Translational Motion Compensation Research Articles

A Robust Translational Motion Compensation Method for Moving Target ISAR Imaging Based on Phase Difference-Lv’s Distribution and Auto-Cross-Correlation Algorithm

Translational motion compensation constitutes a pivotal and essential procedure in inverse synthetic aperture radar (ISAR) imaging. Many researchers have previously proposed their methods to address this requirement. However, conventional methods may struggle to produce satisfactory results when dealing with non-stationary moving targets or operating under conditions of low signal-to-noise ratios (SNR). Aiming at this challenge, this article proposes a parametric non-search method that contains two main stages. The radar echoes can be modeled as polynomial phase signals (PPS). In the initial stage, the energy of the received two-dimensional signal is coherently integrated into a peak point by leveraging phase difference (PD) and Lv’s distribution (LVD), from which the high-order polynomial coefficients can be obtained accurately. The estimation of the first-order coefficients is conducted during the second stage. The auto-cross-correlation function for range profiles is introduced to enhance the accuracy and robustness of estimation. Subsequently, a novel mathematical model for velocity estimation is proposed, and its least squares solution is derived. Through this model, a sub-resolution solution can be obtained without requiring interpolation. By employing all the estimated polynomial coefficients, the non-stationary motion of the target can be fully compensated, yielding the acquisition of a finely focused image. Finally, the experimental findings validate the superiority and robustness of the proposed method in comparison to state-of-the-art approaches.

A Novel Joint Motion Compensation Algorithm for ISAR Imaging Based on Entropy Minimization.

Space targets move in orbit at a very high speed, so in order to obtain high-quality imaging, high-speed motion compensation (HSMC) and translational motion compensation (TMC) are required. HSMC and TMC are usually adjacent, and the residual error of HSMC will reduce the accuracy of TMC. At the same time, under the condition of low signal-to-noise ratio (SNR), the accuracy of HSMC and TMC will also decrease, which brings challenges to high-quality ISAR imaging. Therefore, this paper proposes a joint ISAR motion compensation algorithm based on entropy minimization under low-SNR conditions. Firstly, the motion of the space target is analyzed, and the echo signal model is obtained. Then, the motion of the space target is modeled as a high-order polynomial, and a parameterized joint compensation model of high-speed motion and translational motion is established. Finally, taking the image entropy after joint motion compensation as the objective function, the red-tailed hawk-Nelder-Mead (RTH-NM) algorithm is used to estimate the target motion parameters, and the joint compensation is carried out. The experimental results of simulation data and real data verify the effectiveness and robustness of the proposed algorithm.

A novel motion compensation algorithm for spaceborne inverse synthetic aperture radar imaging of air target under low signal‐to‐noise ratio condition

AbstractThe spaceborne Inverse Synthetic Aperture Radar (ISAR) has garnered significant attention due to its extensive observation range and robust anti‐attack capabilities. Consequently, the ISAR imaging research of air targets based on a spaceborne platform has crucial application value. However, unlike the traditional ground‐based radar system, the spaceborne platform moves along its own orbit while observing the air target, and the received signal energy is weakened due to the extended observation distance. Therefore, it is important to optimise the existing ISAR imaging geometry models and motion compensation algorithms. The authors first construct a geometric model of spaceborne ISAR imaging for air targets. Aiming at the problem of low signal‐to‐noise ratio (SNR), a novel translational motion compensation algorithm based on motion parameter estimation is proposed. The algorithm compensates for both distance migration and Doppler migration caused by the first‐order and second‐order motion components of relative motion, respectively. Finally, simulation and semi‐physical simulation results validate the effectiveness and superiority of the proposed algorithm under different SNR and motion conditions.

Joint Translational Motion Compensation and High-Resolution ISAR Imaging Based on Sparse Bayesian Learning

Joint Translational Motion Compensation and High-Resolution ISAR Imaging Based on Sparse Bayesian Learning

Translational Motion Compensation Method for ISAR Imaging of Air Maneuvering Weak Targets Based on CV-GRUNet

Translational Motion Compensation Method for ISAR Imaging of Air Maneuvering Weak Targets Based on CV-GRUNet

A novel spaceborne ISAR imaging approach for space target with high-order translational motion compensation and spatial variant MTRC correction

ABSTRACT For spaceborne ISAR imaging of space targets, due to the high-speed relative motion between the radar and targets, both the translational and rotational motion exhibit the high-order characteristics. Conventional translational motion compensation (TMC) methods are ineffective in handling such high-order translational motion. Moreover, the migration through resolution cell (MTRC) stems from the presence of the high-order spatial variant phase of the echo signal, resulting in severe defocusing of ISAR images. In this paper, a novel spaceborne ISAR imaging approach for space targets is proposed, which incorporates the high-order TMC and the high-order MTRC correction. First, the slant range model for spaceborne ISAR imaging is derived as a third-order polynomial form. Based on this model, a modified high-order TMC method is proposed, which utilizes the minimum entropy method combined with the Broyden – Fletcher – Goldfarb – Shanno (BFGS) algorithm. The proposed method leverages orbital model information to obtain the initial TMC coefficients, enabling TMC to be completed more accurately and effectively. Then, a modified polynomial keystone transform (PKT) is proposed to correct the high-order MTRC. The PKT-based method effectively addresses both the high-order and first-order MTRC corrections simultaneously. Finally, a spaceborne ISAR imaging approach is proposed to obtain well-focused ISAR images. Simulated and real data results validate the analysis of the spaceborne ISAR imaging model and demonstrate the effectiveness of the spaceborne imaging approach for space targets proposed in this paper.

