In high-resolution and wide-swath synthetic aperture radar (SAR) systems, there are azimuth Doppler ambiguity and ghost ground moving targets (GMTs). Incorporated with an improved equivalent range model (IERM), this article explores a simultaneous scene imaging and GMT indication (GMTI) scheme with multiple beamforming for hypersonic vehicle-borne dive-trajectory high-squint multichannel SAR (HSV-DTHS-MC-SAR). First, the coarse-focusing imageries are recovered. Then, the CTV estimation with the dynamic-eigenspace-based adaptive sum and difference beams (EASDBs) is explored. After that, the SAR scene processing which enjoys “customized” beamforming, minimizes the ghost GMT and reconstructs the ambiguity-free scene image. Moreover, the proposed clutter suppression method generates the beamformer center in the desired GMT direction and reduces the beamformer sidelobe by using quadratic pattern constraints. Finally, the geometry and inverse projections are performed to calibrate the distortion arising from vertical velocity. Compared to the existing methods, the proposed scheme has innovations or improvements in CTV estimation, scene recovery, and cutter suppression. To be specific, it avoids the cumbersome CTV search and thus has lower computation, enjoys more concise and robust removal of ghost GMT in the scene, and frees the steering vector mismatch plus achieves a higher signal-to-clutter-and-noise ratio (SCNR). The extensive simulations confirm the effectiveness of our proposed scheme.

Radar jamming recognition aims to accurately recognize the type of jamming to provide guidance for radar countermeasures. Although previous deep learning-based methods have made promising performance, they mainly rely on convolutional neural network (CNN) based on local processing, which ignore the global information in the time-frequency domain data of the jamming signal and also require much inference time to obtain the final recognition results. In this paper, a novel few-shot jamming recognition network via time-frequency self-attention and global knowledge distillation (JR-TFSAD) is proposed by jointly considering the global information in the time-frequency spectrum of the jamming signal and the real-time performance of the recognition network. A time-frequency self-attention model (TFSA) is proposed to extract the global deep features of radar jamming signals by learning the correlation between two arbitrary points in the time-frequency spectrum of the jamming signal, thus improving the recognition accuracy. Moreover, to effectively reduce the inference time of the method while preserving the recognition accuracy, a global knowledge distillation model (GKD) is further constructed to perform jamming recognition by distilling the global knowledge from the TFSA model. The experimental results on the simulated and measured mixed dataset verify that the proposed method has higher recognition accuracy and shorter inference time compared to the state-of-the-art methods.

This article explores the coherent integration and parameter estimation problem for high-speed target detection using bistatic multiple-input multiple-output (MIMO) radar. Unlike the traditional intrachannel coherent integration that could only focus the target’s energy during the multipulse observation, the bistatic MIMO radar implements joint intrachannel integration and interchannel integration to improve detection performance. However, its difficulties lie in that the high-speed motion results in the range migration (RM) in the intrachannel and the phase and envelope differences among multichannel. To solve these problems, we propose a multichannel coherent integration methodology in the radon Fourier transform (RFT) domain for the bistatic MIMO radar. First, we apply the RFT to integrate the target energy in the intrachannel. Then, the multichannel coherent accumulation is performed by the design of the phase compensation function and envelope alignment function. Finally, based on the relationship between the integration peak’s location and target parameters, a Broyden-like method and linear equation solving are, respectively, presented to estimate the target’s position and velocity. The effectiveness of the proposed method is assessed via simulation experiments and real-measured data. It is further noted that the proposed method can detect targets at low signal-to-noise ratios (SNRs) which are not attainable using state-of-the-art methods.

