Translational Motion Compensation Research Articles

Micro-Doppler Extraction of Radar Targets With Translational Motion Based on Spatial Transformer Network

The micro-Doppler (MD) signature, an effect that is induced by micro-motion, characterizes the rich motion information of targets, making it a valuable tool for target classification and recognition. However, the MD structure is always destroyed for radar targets with translational motion. In this letter, we propose an end–to–end translational motion compensation network based on a spatial transformer network (STN). First, the effect of the translational motion on the radar echo and its time-frequency graph (TFG) is modeled, so that translational motion compensation is regarded as an image spatial transformation task. Then, a two-branch convolutional network based on residual modules is designed to locate the transformation parameters, and the TFG containing only MD is obtained using a grid generator and sampler. Finally, the simulation results demonstrate that the proposed algorithm has high accuracy and stability.

Translational Motion Compensation for Maneuvering Target Echoes With Sparse Aperture Based on Dimension Compressed Optimization

Translational motion compensation for maneuvering targets is a challenging step in inverse synthetic aperture radar (ISAR) imaging. On one hand, nonuniform translational motion and rotational motion of maneuvering targets result in nonlinear bending of the range profile and create high-order slow time phases. On the other hand, radar cross section (RCS) fluctuation of targets may cause a sparse aperture and, therefore, sharply weakens the correlation between the pulses. Under this condition, traditional methods of translational motion compensation are sometimes difficult to get ideal results. Therefore, we propose an effective and novel method to achieve translational motion compensation for maneuvering targets with sparse aperture. In this method, the translational and rotational motions are modeled as cubic polynomial and quadratic polynomial, respectively. Based on this model, we use the echo to construct a 1-D optimization to estimate all translational parameters simultaneously and use the root-mean-square prop-momentum gradient descent (RMSprop-MGD) algorithm to solve the optimization to maintain the accuracy of parameter estimation. Finally, we use those translational parameters to compensate for the translational motion. Since this method converts the multiparameter optimization into a 1-D optimization, we named it dimension compressed optimization (DCO). Experimental results of the measured dataset prove that the proposed method is effective for full-aperture echoes, random sparse echoes, and block sparse echoes. Moreover, this method is robust under a low pulse sampling rate and low signal-to-noise ratio (SNR) environment.

Fully Coherent Integration and Measurement of Optimized Frequency Agile Waveform for Weak Target High-Resolution ISAR Imaging

Frequency agile waveform effectively decreases interception probability and increases the anti-jamming ability for radar probing in the electromagnetic countermeasure environment. However, the transmitting agile frequency introduces jitter phases causing de-coherence of echoes, which decreases signal-to-noise ratio (SNR) accumulation gain and leads to difficult motion estimation. Therefore, stable target detection, motion parameters estimation, and inverse synthetic aperture radar (ISAR) imaging for frequency-agile radar are still intractable problems in the low SNR environment. Aiming at these problems, we proposed a fully coherent processing method for weak target detection and ISAR imaging for frequency agile waveform in this paper. The agile waveform with low range and Doppler side lobes is designed to improve motion parameters estimation and ISAR imaging performance. Then, the joint jitter phases and motion parameters estimation based on the generalized likelihood ratio test (GLRT) is proposed to realize robust target detection and motion parameters estimation in low SNR conditions. The translational motion compensation and sparse ISAR imaging methods are also presented based on the detection and motion parameters estimation results. Finally, both simulated and real-measured data are used to verify the remarkable detection, motion parameters estimation, and ISAR imaging performance compared with the traditional non-coherent accumulation and mixture of coherent and non-coherent accumulation methods.

Ambiguity Function based High-Order Translational Motion Compensation

Ambiguity Function based High-Order Translational Motion Compensation

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.

Parametric translational motion compensation of spaceborne ISAR imagery for earth-orbit targets based on parabola detection and entropy minimization

ABSTRACT In the spaceborne inverse synthetic aperture radar (ISAR) imagery for space targets, the noise environment can be worse than traditional imaging scenarios because of the signal decay caused by the long radial range. Parametric translational motion compensation (TMC) method based on the image quality optimization has been proved to be useful in the low signal-to-noise ratio environments. However, a proper estimation of the initial values is indispensable to avoid local optimum solutions and improve computational efficiency. In this letter, a TMC method based on the parabolic curve detection and the entropy minimization is proposed. An initial compensation result is firstly obtained with the range shifts estimated by the parabolic curve detection using the Hough transform. Further, the minimum entropy-based method is employed to improve compensation accuracy. The imaging results of the real data are provided and compared with other methods to demonstrate the effectiveness and the high accuracy of the proposed method.

