Inverse Synthetic Aperture Radar Imaging Of Targets Research Articles

3D reconstruction of high‐speed moving targets based on HRR measurements

Three-dimensional (3D) target reconstruction from inverse synthetic aperture radar (ISAR) data has a wide application in target scattering modelling, detection, and identification. In ISAR imaging of targets with complex motions such as the non-cooperative manoeuvring targets, the scattering centres on the target may rotate slowly in 3D space during the observation time. In this study, the authors have developed a new formulation for 3D target geometry reconstruction from the scattering centres high-resolution range (HRR) measurements, based on target motion features. First, after the translation compensation, the multi-view HRR of the scattering centres is extracted by HR spectral estimation technique. Then, the multi-view measurements data without correspondence information are associated using the multiple hypotheses tracking algorithm. Finally, the 3D target geometry and motion are reconstructed from the singular value decomposition of the correlated HRR data matrix. The effectiveness of the proposed algorithm is demonstrated by both simulated and real data experiment results.

ISAR imaging of targets with rotating parts based on robust principal component analysis

In inverse synthetic aperture radar (ISAR) imaging, the radar echoes from the rotating parts on a target may contaminate the ISAR image of the main body. The performance of the autofocus algorithms may also be degraded when the interference is too strong, for the commonly used rigid-body motion assumption is not satisfied. In this study, a novel method based on the robust principal component analysis is proposed for ISAR imaging of targets with rotating parts. The low rank property of the main body returns and the sparse property of the rotating components in a certain range cell are utilised to separate them from each other. The separated echoes are then refocused to produce a high-resolution target image and estimate the rotating parameters. Experimental results with both simulated and measured data have demonstrated the effectiveness of the proposed method.

Maneuvering target imaging and scaling by using sparse inverse synthetic aperture

In recent years, there have been increasing interests in addressing the issue of high-resolution inverse synthetic aperture radar (ISAR) imaging from sparse aperture (SA) data. The non-uniform rotation property of maneuvering target introduces non-stationary echo modulation to exhibit two-dimensional (2-D) migration through resolution cells (MTRC), which increases the difficulty of SA-ISAR imaging. In this paper, we focus on ISAR imaging and scaling of maneuvering target for effective MTRC correction and SA synthesis. Under a scaled non-uniform Fourier dictionary to include MTRC, SA maneuvering target imaging is formatted as sparse representation with maximum a posterior (MAP) estimation. Then, sparse ISAR imaging joint with parameter estimation is solved by using a two-step procedure. The estimated rational parameters are used to rescale the reconstructed ISAR image to extract the 2-D target geometry. Finally, the experiments based on simulated and measured data are performed to confirm the effectiveness of the proposed algorithm.

Photonics-based real-time and high-resolution ISAR imaging of non-cooperative target

Real-time and high-resolution imaging is demonstrated based on field trial detection of a non-cooperative target using a photonics-based inverse synthetic aperture radar (ISAR). By photonic generation and de-chirping of broadband linear frequency modulation signals, the radar can achieve a high range resolution thanks to the large instantaneous bandwidth (8 GHz at the K band), as well as real-time ISAR imaging using low-speed analog-to-digital conversion (25 MSa/s). A small-size unmanned aerial vehicle is employed as the non-cooperative target, and ISAR imaging is realized with a resolution far better than those achieved by the previously reported photonics-based ISARs. The capability for real-time ISAR imaging is also verified with an imaging frame rate of 25 fps. These results validate that the photonics-based radar is feasible in practical real-time and high-resolution ISAR imaging applications.

