Inverse Synthetic Aperture Radar Imaging Of Maneuvering Targets Research Articles

Ultrawideband ISAR Imaging of Maneuvering Targets With Joint High-Order Motion Compensation and Azimuth Scaling

Ultrawideband (UWB) radar can achieve ultrahigh-resolution inverse synthetic aperture radar (ISAR) imaging of noncooperative targets by transmitting UWB signals. However, the spatial-variant (SV) high-order migration through range cell (MTRC) and phase errors produced by the UWB radar system have seriously challenged the feasibility of conventional ISAR imaging algorithms. Moreover, maneuvering targets has exacerbated this problem compared with the steady ones. In this article, a UWB ISAR imaging algorithm of maneuvering targets with joint high-order motion compensation and azimuth scaling (JHOMCAS) is proposed. For the azimuth SV linear MTRC and 2-D SV high-order MTRC caused by the maneuvering rotational motion of the targets, the cascaded generalized keystone transform (GKT) is adopted for precise correction. It is worth noting that, when eliminating the SV MTRC by the cascaded GKT, the 2-D SV high-order phase errors induced by the maneuvering rotational motion must be accurately compensated, or MTRC correction will fail. The traditional autofocus methods usually only address the phase errors shared by the total target without due attention to the fine SV property. In response to this problem, this article first develops a joint 2-D SV autofocus and azimuth scaling algorithm (JSVAAS) to achieve the integration of SV high-order phase error compensation and azimuth scaling. A JHOMCAS algorithm is proposed to perform the joint processing of GKT and JSVAAS, ``GKT-JSVAAS-GKT.'' This approach helps accomplish the high-precision UWB ISAR imaging of maneuvering targets, and the well-focused and scaled UWB ISAR images obtained will build a sound foundation for target classification and recognition. Extensive experiments based on both scattering point simulation data and electromagnetic calculation data verify that the proposed algorithm outperforms conventional ISAR imaging approaches in UWB ISAR imaging of maneuvering targets.

An Iterative Phase Autofocus Approach for ISAR Imaging of Maneuvering Targets

Translational motion compensation and azimuth compression are two essential processes in inverse synthetic aperture radar (ISAR) imaging. The anterior process recovers coherence between pulses, during which the phase autofocus algorithm is usually used. For ISAR imaging of maneuvering targets, conventional phase autofocus methods cannot effectively eliminate the phase error due to the adverse influence of the quadratic phase terms caused by the target’s maneuvering motion, which leads to the blurring of ISAR images. To address this problem, an iterative phase autofocus approach for ISAR imaging of maneuvering targets is proposed in this paper. Considering the coupling between translational phase errors and quadratic phase terms, minimum entropy-based autofocus (MEA) method and adaptive modified Fourier transform (MFT) are performed iteratively to realize better imaging results. In this way, both the translational phase error and quadratic phase terms induced by target’s maneuvering motion can be compensated effectively, and the globally optimal ISAR image is obtained. Comparison ISAR imaging results indicates that the new approach achieves stable and better ISAR image under a simple procedure. Experimental results show that the image entropy of the proposed approach is 0.2 smaller than the MEA method, which validates the effectiveness of the new approach.

Optimal Coherent Processing Interval Selection for Aerial Maneuvering Target Imaging Using Tracking Information

When a target is in complex three-dimensional (3-D) motions, inverse synthetic aperture radar (ISAR) images cannot be obtained with any motion compensation algorithms. In this case, selecting optimal coherent processing interval (CPI) (or frame) of the fixed rotational axis is the only way to obtain focused ISAR images. In this article, we focus on selecting optimal CPI for ISAR imaging of maneuvering targets, and a novel method using tracking information is proposed. Without complex computational procedures, the proposed method provides robust performance. The method includes three steps. First, target’s orientation (roll, pitch, and yaw) is estimated by using an extended Kalman filter. Second, the least-square method is adopted to find the best fitted lines of roll, pitch, and yaw angles in different time intervals, and the linearity degrees of the three rotation angles are measured over the imaging intervals. Third, the thresholds of the linearity degrees are set, according to which, optimal CPIs can be selected. The performance of proposed method is demonstrated using both simulated data and measured data. In both cases, optimal CPIs can be selected and well-focused ISAR images can be achieved.

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.

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.

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.

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.

Adaptive filtering approach to chirp estimation and inverse synthetic aperture radar imaging of maneuvering targets

In inverse synthetic aperture radar (ISAR) imaging, due to the noncooperative motions of maneuvering targets, the Doppler shifts of scatterers are usually time-varying, and the radar return signals are usually chirps. Thus, chirp estimation plays an important role in the performance of ISAR imaging. Li and Stoica (1996) presented an adaptive finite impulse response (FIR) filtering approach to estimate the amplitudes and phases of sinusoidal signals and applied it to the synthetic aperture radar (SAR) imaging with the sinusoidal signal model. We extend the Li and Stoica's algorithm to estimate the chirps and then apply it to the ISAR imaging of maneuvering targets. The extended algorithm is verified by some raw ISAR data.

High-resolution ISAR imaging of maneuvering targets by means of the range instantaneous Doppler technique: modeling and performance analysis

Very high resolution inverse synthetic aperture radar (ISAR) imaging of maneuvering targets is a complicated task. In fact, the conventional range Doppler (RD) ISAR technique does not work properly when target motions generate terms higher than the first order in the phase of the received signal relative to each scatterer. This effect typically happens when at least one of these situations occur: (1) very high resolution images are required; (2) the target maneuvers; and (3) the target undergoes significant angular motions (roll, pitch, and yaw). A novel ISAR technique, named range instantaneous Doppler (RID), has been proposed for the reconstruction of very high resolution images of maneuvering targets. In this paper, we analytically show that the RID technique works properly when high-resolution ISAR images are required of maneuvering and/or rolling, pitching, and yawing targets; we also quantify the performance improvement of the RID technique with respect to the RD technique. The problem is tackled from an analytical point of view. First, we define a new model of the ISAR received signal that is valid for maneuvering targets, then we derive and compare the analytical expression of the point spread function (PSF) for the two techniques. Furthermore, we perform a statistical analysis to evaluate the improvement of the RID technique versus the RD technique in terms of spatial resolution. Finally, we prove the effectiveness of the RID technique by simulating the imaging process for two different targets: (1) a ship that undergoes roll, pitch and yaw motions and (2) a fast maneuvering airplane.

Time-frequency approaches to ISAR imaging of maneuvering targets and their limitations

Inverse synthetic aperture radar (ISAR) imaging of the noncooperative maneuvering target is a challenging task because of its time variant orientation and rotation velocity which cannot be measured accurately. This correspondence investigates the principles of ISAR imaging of maneuvering targets, and proposes an algorithm for application in situations where the maneuverability is not too severe and the Doppler variation of subechoes from scatterers can be approximated as a first-order polynomial. The imaging results obtained by using real data show the effectiveness of the new method.