Noise-robust range alignment method for inverse synthetic aperture radar based on aperture segmentation and average range profile correlation

Range alignment is an essential procedure in the translation motion compensation of inverse synthetic aperture radar imaging. Global optimization or maximum-correlation-based algorithms have been used to realize range alignment. However, it is still challenging to achieve range alignment in low signal-to-noise ratio scenarios, which are common in inverse synthetic aperture radar imaging. In this paper, a novel anti-noise range alignment approach is proposed. In this new method, the target motion is modeled as a uniformly accelerated motion during a short sub-aperture time. Minimum entropy optimization is implemented to estimate the motion parameters in each sub-aperture. These estimated parameters can be used to align the profiles of the current sub-aperture. Once the range profiles of each sub-aperture are aligned, the non-coherent accumulation gain is obtained by averaging all profiles in each sub-aperture, which can be used as valuable information. The accumulation and correlation method is applied to align the average range profiles of each sub-aperture because the former step focuses mainly on alignment within the sub-apertures. Experimental results based on simulated and real measured data demonstrate the effectiveness of the proposed algorithm in low signal-to-noise ratio scenarios.

Efficient Nonparametric ISAR Autofocus Algorithm Based on Contrast Maximization and Newton’s Method

Phase autofocus is a significant step in translational motion compensation for inverse synthetic aperture radar (ISAR) imaging. Among the existing autofocus methods, contrast maximization-based algorithms are superior in accuracy and robustness. However, the existing maximum contrast autofocus methods are parametric, which significantly affects their convergence speed and, in some cases, limits their applicability. In this paper, a novel non-parametric maximum contrast ISAR autofocus algorithm based on Newton’s method is proposed to inherit the high level of accuracy and robustness of contrast-based algorithms and, at the same time, achieve faster convergence. The simplified Newton’s method, the modified Newton’s method, and the secant method are used to obtain a higher computational efficiency. The proposed method is then compared to a well-established non-parametric method, namely the minimum entropy autofocus method. It will be shown that the proposed method can improve the computational efficiency by a factor from 5 to 10 times in typical scenarios. Both simulated and real data will be used to test the proposed algorithm, particularly in terms of computational effectiveness.

An enhanced adaptive butterfly optimization algorithm rigorously verified on engineering problems and implemented to ISAR image motion compensation

PurposeThis study aims to evolve an enhanced butterfly optimization algorithm (BOA) with respect to convergence and accuracy performance for numerous benchmark functions, rigorous constrained engineering design problems and an inverse synthetic aperture radar (ISAR) image motion compensation.Design/methodology/approachAdaptive BOA (ABOA) is thus developed by incorporating spatial dispersal strategy to the global search and inserting the fittest solution to the local search, and hence its exploration and exploitation abilities are improved.FindingsThe accuracy and convergence performance of ABOA are well verified via exhaustive comparisons with BOA and its existing variants such as improved BOA (IBOA), modified BOA (MBOA) and BOA with Levy flight (BOAL) in terms of various precise metrics through 15 classical and 12 conference on evolutionary computation (CEC)-2017 benchmark functions. ABOA has outstanding accuracy and stability performance better than BOA, IBOA, MBOA and BOAL for most of the benchmarks. The design optimization performance of ABOA is also evaluated for three constrained engineering problems such as welded beam design, spring design and gear train design and the results are compared with those of BOA, MBOA and BOA with chaos. ABOA, therefore, optimizes engineering designs with the most optimal variables. Furthermore, a validation is performed through translational motion compensation (TMC) of the ISAR image for an aircraft, which includes blurriness. In TMC, the motion parameters such as velocity and acceleration of target are optimally predicted by the optimization algorithms. The TMC results are elaborately compared with BOA, IBOA, MBOA and BOAL between each other in view of images, motion parameter and numerical image measuring metrics.Originality/valueThe outperforming results reflect the optimization and design successes of ABOA which is enhanced by establishing better global and local search abilities over BOA and its existing variants.