Due to the flexible configuration and maneuvering platform, bistatic synthetic aperture radar (SAR) plays an important role in modern remote sensing applications, but the non-ideal track simultaneously introduces model mismatch and spatial-variant phase problems. This paper proposes a sub-aperture parametric polar format algorithm (PFA) for high-resolution bistatic spotlight SAR imaging. First, a bistatic parametric polar format algorithm is proposed to focus on the generally configured bistatic SAR data. A high-precision range model in the bistatic range and ellipsoid parameter angle coordinate space is established to modify the PFA interpolation kernels. To further enhance the azimuth resolution, the PFA sub-images are fused in the image domain based on the coordinate transformation between the unified Cartesian coordinates and local imaging polar coordinates. During the sub-image fusion, in order to ensure the accurate projection and non-aliasing spectrum, we also consider the geometric deformation of the coarse PFA image and analyze the wavenumber support region along with the Nyquist sampling requirement. This novel sub-aperture method, which is theoretically more efficient than the fast back-projection algorithm, alleviates the limitation of full aperture resolution on PFA’s depth of focus. Finally, our method is applied to the general bistatic spotlight SAR data, involving the level-flight airborne transmitter and the dive hypersonic vehicle-borne receiver, and the results of both points and distributed targets demonstrate its effectiveness and efficiency.

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.

This letter proposes a modified polar format algorithm (PFA) applied to the highly squinted synthetic aperture radar (SAR) onboard an accelerating hypersonic platform. In conventional PFA, the range wavenumber is orthogonally decomposed to obtain the 2-D wavenumbers corresponding to the imaging Cartesian coordinates. This operation, however, is inadequate when facing with more complex motion trajectory. In this letter, the essence of the proposed method is to homogenize the spatially nonuniformly sampled echoes by combining interpolation with the generalized wavenumber definition. The imaging polar coordinate system in the slant plane provides new space-wavenumber Fourier transform pair, and the 1-D interpolation compensates for the range-variant phase errors. The proposed algorithm, thus, enables fast and effective imaging of hypersonic SAR in large squint angles and high maneuverability dive mode. Its performance is discussed and analyzed in this letter, including the resolution and the size of the imaging scene. The effectiveness, superiority, and application value of the proposed algorithm are verified by simulation.

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.

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.

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.

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.

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.

In this paper, we present an algorithm for curvilinear Video-SAR (CViSAR) persistent imaging with distortion correction. Firstly, we derived the mapping between the actual and reconstructed location of any target in any frame image formed by curvilinear persistent polar format algorithm, named as curvilinear persistent mapping (CPM). Then, through a combination of the non-uniform input wavenumber spectrum with the non-uniform output imaging grids calculated by CPM, distortion-free frame images can be formed directly via a single non-uniform fast Fourier transform of type 3 (NuFFT-3). Simulation experiments are provided to demonstrate the effectiveness of the proposed algorithm.

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.

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.

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.

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.

In this letter, we explore the concept of group sparse representation (GSR) to exploit the intrinsic structure of synthetic aperture radar (SAR) image. Noting that dictionary design is a crucial factor in GSR performance, we propose an over-complete dictionary to better fit the SAR image despeckling problem. This over-complete dictionary consists of the prespecified dictionaries and learned dictionary. Different kinds of dictionaries emulate the image from different angles. In this way, we can simultaneously obtain better performance on speckle noise suppression and image detail preservation. The experimental results on real SAR images demonstrate that the proposed over-complete dictionary based on GSR can achieve more effective speckle reduction as well as image detail preservation.

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 × 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 sumand 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.

Geosynchronous (GEO) SAR is a radar satellite running in the inclinational geosynchronous orbit (36000km). During the long signal propagation delay time, the relative motion between satellite and target cannot be ignored. In this paper, the two-way slant range is analytically obtained from vector derivation. Meanwhile, it is found that the image formation must consider the non-uniform velocity in a long synthetic aperture time, and therefore, based on the derived slant range and the instantaneous velocity of satellite, a modified back projection algorithm is put forward to overcome the error of the “stop-and-go” error as well as the effects of the non-uniform satellite velocity. Simulation results validate the correctness of the improved imaging algorithm.

