transmits pulses with random carrier frequencies in a coherent processing interval, has an excellent electronic counter-countermeasures performance and the ability to generate the high-range-resolution profiles (HRRPs) of target with a narrow bandwidth receiver. Due to the random nature, the choice of carrier frequencies in RSF radar is usually an undersampling of available frequencies, which make the HRRP generated by matched filtering suffer from high noise-like sidelobes. In this article, we take the sinc-envelopes formed by linear-frequency-modulation pulses as well as RSF phases into account, and propose a novel (or generalized) range sidelobe suppression filters design method for RSF chirp waveform based on convex optimization (the model is transformed into a second-order cone program). By using these filters, the target HRRP with much lower sidelobes can be obtained, and the performance can be traded with the instantaneous bandwidth of the receiver, which provides a new idea for solving the sidelobe problem in RSF radar. [ABSTRACT FROM AUTHOR]

Linear frequency-modulated (LFM) continuous-wave (CW) radar is usually the first choice in synthetic aperture radar (SAR) imaging missions due to its relatively low cost and hardware simplicity. However, the use of continuous-wave unnecessarily introduces the problem of intrapulse motion. Furthermore, a large amount of data generated by the CW imaging process may overburden the onboard communication system with its high streaming data rate. On the other hand, a large amount of data may, indeed, not be necessary for high-resolution SAR imaging. In this article, we took full consideration of the intrapulse motion in LFMCW radar systems and modeled the phase preserving extended frequency scaling algorithm (EFSA) reconstruction process with far fewer data samples as compressive sensing problem and used subgradient descent algorithm with optimal step size to realize compressive sensing reconstruction. Our compressive sensing problem is different from the traditional direct inversion-based problem in that our reconstructed results contain both mainlobe and sidelobes. Comparisons were made with mainstream iterative soft thresholding-based reconstruction algorithm to demonstrate its capability to reconstruct large imaging scenes with high resolution. Both simulations and experiments with measured data have verified our proposed algorithm. [ABSTRACT FROM AUTHOR]

Randomized stepped-frequency (RSF) radar, which transmits random-frequency pulses in a coherent processing interval (CPI), has an excellent electronic counter-countermeasures (ECCM) performance and the ability to synthesize wide-frequency band with a narrow bandwidth receiver. However, due to the use of different frequency pulses, RSF waveform is incompatible with the conventional moving-target-detection (MTD) technique, which makes clutter suppression a difficult problem for RSF radar and limits its application. In this article, the effect of clutter on the coherent processing for RSF radar is first analyzed. The result shows that coherent processing can improve the signal-to-clutter ratio on target Doppler channel by $N$ (number of pulses in one CPI) times, which suppresses weak clutter scatterers to a large extent whilst strong clutter scatterers would still be left influential. Focusing on dominant clutter scatterers, the adaptive range-Doppler clutter suppression algorithm, which is based on designing clutter suppression filters with two-dimensional property to obtain the clear high-range-resolution profile of moving target in clutter environment, is then proposed for RSF radar. The proposed algorithm, which takes into account clutter Doppler extension, can be used for RSF radar coherent processing in clutter environment. [ABSTRACT FROM AUTHOR]

In this paper, we consider the multi-frame integration (including intra-frame integration and inter-frame integration) problem for the detection of weak moving targets, where range walk effect (RWE) occurs within the integration time. A novel integration method is proposed to achieve the accumulation of a target's multi-frame radar echoes. More specifically, the proposed integration method contains two main steps, where the modified Radon Fourier transform (RFT) is firstly used to realize the intra-frame coherent integration for every frame of echo signals. After that, the RFT-domain integration algorithm is presented to achieve the inter-frame coherent integration based on the characteristics of RFT outputs, during which the accumulation of target energy distributed in multiple frames could be realized. The explicit expressions and analysis for various performance measures of the proposed method, such as integration output response, probability of detection, input-output signal-to-noise ratio (SNR), and blind speed sidelobe (BSSL) response are derived and given. Simulation experiments are given to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The performance of synthetic aperture radar is vulnerable to radio frequency interference (RFI). In many situations, the RFI has a low-rank property, since the frequency bands occupied by RFI usually remain stable during a short slow time period. Therefore, low-rank representation (LRR)-based methods can be applied to separate RFI and signal of interest (SOI), by minimizing the rank of RFI components with a regularization constraint to protect SOI. However, traditional methods use the sparsity of the raw data or range profile to formulate the regularization term, which fails to describe the properties of SOI accurately. In addition to the sparse property of range profiles, this article explores the common patterns hidden in the range profiles and proposes two new LRR-based RFI suppression optimization models with a well-designed regularization term to describe such common sparsity to protect the SOI. Four methods are proposed to solve the optimization problems based on the alternating direction multiplier (ADM) method, which provides tradeoff between efficiency and accuracy. Compared with traditional LRR-based RFI suppression methods, the proposed methods make a more precise description of the features of SOI, therefore can better protect the information of SOI during the RFI suppression process and improves the imaging quality. The superior performance of the proposed method is validated by measured data in both sparse and nonsparse scenes. [ABSTRACT FROM AUTHOR]