Dynamic ISAR imaging of maneuvering targets based on sparse matrix recovery

For high resolution inverse synthetic aperture radar (ISAR) imaging of maneuvering targets, the Doppler frequency shifts are time varying during the coherent processing interval (CPI). Thus, the conventional range Doppler (RD) ISAR technique does not work properly. By exploiting two-dimensional (2D) sparsity of the target scene, 2D sparse matrix recovery algorithms are applied to achieve super-resolution within a short CPI, during which the Doppler shifts nearly remains constant. Sequential order one negative exponential (SOONE) function is used to measure the sparsity of a 2D signal. A 2D gradient projection (GP) method is developed to solve the SOONE function and thus the 2D-GP-SOONE algorithm is proposed. The algorithm can solve the sparse recovery of 2D signals directly. Then the 2D-GP-SOONE algorithm is used for the dynamic ISAR imaging of maneuvering targets. Theoretical analysis and simulation results show that the proposed method has a lower computational complexity and can achieve the fast recovering of a sparse matrix. Moreover, the proposed method has a better performance in ISAR imaging of maneuvering targets.

Doppler ambiguity removal and ISAR imaging of group targets with sparse decomposition

Low pulse repetition frequency waveforms inherently suffer from the Doppler ambiguity in inverse synthetic aperture radar (ISAR) imaging of group targets. In this study, a novel Doppler ambiguity removal method with sparse decomposition is presented for ISAR imaging of group targets. By solving a sparsity-driven optimisation problem, chirp signals corresponding to scattering centres of group targets are extracted in the azimuth time-domain, and the Doppler ambiguity number of each extracted chirp signal can be estimated simultaneously in the process of sparse decomposition. As a result, unambiguous ISAR image of group targets can be well reconstructed by a sophisticated compensation of migration through range cells and the time-varying Doppler. Extensive simulated experiments confirm the effectiveness of the proposal.

Joint Sparsity-Based ISAR Imaging for Micromotion Targets

The micromotion of a target will introduce additional frequency modulations on the radar echoes and lead to a contaminated inverse synthetic aperture radar (ISAR) image of the main body. In this letter, a novel ISAR imaging method is proposed for micromotion targets based on the joint sparsity of the main body spectrogram. Under the sparse Bayesian framework, we assign hierarchical priors to the spectrogram to statistically promote the joint sparsity of the main body signals and eliminate the interference of the micromotion counterparts. To avoid the intractability of calculating the posterior, we utilize the Markov chain Monte Carlo sampling in the Bayesian inference. Based on the approximate posterior, the main body spectrogram is estimated from the samples, and a clear image of the main body can be achieved by cross compression. Imaging results of the echoes generated using both the point-scattering model and the electromagnetic computation have validated the effectiveness of the proposed method.

High-resolution ISAR imaging of maneuvering targets based on the sparse representation of multiple column-sparse vectors

For inverse synthetic aperture radar (ISAR) imaging of a maneuvering target, the time-variance of Doppler shifts will produce blurred images over a long coherent processing interval (CPI). Compressed sensing (CS) theory indicates that the precise recovery of an unknown sparse signal can be achieved from a very limited number of measurements. A novel sparse reconstruction of multiple column-sparse vectors using an algorithm with a minor reconstruction error and low computational complexity is proposed here based on multiple measurement vectors (MMV) and the smoothed ℓ0 norm (SL0) algorithm. For ISAR imaging of maneuvering targets, the Doppler shifts remain nearly constant for a short CPI. The proposed algorithm can produce high resolution ISAR images of maneuvering targets with an extremely limited number of pulses. Simulation results demonstrate the effectiveness and computational efficiency of the proposed method.

ISAR Imaging of Targets With Complex Motions Based on a Noise-Resistant Parameter Estimation Algorithm Without Nonuniform Axis

In inverse synthetic aperture radar (ISAR) imaging of targets with complex motions, such as highly maneuvering airplanes and ships fluctuating with oceanic waves, azimuth echoes of a range cell have to be modeled as multicomponent cubic phase signals (CPSs) after the range alignment and the phase adjustment. Due to the time-varying Doppler frequencies of scatterers, ISAR image obtained with the standard range-Doppler algorithm is blurred, and the range-instantaneous-Doppler (RID) technique is required to improve the image quality. In this paper, by employing a novel parametric autocorrelation function and the generalized scaled Fourier transform, an effective parameter estimation algorithm is proposed for multicomponent CPSs and applied to reconstruct the RID image for targets with complex motions. Analyses of the implementation, the cross-term, the anti-noise performance, and the computational cost demonstrate that, compared with other three representative estimation algorithms, the proposed algorithm can eliminate the brute-force searching procedure and acquire a higher anti-noise performance without the nonuniform axis. Through simulations and analyses for synthetic models and the real radar data, we verify the effectiveness of the proposed estimation algorithm and the corresponding ISAR imaging algorithm.