A Study on RCS and Scattering Point Analysis Based on Measured Data for Maritime Ship

In order to set up radar cross section(RCS) reduction factors for a target, the scattering point position of the target should be identified through inverse synthetic aperture radar(ISAR) image analysis. For this purpose, ISAR image focusing is important. Maritime ship is non-linear maneuvering in the sea, however, which blur the ISAR image. To solve this problem, translational and rotational motion compensation are essential to form focused ISAR image. In this paper, hourglass and ISAR image analysis are performed on the collected data in the sea instead of using the prediction software tool, which takes much time and cost to make computer-aided design(CAD) model of the ship. Keywords: Radar Cross Section(ë ˆì´ë”ë°˜ì‚¬ë‹¨ë©´ì ), Scatterer(ì‚°ëž€ì ), High Resolution Range Profile(ê³ í•´ìƒë„ 거리측면도), Hourglass(모래시계), Inverse Synthetic Aperture Radar(ì—­í•©ì„±ê°œêµ¬ë ˆì´ë”)

High-Resolution ISAR Imaging and Motion Compensation With 2-D Joint Sparse Reconstruction

With regard to the multifunction radar transmitting sparse stepped-frequency-modulation (SSFM) signal for inverse synthetic aperture radar (ISAR) imaging, the received echo signal is usually sparse in two dimensions, i.e., sparse stepped-frequency-modulation and sparse aperture waveforms (SSFM-SAWs), and there are translational and rotational motion errors between subpulses. The two problems seriously challenge the feasibility of conventional 1-D sparse reconstruction algorithms. This article proposes a novel high-resolution ISAR imaging and motion compensation with the 2-D joint sparse reconstruction (2D-JSR) algorithm. In this technique, a 2D-JSR dictionary is established according to the SSFM-SAW signal model. Based on the Bayesian compressive sensing (BCS) theory, the 2D-JSR is then transformed into solving a sparsity-driven optimization problem with ${l_{1}}$ -norm constraint. With the accommodation of a modified quasi-Newton solver, the exact recovery of SSFM-SAW can be achieved. In addition, a new algorithm, named joint translational motion compensation and range spatial-variant autofocus (JTSVA) algorithm, is also developed to realize motion parameters by a two-step estimation. Integrating with 2-D coupling information of echo signal and the efficient and robust motion compensation algorithm, the accurate motion parameters together with well-focused and scaled high-resolution ISAR images can be obtained. Extensive experiments based on both simulated and real data demonstrate that the proposed algorithm is capable of the precise reconstruction of ISAR images and the effective suppression of both motion errors and noise.

A translational motion compensation technique for inverse synthetic aperture radar images using multi‐objective particle swarm optimization algorithm

AbstractIn this study, a novel multi‐objective (MO) motion compensation (MC) technique is proposed to clear blur effects in the inverse synthetic aperture radar (ISAR) images resulted by the translational motion of the target. Thanks to the MO particle swarm optimization (PSO) algorithm, the velocity and acceleration of the target which minimize the entropy and maximize the contrast of the ISAR image are optimally determined. In order to find an optimal solution from trade‐off solutions between entropy and contrast, Pareto front technique is exploited. To demonstrate the performance of the algorithm, the proposed method is implemented for the four ISAR scenarios reported elsewhere, and compared with the single‐objective meta‐heuristic optimization algorithms (artificial bee colony, genetic algorithm, and PSO with island model) implemented in the literature. Furthermore, the accuracy of the proposed technique is numerically pointed out by comparing the entropy and contrast values of each scenarios with the actual values. The results obviously indicate that the proposed MO MC technique is very successful and efficient compared to single‐objective algorithms and performance of the technique is higher than the other methods as the velocity and the acceleration increases.