In this letter, we focus on the high-resolution imaging of ship targets under sparse aperture condition and its application in three-dimensional (3D) imaging. A bi-static high-resolution algorithm dealing with the sparse echo condition is proposed, which consists of the following processing steps: (1) A side-lobe apodization–multiple orthogonal least-square method is proposed to recover the complete signal and reduce the influence of side-lobe at the same time, which enhances the precision of phase extraction in interferometric inverse synthetic aperture radar (InISAR) imaging; (2) The bi-static configuration is used to accentuate the relative motion between the target and radar, such that the requirement for long coherence processing time can be avoided and (3) The 3D InISAR technique is employed to obtain the 3D images of the ship targets. Experiments with simulated and real data are utilized to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Deceptive jamming can severely degrade synthetic aperture radar (SAR) image quality by introducing high-fidelity false targets. In this letter, a simultaneous deceptive jamming suppression and target reconstruction method for a single-channel SAR system is proposed. The signal model is formulated by constructing a joint dictionary based on different time–frequency distributions of the actual targets and false targets. Then, an efficient algorithm is proposed based on the alternating direction method of multipliers (ADMMs) to simultaneously recover the actual and false targets. Several strategies are also proposed to accelerate the computation. Compared with other existing single-channel SAR deceptive jamming suppression methods, the proposed method has lower reconstruction error and computational load. Simulation results demonstrate the superior performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Conventional single antenna synthetic aperture radar (SAR) has a conflict between wide imaging swath width and fine cross-range resolution. Single input multiple output (SIMO) and multiple input multiple output (MIMO) radars are the two methods to overcome this limitation. In order to separate the signals of MIMO radar, the codes are expected to be orthogonal for all delays. This is impossible in SAR. Due to the special imaging geometry of SAR, short-term shift orthogonal (STSO) codes were proposed to be used in MIMO SAR. This letter compares the SIMO SAR structure and MIMO SAR structure using STSO codes. We draw a conclusion that the number of receivers of MIMO structure is less than that of SIMO structure. [ABSTRACT FROM AUTHOR]

For conventional polar format algorithm (PFA), because of the image distortion and defocus caused by the plane-wave assumption, the effective imaging scene is bounded to a small region near the reference point. In this paper, refocusing and zoom-in polar format algorithms (RZPFAs) for curvilinear spotlight synthetic aperture radar (SAR) imaging are proposed, which can produce refocused image for an arbitrary region of interest (ROI). First, refocusing is implemented by a phase compensation of the already dechirped signal with respect to the new refocusing point. The relation [named refocused distortion mapping (RDM)] between targets’ actual locations and their reconstructed locations in the refocused image is then derived. Based on the RDM, the distortion-negligible region (DiR) and defocus-negligible region (DeR) are defined as extents within which the residual distortion and residual defocus are less than some preselected thresholds. When the ROI is within both the DiR and the DeR, zoom-in imaging based on nonuniform fast Fourier transform of type-1 (NuFFT-1) is sufficient to form a desired image, which is named RZPFA based on NuFFT-1 (RZPFA-1). However, when the ROI exceeds the DiR, zoom-in imaging based on nonuniform fast Fourier transform of type-3 (NuFFT-3) should be selected, which is named RZPFA based on NuFFT-3 (RZPFA-3). With just a small amount of extra computation than RZPFA-1, RZPFA-3 can realize pixel-based quasi-orthorectified imaging directly. Besides, the proposed algorithms can also be extended to wide-area persistent imaging where ROI is larger than DeR. The simulation and real data results demonstrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Long time coherent integration (LTCI) technique enhances radar detection of maneuvering weak targets. Unfortunately, range migration (RM) and Doppler frequency migration (DFM) would occur during the integration period. Existing LTCI methods are all based on the assumption that the target is of single motion model (i.e., the target's motion model is fixed) during the long integration time. However, with increasing target's maneuverability and long observation time (needed to detect weak targets), the target's motion model is often changing (i.e., target is of multiple motion models) during the integration time, which would reduce the performance of existing LTCI methods significantly. In this paper, we address the LTCI problem for detecting a maneuvering weak target with multiple motion models, where the target signal model is first established. Thereafter, the short-time generalized Radon Fourier transform (STGRFT) based LTCI algorithm is proposed. The STGRFT method could not only eliminate RM and DFM but also estimate the model-changing point (i.e., the time when target's motion model changes) and accumulate the target energy distributed in different motion stages (corresponding to different motion models). Finally, numerical and real-data experiments are carried out to demonstrate its effectiveness. The results confirm that in terms of integration gain, input–output signal-to-noise ratio performance and detection ability, STGRFT-based method is superior to the existing popular LTCI algorithms. [ABSTRACT FROM AUTHOR]