Geosynchronous synthetic aperture radar (GEO SAR) data focusing is a more challenging and difficult task than the low earth orbit (LEO) SAR due to the strong 2-D coupling of echo signal induced by the orbital trajectory curvature. The range cell migration (RCM) in the GEO SAR configuration is space variant in both range and azimuth directions, hence standard RCM correction (RCMC) functions developed for LEO SAR are inadequate for GEO. In this letter, a curved trajectory model is proposed, taking into consideration the impacts of “stop-and-go” assumption. Based on the range model, a new data transform is derived to deal with the complicated coupling in GEO SAR. From the derivation, we find that the original Stolt mapping is a special case of the proposed “generalized Omega-k” algorithm. In comparison with the original Omega-k algorithm, this new algorithm can correct more complicated RCM effectively. Finally, simulation results show that the proposed imaging algorithm performs well for large scene focusing in an L-Band GEO SAR system.

In inverse synthetic aperture radar (ISAR) imaging, a target is usually regarded as consist of a few strong (specular) scatterers and the distribution of these strong scatterers is sparse in the imaging volume. In this paper, we propose to incorporate the sparse signal recovery method in 3D multiple-input multiple-output radar imaging algorithm. Sequential order one negative exponential (SOONE) function, which forms homotopy between 1 and 0 norms, is proposed to measure the sparsity. Gradient projection is used to solve a constrained nonconvex SOONE function minimization problem and recover the sparse signal. However, while the gradient projection method is computationally simple, it is not robust when a matrix in the algorithm is ill conditioned. We thus further propose using diagonal loading and singular value decomposition methods to improve the robustness of the algorithm. In order to handle targets with large flat surfaces, a combined amplitude and total-variation objective function is also proposed to regularize the shapes of the flat surfaces. Simulation results show that the proposed gradient projection of SOONE function method is better than orthogonal matching pursuit, CoSaMp, l1-magic, Bayesian method with Laplace prior, smoothed l0 method, and l1-ls in high SNR cases for recovery of ± 1 random spikes sparse signal. The quality of the simulated 3D images and real data ISAR images obtained using the new method is better than that of the conventional correlation method and minimum l2 norm method, and competitive to the aforementioned sparse signal recovery algorithms.

A multilayered surface coil array for magnetic resonance imaging with an improved signal-to-noise ratio (SNR) performance is introduced and investigated by a simulation study. By using an effective decoupling method, the strong mutual coupling effect between the coil layers can be accurately removed, leading to a coherent combination of the signals of the individual coils. This results in a much stronger received signal power which increases with the number of coil layers in the array. This, together with a smaller rate of increase of noise power with the number of coil layers, leads to a net increase in the SNR of array output with the number of coil layers in the array. Rigorous numerical simulation examples have been carried out to confirm and verify the performance of the new array.

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.

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.

A new method is introduced to increase the signal-to-noise ratio (SNR) in low-field magnetic resonance imaging (MRI) systems by using a vertically stacked phased coil array. It is shown theoretically that the SNR is increased with the square root of the number of coils in the array if the array signals are properly combined to remove the mutual coupling effect. Based on this, a number of vertically stacked phased coil arrays have been designed and characterized by a numerical simulation method. The performance of these arrays confirms the significant increase of SNR by increasing the number of coils in the arrays. This provides a simple and efficient method to improve the SNR for low-field MRI systems.

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.

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.

A novel approach to motion parameter estimation with low pulse repetition frequency (PRF) sampling based on compressed sensing (CS) theory is introduced. As is known to us, when PRF is less than the Doppler spectrum bandwidth, moving targets suffer both Doppler centroid frequency ambiguity and Doppler spectrum ambiguity. Under this condition, the traditional parameter estimation method in the Doppler domain is out of action. The key of this letter converts motion parameter estimation in the synthetic aperture radar system with low PRF sampling into solving an optimization equation based on CS theory. Because moving targets in the scene can be regarded as sparse signals after clutter cancellation, an optimization algorithm based on CS theory is proposed to reconstruct sparse signals and meanwhile estimate the along-track velocities and azimuth positions of moving targets. Considering the fact that range cell migration of moving targets is not subject to PRF limitations, Radon transform is adopted to obtain unambiguous across-track velocities and range positions. Results on simulation and real data are provided to show the effectiveness of this method.