ISAR Imaging of Non-Uniform Rotating Targets Based on Optimized Matching Fourier Transform

Uniformly accelerated rotation of maneuvering targets can lead to quadratic phase terms, after translational motion compensation, which causes the cross-range signal in a certain range cell to be multicomponent linear frequency modulated (LFM) signals. Due to the time-varying Doppler frequencies of scatterers, ISAR image obtained by the conventional range-Doppler (RD) algorithm is blurred severely. To solve this problem, a new imaging method based on optimized matching Fourier transform (MFT) is proposed in this paper. In the new method, the equivalent rotation of targets is described by the rotation ratio (RR) which is the same for all scatterers on the target. The quadratic phase components caused by target’s angular acceleration can be compensated by MFT, as soon as the RR is obtained. The estimation of RR is achieved according to the optimal imaging result with the image entropy as the cost function. As a result, the blurring of the ISAR image is eliminated effectively and a well-focused image is got. From the experimental results using simulated data, it can be concluded that the proposed method based on optimized MFT is effective in ISAR imaging of non-uniform rotating targets in terms of image quality and computational efficiency.

Fast ISAR autofocus algorithm via sub‐aperture

Translational motion compensation (TMC) is a significant step for inverse synthetic aperture radar (ISAR), while phase autofocus plays an important role in TMC. By eigenvalue decomposition (EVD), principle eigenvectors of the covariance matrix constructed by the complex range-aligned signal can be acquired for ISAR-phase autofocus. Nevertheless, for purpose of enhancing the cross-range resolution, the observation pulse number should be increased. Large observation pulse number will lead to the heavy computational complexity due to EVD, which may overburden the system. To overcome this issue, a novel sub-aperture EVD-based method is proposed here. The full-aperture signal is divided into multiple partially overlapped sub-apertures with a novel partition criterion based on the average range profiles, then the conventional EVD-based methods are utilised to obtain the phase errors in sub-apertures. After the jump-phase compensation, combine the phase-error estimation of all sub-aperture to complete the full-aperture phase autofocus. With this method, the computational efficiency can be improved remarkably in typical scenarios, compared with the traditional autofocus algorithms based on EVD. Finally, results for the simulated data and the real measured data are demonstrated to verify the validity of the proposed algorithm.

Bistatic ISAR Imaging for Nonuniformly Rotating Targets

When a target undergoes nonuniform rotational motion (RM), conventional motion compensation (MOCOM) approaches for monostatic inverse synthetic aperture radar (ISAR) systems fail to correct the nonuniform RM error in bistatic radar systems; this failure is caused by nonlinear phase relationships between scatterers in bistatic ISAR (Bi-ISAR) systems. To address this problem, in this paper, we propose a new MOCOM framework for Bi-ISAR imaging of nonuniformly rotating targets. In the proposed method, a newly devised correction is performed to ensure successful RM compensation, followed by translational motion compensation. Because the phase relationships of the scatterers become virtually linear after this correction, nonuniform RM can be successfully converted into uniform RM. Namely, using the proposed method, translational and rotational motion errors are correctly removed, and focused Bi-ISAR images are obtained even for a target involved in nonuniform RM. Furthermore, for more effective use of Bi-ISAR images, a method to restore sheared Bi-ISAR images is exploited in the proposed framework. In the simulations and experiments, we determined that the proposed method can provide high-quality Bi-ISAR images for nonuniformly rotating targets.

Accelerated translational motion compensation with contrast maximisation optimisation algorithm for inverse synthetic aperture radar imaging

Range alignment of traditional translational motion compensation for inverse synthetic aperture radar imaging generally cannot be implemented accurately under low signal-to-noise ratio, resulting in the following phase adjustment invalid. In this study, a novel accelerated translational motion compensation with contrast maximisation optimisation algorithm is proposed. Translational motion is first modelled as a parametric finite order polynomial. The translational motion property can be compactly expressed by a polynomial coefficient vector. Meanwhile, the image contrast is utilised to estimate the polynomial coefficient vector based on the maximum contrast optimisation, implemented by Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. BFGS is an effective quasi-Newton algorithm, yielding fast convergence and small computational complexity. Moreover, a method called pseudo Akaike information criterion is also proposed to determine the polynomial order adaptively. Both simulated and real data experiments are provided for a clear demonstration of the proposed algorithm.

Passive ISAR part I: framework and considerations

In part I of a two-part passive imaging series, a comprehensive framework for imaging of air targets with passive radar (PR) systems is developed. All operations necessary to convert raw sample data into two-dimensional imagery are touched on. The bistatic geometries, relatively narrow signal bandwidths, and low centre frequencies of most PR illuminators are considered along with their impact on image formation procedures. The core PR processing stages stemming from the illuminators' waveform properties are also examined within this context. The various stages of translational motion compensation are shown to have significant impact on image quality, while target tracking data aids estimation of the apparent target rotation. In turn, this enables cross-range scaling for more advanced methods of image formation to a known image formation plane. Thus, the established framework establishes necessary operations for extension to the multistatic imaging case. The framework is validated in simulation with parameters in accordance with digital television broadcast signals.