Conventional single-platform spaceborne synthetic aperture radar has a conflict between wide imaging swath width and fine-cross-range resolution. Displaced phase center antennas is one method to overcome this limitation. For spectrum recovery of nonuniform sampling, a detailed analysis on the relationship between coefficient matrix singularity and the number of effective phase centers (EPCs) passed by the platform during one pulse repetition time (PRT) is given. When the distance passed by the platform during one PRT is multiple of the distance between two EPCs plus a small value, the coefficient matrix is singularity. An explicit expression of the coefficient matrix is also derived. [ABSTRACT FROM AUTHOR]

Radio frequency interference (RFI) can severely degrade the image quality for synthetic aperture radar (SAR). Traditional sparse recovery-based RFI suppression methods construct a joint dictionary to represent both the time-domain RFI and the useful target echo (UTE), thus, transforming the task of RFI suppression into a signal recovery problem. However, these methods generally have poor performance when the one-dimensional (1-D) range profiles are nonsparse. To overcome this problem, two RFI suppression algorithms are proposed based on a modified block sparse Bayesian learning (BSBL). In the first algorithm, the vector to be recovered is divided into several blocks of identical size. The Bayesian hyper-parameters corresponding to the RFI and UTE are learned separately by exploiting the temporal intrablock correlation, and then the nonsparse vector can be successfully recovered. In the second algorithm, the dictionary is adaptively tuned during the iteration process of BSBL, thus reducing the computational load. After recovering the UTE, a well-focused 2-D image can be obtained by traditional SAR imaging algorithms. With the use of properties of both the RFI and UTE, and by exploiting the intrablock correlation, the proposed RFI suppression methods outperform other sparse recovery methods, especially when the range profile is nonsparse. Simulation results demonstrate the superior performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Multiview synthetic aperture radar (SAR) images could provide much richer information for automatic target recognition (ATR) than from a single-view image. It is desirable to find optimal SAR platform flight paths and acquire a sequence of SAR images from appropriate views, so that multiview SAR ATR can be carried out accurately and efficiently. In this paper, a novel optimization framework for multiview SAR ATR is proposed and implemented. The geometry of the multiview SAR ATR is modeled according to the recognition mission and flight environment. Then, the multiview SAR ATR is abstracted and transformed into a constrained multiobjective optimization problem with objective functions considering the tradeoffs between recognition performance and efficiency and security. A specific approach based on convolutional neural network ensemble and constrained nondominated sorting genetic algorithm II is employed to solve the multiobjective optimization, and optimal flight paths and corresponding imaging viewpoints are obtained. The SAR sensor can thus choose an applicable flight path to acquire the multiview SAR images from different tradeoff solutions according to application requirements. Finally, accurate recognition results can be obtained based on those multiview SAR images. Extensive experiments have shown the validity and superiority of the proposed optimization framework of multiview SAR ATR. [ABSTRACT FROM AUTHOR]

The micromotion feature extraction method based on track-before-detect (TBD) can save the radar resource and improve the real-time performance of micromotion feature extraction by implementing target detecting, tracking, and micromotion feature extraction simultaneously. Usually, multitargets will exist in different areas, and the limited radar resources should be allocated for different areas to achieve the maximal performance of radar. For single-beam phased array radar, an adaptive resource allocation optimization model is established according to the processing steps of the micromotion feature extraction method based on TBD, and an adaptive resource allocation strategy is proposed. With the method, the radar efficiency can be significantly improved. The effectiveness of the proposed method is demonstrated by simulations. [ABSTRACT FROM AUTHOR]