Inverse synthetic aperture radar imaging of maneuvering target based on cubic chirps model with time-varying amplitudes

Inverse synthetic aperture radar (ISAR) imaging of maneuvering target is a main topic in the field of radar signal processing, and the received signal in a range bin can usually be characterized as multicomponent cubic chirps with constant amplitudes after motion compensation. In fact, the phenomenon of migration through resolution cell (MTRC) often occurs for the target’s complex motion, and this will induce the time-varying character for the amplitudes of cubic chirps. An algorithm for the parameters estimation of multicomponent cubic chirps with time-varying amplitudes based on the extension form of match Fourier transform is proposed, and by using it in ISAR imaging of maneuvering target, the quality of images can be improved significantly compared with the constant amplitudes model. Results of simulated and real data validate the effectiveness of the algorithm in this paper.

3D Geometry and Motion Estimations of Maneuvering Targets for Interferometric ISAR With Sparse Aperture

In the current scenario of high-resolution inverse synthetic aperture radar (ISAR) imaging, the non-cooperative targets may have strong maneuverability, which tends to cause time-variant Doppler modulation and imaging plane in the echoed data. Furthermore, it is still a challenge to realize ISAR imaging of maneuvering targets from sparse aperture (SA) data. In this paper, we focus on the problem of 3D geometry and motion estimations of maneuvering targets for interferometric ISAR (InISAR) with SA. For a target of uniformly accelerated rotation, the rotational modulation in echo is formulated as chirp sensing code under a chirp-Fourier dictionary to represent the maneuverability. In particular, a joint multi-channel imaging approach is developed to incorporate the multi-channel data and treat the multi-channel ISAR image formation as a joint-sparsity constraint optimization. Then, a modified orthogonal matching pursuit (OMP) algorithm is employed to solve the optimization problem to produce high-resolution range-Doppler (RD) images and chirp parameter estimation. The 3D target geometry and the motion estimations are followed by using the acquired RD images and chirp parameters. Herein, a joint estimation approach of 3D geometry and rotation motion is presented to realize outlier removing and error reduction. In comparison with independent single-channel processing, the proposed joint multi-channel imaging approach performs better in 2D imaging, 3D imaging, and motion estimation. Finally, experiments using both simulated and measured data are performed to confirm the effectiveness of the proposed algorithm.

Inverse synthetic aperture radar imaging of targets with complex motions based on modified chirp rate–quadratic chirp rate distribution for cubic phase signal

For targets with complex motions, the time-varying Doppler frequency deteriorates inverse synthetic aperture radar (ISAR) images. After range alignment and phase adjustment, azimuth echoes in a range cell can be modeled as multicomponent cubic phase signals (CPSs). The chirp rate and the quadratic chirp rate of the CPS are identified as the causes of the time-varying Doppler frequency; thus, it is necessary to estimate these two parameters correctly to obtain a well-focused ISAR image. The parameter-estimation algorithm based on the modified chirp rate-quadratic chirp rate distribution (M-CRQCRD) is proposed for the CPS and applied to the ISAR imaging of targets with complex motions. The computational cost of M-CRQCRD is low, because it can be implemented by the fast Fourier transform (FFT) and the nonuniform FFT easily. Compared to two representative parameter-estimation algorithms, the M-CRQCRD can acquire a higher antinoise performance due to the introduction of an optimal lag-time. Through simulations and analyses for the synthetic radar data, the effectiveness of the M-CRQCRD and the imaging algorithm based on the M-CRQCRD are verified.