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.

Azimuth ambiguity occurs in synthetic aperture radar (SAR) systems due to the well-known constraint of minimum antenna area, particularly at high resolutions and wide swaths. A space time domain method can be utilized to remove this ambiguity if the multiple-channel data are available. In this letter, a modified approach is presented to determine the filter weight vectors. This approach was successfully applied to the real data, which were collected by an experimental airborne multiple-channel SAR system. The channel imbalance and the error in antenna phase center position are analyzed in detail.

Conventional antenna array 3-D inverse synthetic aperture radar (ISAR) imaging method does imaging by first carrying out ISAR imaging for all the different antennas and then using array direction of arrival (DOA) estimation on every scatterer to get the position of that scatterer. The aperture formed by the target's motion is used to separate the scatterers, and the antenna array aperture is used for position measurement. In this letter, a 3-D imaging method based on a combination of sparse array beamforming and ISAR imaging is proposed, where 3-D images are formed by space-time match filtering. The estimation of the target's rotation parameters is also given. Simulation results have shown the effectiveness of the proposed methods.

The performance of high-frequency surface wave radar (HFSWR) is known to suffer from external environmental interference and noise, such as cochannel radio-frequency interference from other radiating source, ionospheric clutter, lightning impulsive noise, etc. This paper experimentally evaluates the interference cancellation performance of various adaptive beamforming schemes with respect to the aforementioned three types of interferences in an attempt to find the most promising adaptive cancellation scheme in practical HFSWR environment.

In this paper, a 3D inverse synthetic aperture radar (ISAR) imaging method based on an antenna array configuration is proposed. The performance of conventional interferometric ISAR imaging system using three antennas is poor, as the positions of scatterers, which have the same range-Doppler value and projected onto the ISAR plane as a synthesis scatterer, cannot be correctly estimated. However, by using two antenna arrays perpendicular to each other, the system's ability to separate these scatterers can be improved. The criterion for the selection of a range unit that contains an isolated scatterer in the 2-D array domain for doing motion compensation is discussed. If there is no range unit which contains only an isolated scatterer, then radial and cross-range motion compensation has to be carried out by motion parameter estimation. Two cross-range motion parameters measurement algorithms, one based on array processing of the range profile and another based on correlation of ISAR images of different antennas, are proposed. The coordinates registration problem for the scatterers of a synthesis scatterer is also discussed. Simulation results have shown the effectiveness of the proposed methods.

The rotation of structures in a target introduces additional frequency modulations on the returned signals and also generates sidebands about the center Doppler frequency of the target. In other words, the body image will be contaminated due to the interference from the rotating parts. In this paper, an imaging method for moving targets with rotating parts is presented. The method is simple to implement and is based on the Hough transform (HT), which is widely used in image processing. Using the standard HT and an extended HT, we put forward a separation method by detecting the straight lines and the sinusoids on the spectrogram, respectively. A computer simulation is given to illustrate the effectiveness of the proposed method.

Conventional envelope alignment method of ISAR imaging assumes that the target is a rigid body, and therefore, the correlation between one-dimensional range profiles at neighboring time slots gives a larger value compared to correlation between time separated range profiles. But this is not the case for targets such as helicopter which have rotating body parts. In this paper, an envelope alignment algorithm for helicopter target ISAR imaging is proposed. For every range profile, correlation with other range profiles are done, and the range profiles with larger correlation value are used to provide an initial estimate of the target motion parameters by polynomial fitting. Then the estimated parameters are averaged to get a better estimation. The position of the rotor signals are obtained by taking the difference of the range profiles in the slow time domain. The rotor signals are then suppressed by zero-force windowing technique. Simulation results have shown the effectiveness of this algorithm.