It is a feasible and promising way to utilize deep neural networks to learn and extract valuable features from synthetic aperture radar (SAR) images for SAR automatic target recognition (ATR). However, it is too difficult to effectively train the deep neural networks with limited raw SAR images. In this paper, we propose a new approach to do SAR ATR, in which a multiview deep learning framework was employed. Based on the multiview SAR ATR pattern, we first present a flexible mean to generate adequate multiview SAR data, which can guarantee a large amount of inputs for network training without needing many raw SAR images. Then, a unique deep convolutional neural network containing a parallel network topology with multiple inputs is adopted. The features of input SAR images from different views will be learned by the proposed network layer by layer; meanwhile, the learned features from the distinct views are fused in different layers progressively. Therefore, the proposed framework is able to achieve a superior recognition performance, and requires only a small number of raw SAR images for network training samples generation. Experimental results have shown the superiority of the proposed framework based on the Moving and Stationary Target Acquisition and Recognition data set. [ABSTRACT FROM AUTHOR]

The micromotion feature of space target provides an effective approach for target recognition. The existing micromotion feature extraction is implemented after target detection and tracking; thus the radar resources need to be allocated for target detection, tracking, and feature extraction, successively. If the feature extraction can be implemented by utilizing the target detecting and tracking pulses, the radar efficiency can be improved. In this paper, by establishing a feedback loop between micromotion feature extraction and track-before-detect (TBD) of target, a novel feature extraction method for space target is proposed. The TBD technology is utilized to obtain the range-slow-time curves of target scatterers. Then, micromotion feature parameters are estimated from the acquired curve information. In return, the state transition set of TBD is updated adaptively according to these extracted feature parameters. As a result, the micromotion feature parameters of space target can be extracted concurrently with implementing the target detecting and tracking. Simulation results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a waveguide-stripline series–corporate hybrid feed technique to ease the feed-network design for dual-polarized antenna arrays. The hybrid feed network consists of a stripline series feed network and a waveguide-stripline corporate feed network, incorporating both of their advantages in one application. Furthermore, the design can efficiently simplify the manufacturing and packaging process without any bonding postprocess. The proposed technique is realized on an $8 \times 8$ dual-polarized antenna array, and the expected performances are verified by measurement. Experimental results show that the antenna array can operate in a wide bandwidth of 13.2% with more than 40 dB isolation for dual polarization. Cross polarizations at the center frequency of 15.2 GHz are below −27 dB, and the achievable aperture efficiency is 80% for both the polarizations. This hybrid feed approach can be considered as a promising solution for efficient feed-network applications. [ABSTRACT FROM PUBLISHER]

A three-dimensional inverse synthetic aperture radar (ISAR) image can provide the target?s information in range, cross-range, and height directions. Because the existing three-dimensional ISAR imaging methods require changing the observing angle, they are highly dependent on radar hardware complexity or target maneuvering movement. In this paper, a new three-dimensional ISAR imaging method based on shipborne radar is proposed. Instead of utilizing multiple radar receivers or uncontrollable target movement, we take advantage of the ship?s known yaw, pitch, and roll rotations, and obtain different observing angles using a single receiver fixed on the ship. To extract the scatterers, a new algorithm called REACT that combines RELAX and adaptive chirplet transform is presented. Subsequently, the errors of the three-dimensional imaging method are analyzed by partial derivative deduction and Monte Carlo simulation. On the basis of the error analysis, a novel best imaging time selecting method based on image quality function that considers both accuracy and resolution is also put forward. Simulation results validate our theoretical analysis and show the effectiveness of our method. [ABSTRACT FROM AUTHOR]