ISAR Imaging of Targets With Complex Motions Based on Modified Lv’s Distribution for Cubic Phase Signal

For targets with complex motions, such as highly maneuvering airplanes and ships fluctuating with oceanic waves, the Doppler frequencies of scatterers are actually time-varying and azimuth echoes of a range cell have to be modeled as multicomponent cubic phase signals (CPSs) after the range alignment and the phase adjustment. In inverse synthetic aperture radar (ISAR) imaging based on the CPS model, the chirp rate and the quadratic chirp rate are identified as causes of the image defocus. In this paper, by employing a novel parametric symmetric self-correlation function and the keystone transform, an effective estimation algorithm, known as the modified Lv’s distribution (MLVD), is presented for the CPS and applied to ISAR imaging of targets with complex motions. The MLVD is simple and can be easily implemented using the complex multiplication, the fast-Fourier transform (FFT), and the inverse FFT (IFFT). The implementation, the cross-term, the antinoise performance, and the computational cost are analyzed for the MLVD. Compared to three representative estimation algorithms for the CPS, the MLVD can acquire a higher antinoise performance and eliminate the brute-force searching without the interpolation. Through simulations and analyses for synthetic models and the real radar data, we verify the effectiveness of the MLVD and the corresponding ISAR imaging algorithm.

ISAR imaging of maneuvering target with complex motions based on ACCF–LVD

This paper considers the inverse synthetic aperture radar (ISAR) imaging problem for a maneuvering target with complex motions, involving range migration (RM) and Doppler frequency migration (DFM) within the coherent integration period of radar imaging, which will degrade the imaging quality. A nonsearching ISAR imaging algorithm based on adjacent cross correlation function (ACCF) and Lv's distribution (LVD), i.e., ACCF–LVD, is proposed, where the ACCF is applied to correct the RM and reduce the order of DFM. Then the motion parameters are estimated via LVD and Fourier transform. With the estimated motion parameters, high quality ISAR images can be achieved. The advantage of the presented method is that it can estimate the motion parameters under low signal-to-noise ratio (SNR) without searching procedures. Finally, several simulation examples are shown to confirm the validity of the proposed algorithm.

Asymptotic Statistical Performance of Local Polynomial Wigner Distribution for the Parameters Estimation of Cubic-Phase Signal With Application in ISAR Imaging of Ship Target

The parameters estimation of cubic-phase signal based on local polynomial Wigner distribution (LPWD) was proposed in the author’s previous paper, and it outperforms the traditional algorithms in inverse synthetic aperture radar (ISAR) imaging of maneuvering target. The contribution of this paper is that the asymptotic statistical performance of LPWD for the parameters estimation of cubic-phase signal is analyzed theoretically, and the asymptotic statistical results are derived for all the estimated parameters via the first-order perturbation principle. Then, the LPWD is used in ISAR imaging of ship target with complex motion, and the high-quality instantaneous ISAR images can be obtained.

Sparse aperture inverse synthetic aperture radar imaging of manoeuvring targets with compensation of migration through range cells

In inverse synthetic aperture radar (ISAR) imaging of a target with significant manoeuvres, severe migration through range cells (MTRCs) and time-varying Doppler usually involve in the echoed signal. Both of them may challenge the conventional motion compensation and imaging methods, which are usually based on the assumption of small rotational angle with short coherent time duration. Moreover, for a multi-functional ISAR, full aperture (FA) data collection might be unachievable because of the conflict with other radar activities, resulting in sparse aperture (SA) data. In this study, an ISAR imaging method of manoeuvring targets with SA data is proposed, where MTRC compensation and time-varying Doppler are well accounted. By using a sparse chirp-Fourier basis with first-order range–azimuth coupling term, an improved orthogonal match pursuit algorithm is implemented to solve the sparsity-driven optimisation for FA data reconstruction from SA-ISAR measurements. As a primary item, MTRC compensation is seamlessly embedded into the FA signal reconstruction for ISAR imaging of manoeuvring targets. Both simulated and real data are utilised to confirm the effectiveness of the proposed method.