In ISAR imaging, most works have assumed the target to be a rigid body, a body without any rotating, vibrating or moving parts. The rotation of structures in a target, such as the rotor of a helicopter or the turret of a tank target, may induce frequency modulation on the returned signals and generate sidebands about the center frequency of the target’s body doppler frequency, known as the micro-doppler phenomenon. In this paper we present a new method based on range grouping of the target’s range profile for the separation of the rotating or moving parts from the target’s body. The method is carried out on the Range Profile Image, different from other methods that do the separation at the signal level.

The performance of over-the-horizon radars (OTHRs) degrades significantly due to transient interferences such as lightning, meteor echoes, and man-made impulse bursts. The conventional techniques used to suppress these transient interferences generally follow a simple time-domain procedure which first localizes the transient, sets the corrupted time samples to zero, and follows by data reconstruction via interpolations. However, for an OTHR operation with a short coherent integration time (CIT) of a few seconds, the transient interference may corrupt a significant portion of the received data such that data interpolation can become erroneous. In this paper, a new transient interference excision technique is proposed. It utilizes the adaptive time-frequency analysis technique to parameterize the radar signal. The transient interference can then be identified and subsequently removed via their characteristic parameters. No data interpolation is needed. The performance of the proposed technique is illustrated by simulated and experimental OTHR data.

Based on the gliding-box algorithm and the Radon transform algorithm, a novel method for detecting ship wakes in synthetic aperture radar (SAR) images is proposed. The method is applied to both simulation images and real SAR image, and the result shows that the detection accuracy is satisfactory in strong noise environment. One significant feature of the new algorithm is that it can even detect ship wakes which are significantly shorter than the image dimensions

In this paper, a three-dimensional (3-D) interferometric synthetic aperture radar (ISAR) imaging method for moving targets is presented. This imaging method is based on the ISAR principle and the simple observation that all scatterers on a moving target move in tandem. The angular motion parameters in the cross-range directions could be estimated using the overall range profile of the moving target. Registration of the respective complex images at the two (or more) interferometric antennas can then be achieved via compensating the respective echoes at the raw data level, thus avoiding phase-unwrapping processing and image-resampling processing as required by conventional methods. Finally, a 3-D image of the moving target can then be reconstructed from the 3-D spatial coordinates of these scatterers. Furthermore, the method works well even for a target moving in heavily cluttered environments.

In most interferometric inverse synthetic aperture radar (InISAR) systems, the pixels between two ISAR images derived from corresponding antennas usually do not register properly. As such, correct phase difference between two ISAR images could not be obtained. A three-dimensional (3-D) motion compensation method, or 3-D focusing, is put forward in this paper. With a multiple antenna pair configuration, the angular motion parameters both in the azimuth and pitching directions are accurately estimated without the usual phase-unwrapping processing. As such, there is no phase ambiguity in the inteferometric systems. The angle motion trajectory measurement here is based on the range profiles (or spatial spectrum) so that the "angle scintillation" phenomenon can be effectively suppressed. The angular motion trajectory is then obtained by curve fitting of the spatial spectrum (range profile in the cross-range direction). The compensated ISAR images are registered accurately. Finally, the simulation data are used to illustrate the accuracy of the proposed method.

In InISAR system, the pixels between two ISAR images derived from corresponding antennas usually do not register properly without prior compensation. A three-dimension motion compensation method, or 3D focusing, is put forward in this paper. While the multi-antenna-pair configuration in radar system, the angular motion parameters both in the azimuth and pitching are estimated accurately and the phase unwrapping processing can be avoided in the procedure of obtaining phase unambiguous. Simulation data is used to illustrate the accuracy of the proposed method. The method has also been extended to stripmap SAR for the interferometric 3D imaging of moving and/or man-made objects.