A wideband dual-polarized and dual-monopulse array with highly self-compact configuration is proposed for synthetic aperture radar (SAR) applications. To investigate the monopulse performances, a monopulse comparator network integrated with a 16 $\times$ 32 antenna array is designed to form a complete monopulse array. In order to realize the complicated monopulse array and avoid the disadvantages from conventional machining methods in manufacturing waveguide structures, a 3-D metal-direct-printing technique is adopted in this work to obtain better mechanical and electrical performances. Experimental results show that the monopulse array can operate in a wide bandwidth of 12.5% in the Ku-band. Port isolation and polarization isolation better than 30 and 45 dB are achieved, respectively. Furthermore, satisfactory monopulse performances in sum- and difference-patterns are also obtained in the wideband. In particular, a high efficiency of 95% is achieved at the center frequency of the sum-patterns, validating the design feasibility and merit of the 3-D printing technique in SAR applications. [ABSTRACT FROM PUBLISHER]

A wideband bandpass filter (BPF) with two cascaded sections, each consisting of a surface acoustic wave (SAW) resonator and one or two transmission lines in parallel, is proposed. The lower and upper rolloffs of the passband are separately controlled by the narrowband characteristics of the SAW resonators to realize high frequency selectivity. The filter principle is analyzed for the two sections using the modified Butterworth-Van Dyke model for SAW resonators. The effects of cascading the two sections using a transmission line are also discussed. To demonstrate the proposed filter topology, two types of resonators are chosen from a limited set of available SAW resonator chips. The filter is designed and simulated based on the data of on-wafer measurements of the chip resonators. It is shown that the wideband behavior of the SAW resonators has impact on the filter. A prototype BPF centered at 2.02 GHz is implemented on PCB combining the discrete chip SAW resonators and microstrip lines. The chips are assembled using 1-mil wire bond technique. The measured filter has a 3-dB fractional bandwidth of 28.8% with high frequency selectivity. [ABSTRACT FROM PUBLISHER]

Bistatic inverse synthetic aperture radar (ISAR) is an efficient geometric configuration of ISAR to image targets traveling along radar transmitter's line of sight, but it has the problem of defocusing and distortion because of the time-varying bistatic angle. In this paper, we derive the defocusing term and the distortion term by expanding the bistatic angle using first-order Taylor expansion. In addition, necessary constraints to neglect the defocusing term are provided via point spread function analysis, and a novel method to eliminate distortion based on linked scatterers is proposed. Simulation results validate our theoretical analysis and show the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Shipborne bistatic inverse synthetic aperture radar (ISAR), with a stationary and visible transmitter on land and a mobile and stealthy receiver on a ship with complex 3-D rotations is a strategic asset which can provide complementary information to monostatic ISAR. However, it must first overcome the serious problems of defocusing and distortion due to the time-varying bistatic angle. In this letter, an improved cubic chirplet decomposition method is proposed to solve the defocusing problem. By decomposing the ISAR echo into cubic chirplet atoms and estimating the parameters for each term, higher-order defocusing terms can be eliminated. Moreover, a novel method based on linked scatterers is put forward to solve the distortion problem. The method utilizes ‘linked scatterers’ from monostatic ISAR image to calculate and compensate the distortion angle. The effectiveness of the proposed methods is verified by the simulation results. [ABSTRACT FROM PUBLISHER]

Conventional inverse synthetic aperture radar observes the target from one viewing direction and only obtains partial information. By using a distributed multiple-input multiple-output (MIMO) array, the target can be observed from multiple views and a better image can be obtained. The distributed MIMO radar imaging signal model is derived in this paper. Because the strong scatterers (scattering centers) of a target are usually sparsely distributed, a sparse signal recovery algorithm using homotopy between the ℓ1 norm and ℓ0 norm applicable to complex signals is proposed to recover the strong scatterers. This ℓ1 norm and ℓ0 norm homotopy method is then extended to the block sparse signal case. The equivalence between complex ℓ1 ℓ0 norms homotopy method and block size of two real ℓ1 ℓ0 norms homotopy method is demonstrated. This proves that a complex signal-based algorithm is better than a real signal-based algorithm, which only separates the complex signal into its real part and imagery part and does not use their block property. This method is compared with other methods, i.e., block orthogonal matching pursuit (BOMP), block CoSaMp, block smoothed ℓ0 norm based method (BSL0), spectral projected gradient (SPG L1), and block sparse Bayesian learning (BSBL). The signal recovery performance of ℓ1 ℓ0 norms homotopy method is better than the other methods except for BSBL. However, BSBL costs more computationally. Imaging using distributed MIMO antennas is simulated. The simulation results show that the image quality using distributed MIMO radar is better than that using monostatic (or bistatic) MIMO radar. [ABSTRACT FROM AUTHOR]