ISAR Imaging of Targets With Complex Motion Based on the Chirp Rate–Quadratic Chirp Rate Distribution

In inverse synthetic aperture radar (ISAR) imaging of targets with complex motion such as fluctuating ships with oceanic waves and high maneuvering airplanes, the azimuth echo signals can be modeled as cubic phase signals (CPSs) after the migration compensation. The chirp rate (CR) and the quadratic chirp rate (QCR) are two important physical quantities of the CPS, which deteriorate the azimuth focusing quality due to the Doppler frequency shift. With these two quantities, other parameters can be estimated by using the fast Fourier transform (FFT). Therefore, the CPS can be uniquely determined by both CR and QCR. In this paper, based on the proposed generalized keystone transform and the parametric instantaneous autocorrelation function, a novel distribution of the CPS, known as the CR–QCR distribution (CRQCRD), is presented and applied in a newly proposed ISAR imaging algorithm for targets with complex motion. The CRQCRD is simple and only requires the FFT and the nonuniform FFT (NUFFT). Owing to the application of the NUFFT, the computational cost is saved, and the searching procedure is unnecessary for the nonuniformly spaced signal. Compared to other four representative methods for CPSs, the CRQCRD, which can acquire higher antinoise performance and no error propagation, is searching-free and more suitable for the situation of multitargets. Several simulation examples, analyses of the antinoise performance, and ISAR images validate the effectiveness of the CRQCRD and the corresponding ISAR imaging algorithm.

3D interferometric ISAR imaging of noncooperative targets

Inverse synthetic aperture radar (ISAR) images are frequently used in target classification and recognition applications. Nevertheless, the interpretation of ISAR images remains problematic for several reasons. One of these is the fact that the image plane cannot be defined by the user but instead depends on the target's own motions and on its relative position with respect to the radar. In order to overcome the problem of interpreting two-dimensional (2D) ISAR images, a method for three-dimensional (3D) reconstruction of moving targets is presented. This method is based on the use of a dual interferometric ISAR system. The interferometric phases measured from two orthogonal baselines are used to jointly estimate the target's effective rotation vector and the heights of the scattering centers with respect to the image plane. The scattering center extraction from the ISAR image is performed by applying a multichannel CLEAN technique. Finally, a 3D image of the moving target is reconstructed from the 3D spatial coordinates of the scattering centers. The effectiveness and robustness of the proposed algorithm is first proven theoretically and then tested against several radar-target scenarios as well as in the presence of noise.

Simultaneous ISAR imaging of group targets flying in formation

This paper proposes a novel inverse synthetic aperture radar (ISAR) imaging method based on second-order keystone transform (KT) and Sandglass transform for group targets flying in a formation with constant accelerated rectilinear motion in the same radar beam. First, range curvature and range walk of each sub-target among group targets are corrected by the second-order KT combined with the quadratic phase term compensation. After range alignment, the signals in each range frequency cell can be modelled as multiple chirp signals and then the Sandglass transform is utilized to cross-range imaging, which transforms the time–frequency distribution of the signals in each range frequency cell into beelines parallel to the slow time axis simultaneously. Finally, cross-range profiles of group targets in each range frequency cell are obtained via a projection of the perk of every scatterer in the two-dimensional accumulation plane onto the frequency axis. The advantage of the proposed method is that it can align range profiles of each sub-target simultaneously and image cross-range profiles directly without separating the returned signals, which simplifies the operation procedure. Simulation results are used to demonstrate the effectiveness of the proposed method.

ISAR Imaging of Targets With Complex Motions Based on the Keystone Time-Chirp Rate Distribution

In inverse synthetic aperture radar (ISAR) imaging of targets with complex motions such as fluctuating ships with oceanic waves and high maneuvering airplanes, the azimuth echo signals can be modeled as cubic phase signals (CPSs). In this letter, a new ISAR imaging algorithm based on the keystone time-chirp rate distribution (KTCRD) is proposed for the targets with complex motions. Compared with the recently published algorithms for the CPSs, the KTCRD can estimate the parameters of multicomponent CPSs without searching procedures and can acquire high antinoise performance with a relatively low computational load. With the estimated motion parameters, high-quality ISAR images can be obtained. Several simulation examples on the synthetic model are shown to validate the effectiveness of the new algorithm presented in this letter.