The range migration algorithm (RMA) is an accurate algorithm to process spotlight synthetic aperture radar (SAR) data. However, in applying the RMA to process the SAR data, especially for short-dwell data, the data length has to be extended in azimuth by zero-padding it to the length of the azimuth filter. On the other hand, the spectral analysis (SPECAN, which is a deramp followed by a fast Fourier transform) algorithm is efficient, since the data extension is unnecessary, but an assumption is made that all targets have the same FM rate and the same range cell migration. A novel algorithm is introduced here to combine the accuracy of the RMA and the efficiency of SPECAN. It involves bulk azimuth uncompressing the RMA processed data with a single linear FM filter, and then refocusing the data using SPECAN, all done without the azimuth extension in the RMA. Localized artifacts generated can be removed easily. Point target simulations, including one with a high squint angle of 60/spl deg/, were successfully performed to verify the algorithm.

In this paper, a new easy-to-implement variant of the stepped-frequency signal format is presented for ground clutter cancellation application. A sequence of pulse-groups, where each pulse-group consists of two pulses of the same carrier frequency, is transmitted. The carrier frequency of each subsequent burst is stepped at /spl Delta/f. Simple first-order cancellation of ground clutter can be carried out by comparing the returns of the two adjacent pulses, as the clutter movement (due to wind, etc.) can be assumed negligible within this interval (typically <1 /spl mu/s). A computer simulation is given to illustrate the effectiveness of the proposed method.

In this paper we have presented a new mode of stepped-frequency signal. By using this signal, which transmits two pulses of the same earner frequency at each burst and with this carrier frequency stepped up a /spl Delta/f in each subsequent burst, the ground clutter can be cancelled by using simple first-order cancellation. A computer simulation is given to illustrate the effectiveness cif the proposed method.

For original paper see ibid., vol.36, no.5, pt.1, p.1531-9 (1998). The quality phase-gradient autofocus (QPGA) technique was proposed to speed up the estimation convergence of phase-gradient autofocus by selectively increasing the pool of quality synchronization sources instead of selecting the "brightest" pixels within the image. It is now found that the QPGA, with its inherent scatter "growing" concept and target-filtering procedure, is also able to focus in environments with stationary and moving targets.

Based on the gliding-box and relative differential box-counting algorithms, a novel method that estimates accurately the multifractal exponents, a distinct characteristics of gray-scale digital images, is proposed. Four natural texture images are used to test the performance of the novel multifractal measure. Comparisons with published methods show that the proposed method can efficiently describe texture images and can provide accurate classification results.

Based on the relative differential box-counting algorithm and the gliding-box algorithm, a novel method for estimating the lacunarity features of grayscale digital images is proposed. Four natural texture images are used to test the performance of the novel lacunarity measure. Comparisons with published methods show that the proposed method can efficiently describe texture images, and provide accurate classification results. Real synthetic aperture radar (SAR) images analyses are found to have different lacunarity values for different regions. We show that good results can be obtained with appropriate lacunarity parameters applied to SAR images segmentation.

New applications of nonlinear chirp scaling (NLCS) have been found in processing monostatic and bistatic SAR data acquired in the strip-map mode. When the effects in impulse response broadenings due to quadratic range migration are negligible for short radar wavelengths, a time domain range cell migration correction (RCMC) can be applied. After the correction, the FM rates of targets confined in a range gate will differ from each other. A nonlinear chirp perturbation function can be applied to each range gate to equalize the targets' FM rates before azimuth compression. This algorithm is analyzed in the paper and is supported by point target simulation experiments.

A high squint subaperture (HSS) algorithm was developed from the perspective of a strip map synthetic aperture radar (SAR) system and has the ability to focus SAR data at extremely high squint angle of 55/spl deg/ with less than 1.27% mainlobe expansion or even higher squint angles if larger mainlobe expansion can be tolerated. The unique characteristic of this algorithm is that the subapertures are formed by multiplying the received signal with a set of overlapped complex conjugated reference signals where the chirp rate varies in the azimuth direction. This is done to solve the range focus problem that arises after the azimuth input signal undergoes the range walk removal procedure. As the IISS algorithm only involves short fast Fourier transforms (FFTs) and avoids interpolation, it is computationally efficient and allows for small data buffers and facilitates hardware real-time implementation. The distinct feature of this algorithm is its simplicity of implementation, which is vital in real time processing and motion compensation when the squint angle may be changing continuously.