Compressed sensing (CS) has been successfully applied to inverse synthetic aperture radar (ISAR) imaging of moving targets. In existing CS-based ISAR imaging algorithms, Laplace distribution is widely adopted to enforce sparseness on signal recovery. However, this kind of CS method using Laplace prior encounters the problems of determining optimum regularization factor and heavy computation load. In this paper, a fast marginalized sparse Bayesian learning (MSBL) method is proposed for three-dimensional (3-D) interferometric super-resolution ISAR imaging. After deriving the target sparsity-driven imaging model, a fast MSBL approach is applied to obtain super-resolution ISAR image, and then a high-quality 3-D view of the target is achieved via the interferometry technique using an ISAR imagery pair. Experiments on simulated and real data are provided to validate the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

ABSTRACT An H-plane wideband rectangular waveguide T-junction divider with an equal-phase and an unequal-power-division is proposed. This T-junction has an intrinsic hallmark of symmetry, namely, the lengths of two output branches being maintained equally with reference to the input port. Numerical analysis results are compared with the measured ones to verify the design idea. This device has a potential application in array feeding network designs where equal-phase and unequal-power division are to be achieved by equal-length feeding lines. © 2015 Wiley Periodicals, Inc. Microwave Opt Technol Lett 57:1216-1219, 2015 [ABSTRACT FROM AUTHOR]

The theory of compressed sensing (CS) states that an unknown sparse signal can be accurately recovered from a limited number of measurements by solving a sparsity-constrained optimization problem. In this paper, we present a new framework of high-resolution bistatic inverse synthetic aperture radar (Bi-ISAR) imaging based on CS. A phase-preserved CS approach for high-range resolution imaging is proposed. The phase of a Bi-ISAR signal can be extracted by constructing a phase-preserved Fourier basis, which is crucial to azimuth processing of Bi-ISAR imaging. After performing CS reconstruction in range, we present an improved version of CS-based cross-range imaging by combining modified Fourier basis and weighting with CS optimization. Simulated data are used to test the robustness of the Bi-ISAR imaging framework with two-dimensional (2-D) CS method. The results show that the framework is capable of accurate reconstruction of Bi-ISAR image in both range and cross-range. [ABSTRACT FROM PUBLISHER]

A low profile slot antenna array is designed to operate over a wide bandwidth with low sidelobe levels. Taylor synthesis is used to taper the power distribution among array aperture. An efficient approach to design an equal-phase but unequal-power symmetric waveguide divider is proposed for constructing an amplitude-tapering feed-network for the array. Phase differences are balanced by adjusting the waveguide phase velocities. The basic radiator is a 2\,\times\,2 slot subarray, uniformly fed by such a waveguide divider. A 16\,\times\,16 array is then constructed by 8\,\times\,8 of such subarrays. A 1-to-64 way corporate-feed waveguide network is designed to excite all subarrays instead of individual slots, and the required power distribution is obtained by adopting a 30 dB Taylor \overline N=4 synthesis. Measured results indicate that the array can achieve a 13.8% bandwidth and a gain of more than 29.5 dBi. The first sidelobe level is -26.5~dB in the E-plane and -30.4~dB in the H-plane. The holistic sidelobe levels in both planes are better than -25~dB with a better than -40~dB cross-polarization. [ABSTRACT FROM PUBLISHER]

Supersized darkness in three dimensions surrounded by all light in free space is demonstrated theoretically and experimentally in the visible regime. The object staying in the darkness is similar to staying in an empty light capsule because light just bypasses it by resorting to destructive interference. A binary-optical system is designed and fabricated based on achieving antiresolution (AR), by which electromagnetic energy flux avoids and bends smoothly around a nearly perfect darkness region. AR remains an unexplored topic hitherto, in contrast to the super-resolution for realizing high spatial resolution. This novel scheme replies on smearing out the point spread function and thus poses less stringent limitations upon the object's size and position since the created dark (zero-field) area reach 8 orders of magnitude larger than λ2 in cross-sectional size. It functions very well with arbitrarily polarized beams in three dimensions, which is also frequency scalable in the whole electromagnetic spectrum. [ABSTRACT FROM AUTHOR]

Micro-motion features are very useful for target recognition in inverse synthetic aperture radar (ISAR). In this paper, we propose an effective algorithm for micro-motion feature extraction in ISAR imaging with sparse aperture. The micro-Doppler effect induced by rotational micro-motion in ISAR with sparse aperture is analysed first, and then an improved four-parameter Hough transform and an Orthogonal matching pursuit decomposition method are proposed to extract the micro-motion parameters, such as rotation rate and radius. Simulations are given to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