Translational Motion Compensation of Space Micromotion Targets Using Regression Network

The high-speed translational motion of space targets will cause the micro-Doppler to shift, tilt, and fold, which brings great difficulty to the extraction of micromotion features. Translational motion must be compensated in advance to extract the authentic characteristics of micro-Doppler curves. To solve the problem of translational motion compensation, an estimation method of translational parameters based on deep learning theory is proposed. A polynomial model describing translational motion is constructed firstly by analyzing its dynamic principle. Meanwhile, the parametric characterization of micromotion signal is deduced by taking the cone target as an example. On this basis, two regression networks that can estimate acceleration and velocity respectively from the time-frequency graph are built using transfer learning. The translational motion compensation is accomplished with high accuracy and low computation complexity. The proposed method can also achieve satisfactory results in the presence of high intensity noise and discontinuous scattering points. Finally, the effectiveness and robustness of the proposed method are validated by simulations.

Bistatic ISAR Imaging and Scaling of Highly Maneuvering Target With Complex Motion via Compressive Sensing

The bistatic configuration used for inverse synthetic aperture radar (ISAR) imaging has attracted considerable attention because of its potential for overcoming certain monostatic ISAR imaging limitations. However, translational motion compensation (TMC) and rotational motion compensation (RMC) are the most fundamental requirements for both bistatic ISAR (Bi-ISAR) and monostatic ISAR imaging to obtain the high levels of image resolution desired. Moreover, range and cross-range scaling (RCRS) and bistatic distortion correction (BDC) are essential functionalities for the efficient use of Bi-ISAR images in applications. In this paper, a novel approach to sparse aperture (SA) Bi-ISAR imaging is presented for simultaneously performing TMC, RMC, RCRS, and BDC on maneuvering targets based on compressive sensing. Instead of solving conventional optimization problems constrained by the sparsity of the signals, the proposed method utilizes sensing matrix estimation technique for Bi-ISAR image reconstruction using parametric signal model reconstruction by looking for the basis functions that best represent the behavior of sensing dictionary matrix comprised of the observed SA data. The reconstruction of the sensing matrix is based on a modified orthogonal matching pursuit (MOMP)-type basis function-searching scheme. Finally, we generate a focused and scaled Bi-ISAR image from the complete Bi-ISAR signal recovered using the proposed method. Several simulation results reveals that the proposed method is very efficient in forming Bi-ISAR images of high-speed maneuvering targets in terms of the Bi-ISAR signal reconstruction accuracy.

ISAR Autofocus Imaging Algorithm for Maneuvering Targets Based on Phase Retrieval and Gabor Wavelet Transform

The imaging issue of a rotating maneuvering target with a large angle and a high translational speed has been a challenging problem in the area of inverse synthetic aperture radar (ISAR) autofocus imaging, in particular when the target has both radial and angular accelerations. In this paper, on the basis of the phase retrieval algorithm and the Gabor wavelet transform (GWT), we propose a new method for phase error correction. The approach first performs the range compression on ISAR raw data to obtain range profiles, and then carries out the GWT transform as the time-frequency analysis tool for the rotational motion compensation (RMC) requirement. The time-varying terms, caused by rotational motion in the Doppler frequency shift, are able to be eliminated at the selected time frame. Furthermore, the processed backscattered signal is transformed to the one in the frequency domain while applying the phase retrieval to run the translational motion compensation (TMC). Phase retrieval plays an important role in range tracking, because the ISAR echo module is not affected by both radial velocity and the acceleration of the target. Finally, after the removal of both the rotational and translational motion errors, the time-invariant Doppler shift is generated, and radar returned signals from the same scatterer are always kept in the same range cell. Therefore, the unwanted motion effects can be removed by applying this approach to have an autofocused ISAR image of the maneuvering target. Furthermore, the method does not need to estimate any motion parameters of the maneuvering target, which has proven to be very effective for an ideal range–Doppler processing. Experimental and simulation results verify the feasibility of this approach.