A new technique for inversed synthetic aperture radar (ISAR) ranging, which resembles the principle of the Vernier measuring system, is presented. In this technique, the transmitted ISAR pulse comprises a train of chirp subpulses with uniformly stepped up center frequencies. The return ISAR echo is first processed, using hardware, to determine a coarse estimate of the target range. Further refinements of the range estimate are achieved through software processing, consisting of two stages of discrete Fourier transform operation. The ranging accuracy can be increased without the need for increased bandwidth, but at the expense of a slight increase in computational complexity. Numerical evaluation shows that a noiseless system is capable of achieving high-ranging accuracy, of the order of millimeters, even in the presence of dispersion and target motion. From computer simulations, the proposed system is also found to be robust against additive system noise and frequency jitter under practical conditions.

Propagation of electromagnetic waves in forest environments is examined in which both the transmitting and receiving points are located in the trunk layer of a four-layered anisotropic forest model. This propagation model considers the forest as a horizontally stratified, anisotropic media of canopy and trunk, bounded by ground below and air above. The electromagnetic fields are obtained using dyadic Green's functions in their eigenfunction expansion forms for an anisotropic four-layered geometry. Analytical results are found for the fields, which consist primarily of three wave modes: a direct wave, multiply reflected waves, and lateral waves. These field constituents are compared, and their domains of preponderance are calculated; it is found that the lateral wave plays a major role in communication at large distances. Radio losses for typical forest are calculated to illustrate numerical application of the forest model.

High-squint angle synthetic aperture radar (SAR) imaging has applications in specific situations such as providing looking angle-dependent scattering information and revisiting an area of interest. It is very difficult for current SAR-imaging algorithms to be used directly in high-squint configurations. This is due to the large range-cell migration and the nonnegligible cubic-phase term in the signal. In this paper, an improved algorithm called the time-varying step-transform algorithm is proposed. It uses different chirp rates to deramp the signal in different subapertures to alleviate the range-focus problem. The interval between subapertures is also varied according to the deramping chirp rate. The nonnegligible cubic-phase term is included in the focusing procedure. The performance of the proposed algorithm in high-squint-angle mode is excellent. By reducing the subaperture length as the squint angle is increased, the performance of the algorithm is almost independent of squint angle. The proposed algorithm is also flexible for multilook processing and motion-error compensation.

A new method of phase error estimation that utilizes the weighted least-squares (WLS) algorithm is presented for synthetic aperture radar (SAR)/inverse SAR (ISAR) autofocus applications. The method does not require that the signal in each range bin be of a certain distribution model, and thus it is robust for many kinds of scene content. The most attractive attribute of the new method is that it can be used to estimate all kinds of phase errors, no matter whether they are of low order, high order, or random. Compared with other methods, the WLS estimation is optimal in the sense that it has the minimum variance of the estimation error. Excellent results have been obtained in autofocusing and imaging experiments on real SAR and ISAR data.

The phase-gradient autofocus (PGA) technique is robust over a wide range of imagery and phase error functions, but the convergence usually requires four-six iterations. It is necessarily iterative in an attempt to converge on a dominant target against clutter interference, while sufficiently capturing the blur function. The authors propose to speed the estimation convergence by selectively increasing the pool of quality synchronization sources and not be limited by the range pixels of the SAR map. This is highly probable since each range bin contains more than one prominent scatterer across the integration aperture. It is also highly probable that the least-brightest selected scatterer in a range gate may turn out to be of higher energy as compared to the maximum brightest scatterer of another gate. With appropriate target filtering to final select the quality scatterers out of the large pool and with higher order phase error measurement tool, the new algorithm achieves near-convergence focusing quality without iteration. The authors named this solution the quality PGA (QPGA) algorithm.