A new concept of using a stacked phased coil array to increase the signal-to-circuit noise ratio (SCNR) in magnetic resonance imaging (MRI) is introduced. Unlike conventional phased coil arrays, the proposed stacked phased coil array is constructed by stacking the coil elements closely together in the vertical direction. Through a proper combination of the coil terminal voltages, the SCNR is shown to increase with the square root of the number of coil elements. A prototype two-element array is constructed and an experimental method is designed to determine the combiner coefficients in a simulated MRI electromagnetic field environment. The experimental results show that the mutual coupling effect among the array coils can be totally removed and the combiner output voltage increases with the number of coil elements. This demonstrates the feasibility of the proposed method. [ABSTRACT FROM PUBLISHER]

Bistatic inverse synthetic aperture radar (BiISAR) imaging provides complementary information to monostatic ISAR imaging. A suggestion that the conventional bistatic ISAR can be replaced by an equivalent monostatic radar is shown to be inadequate in this paper. We prove that the BiISAR image is a sheared version of the projection of the target on the range-Doppler plane. A bistatic interferometric radar configuration is proposed to correct this distortion and to form 3-D image. Simulation results have validated our analysis and shown the viability of the proposed 3-D imaging algorithm. [ABSTRACT FROM PUBLISHER]

A conventional inverse synthetic aperture radar image is a 2-D range-Doppler projection of a target and does not provide 3-D information. Three-dimensional imaging using an interferometric technique cannot separate scatterers that have been projected onto the same range-Doppler unit. Multiple-input–multiple-output (MIMO) radar, however, in addition to having a wide virtual aperture and a high cross-range resolution, could also obtain a target's 3-D image in one snapshot. We discuss in this paper the use of time-domain information to improve MIMO radar's imaging quality. A 3-D image alignment algorithm and a rotation vector estimation method are discussed. Simulation results show that image SNR is improved and cross-range sidelobes are mitigated. [ABSTRACT FROM PUBLISHER]

Conventional inverse synthetic aperture radar image is a 2-D range-Doppler projection of a target and does not provide 3-D information. Its formation also requires complex motion compensation when the target is uncooperative and maneuvering. On the other hand, multiple-input and multiple-output (MIMO) radar, in addition to having a wide virtual aperture and high cross-range resolution, could also obtain a target's 3-D image in one snapshot and thus have avoided the complex motion compensation needed. In this paper, we propose a 3-D imaging algorithm using three MIMO configurations. The signal model is derived based on a modified zero correlation zone code. A strong scatterer selection criterion is also proposed for the construction of the target profile. [ABSTRACT FROM PUBLISHER]

A bistatic range migration algorithm (RMA) based on an exact analytical bistatic point-target (PT) spectrum in the tandem configuration is proposed in this letter. For the conventional geometry-based bistatic formula method, the derived spectrum is only a quasi-analytical one because a variable called half-quasi-bistatic angle (HQBA) is not exactly analytically expressed. The key step of the proposed algorithm is to deduce an analytical HQBA in the tandem configuration, and thus, an exact analytical closed-form PT spectrum is acquired. Based on this analytical spectrum, a bistatic RMA is presented. It is demonstrated that this algorithm can handle the bistatic tandem configuration with extremely large baseline/range ratio and can also be applied to wide-swath imaging. [ABSTRACT FROM PUBLISHER]

Large 2-D sparse array provides high angular resolution microwave images but artifacts are also induced by the high sidelobes of the beam pattern, thus, limiting its dynamic range. CLEAN technique has been used in the literature to extract strong scatterers for use in subsequent signal cancelation (artifacts removal). However, the performance of DFT parameters estimation based CLEAN algorithm for the estimation of the signal amplitudes is known to be poor, and this affects the signal cancelation. In this paper, DFT is used only to provide the initial estimates, and the maximum likelihood parameters estimation method with steepest descent implementation is then used to improve the precision of the calculated scatterers positions and amplitudes. Time domain information is also used to reduce the sidelobe levels. As a result, clear, artifact-free images could be obtained. The effects of multiple reflections and rotation speed estimation error are also discussed. The proposed method has been verified using numerical simulations and it has been shown to be effective. [ABSTRACT FROM AUTHOR]