Joint ISAR imaging and azimuth scaling under low SNR using parameterized compensation and calibration method with entropy minimum criterion

In general, the method of conventional motion compensation for inverse synthetic aperture radar (ISAR) imaging is divided into translational motion compensation (TMC) and rotational motion compensation (RMC) in sequence. TMC is the premise of rotational compensation and the most critical procedure is range alignment. However, the deviation of echo correlation results in the poor performance of range alignment under low signal-to-noise ratio (SNR). Therefore, a new high-resolution ISAR imaging and azimuth scaling method under low SNR using parameterized compensation and calibration is proposed in this paper. Firstly, the target motion is modeled, in which translational motion is modeled as formula of the polynomial coefficient vector. In addition, entropy minimization corresponding to echo signal with compensation term based on coefficients is taken as objective function. Moreover, the particle swarm optimization (PSO) algorithm is utilized to search the global optimal parameters to be estimated precisely and efficiently to implement joint motion compensation and azimuth scaling. The experimental results from both simulated and real data verify the effectiveness and robustness of the method.

Interferometric ISAR Imaging of Space Targets Using Pulse-Level Image Registration Method

Interferometric inverse synthetic aperture radar (InISAR) imaging is capable of retrieving three-dimensional target information from multi-channel inverse synthetic aperture radar (ISAR) images obtained by adjacent radars or antennas. The InISAR imaging of space targets is more difficult, compared with that observes short range targets with small radar systems. Considering the practical constraints when observing space targets with multiple large radars, this paper establishes a flexible InISAR imaging system which allows for non-perpendicular baselines and observation of targets under squint mode. Based on this, the generalized formulas for calculating scatterers' coordinates from measured interferometric phases are derived. Moreover, a pulse-level image registration method based on joint translational motion compensation is proposed to recover the coherence between multi-channel ISAR images, under condition of inconsistent pulse delays at different radar channels. With the proposed method, promising InISAR imaging results are obtained via two simulation experiments. The first experiment assumes ideal point scattering of the target, which verifies the high accuracy and computational efficiency of the proposed ISAR image registration method; the second experiment considers more practical target scattering using facet target model and physical optics algorithm, which demonstrates the possible deviation in InISAR imaging result caused by scatterer's anisotropic scattering and residual error of translational motion compensation. We believe that the derivation and simulation results in this paper would be helpful reference for practical InISAR imaging experiments.

Fusion of Inverse Synthetic Aperture Radar and Camera Images for Automotive Target Tracking

Automotive targets undergoing turns in road junctions offer large synthetic apertures over short dwell times to automotive radars that can be exploited for obtaining fine cross-range resolution. Likewise, the wide bandwidths of the automotive radar signal yield high-range resolution profiles. Together, they are exploited for generating inverse synthetic aperture radar (ISAR) images that offer rich information regarding the target vehicle's size, shape, and trajectory which is useful for object recognition and classification. However, a key requirement for ISAR is translation motion compensation and estimation of the turning velocity of the target. State-of-the-art algorithms for motion compensation trade-off between computational complexity and accuracy. An alternative low complexity method is to use an additional sensor for tracking the target motion. In this work, we propose to exploit computer vision algorithms to identify the radar target object in the sensor field-of-view (FoV) with high accuracy. Further, we propose to track the target vehicle's motion through fusion of vision and radar data. Vision data facilitates the accurate estimation of the lateral position of the target and its lateral velocity which complements the radar's capability of accurate estimation of range and radial velocity. Through simulations and experimental evaluations with a monocular camera and Texas Instrument's millimeter wave radar, we demonstrate the effectiveness of sensor fusion for accurate target tracking for translational motion compensation and generation of high quality ISAR images.

An Effective Translational Motion Compensation Approach for High-Resolution ISAR Imaging With Time-Varying Amplitude

An Effective Translational Motion Compensation Approach for High-Resolution ISAR Imaging With Time-Varying Amplitude

Joint Translational Motion Compensation for Multitarget ISAR Imaging Based on Integrated Kalman Filter

Joint Translational Motion Compensation for Multitarget ISAR Imaging Based on Integrated Kalman Filter

Noise-Robust ISAR Translational Motion Compensation via HLPT-GSCFT

Translational motion compensation is a prerequisite of inverse synthetic aperture radar (ISAR) imaging. Translational motion compensation for datasets with low signal-to-noise ratio (SNR) is important but challenging. In this work, we proposed a noise-robust translational motion compensation method based on high-order local polynomial transform–generalized scaled Fourier transform (HLPT-GSCFT). We first model the translational motion as a fourth-order polynomial according to order-of-magnitude analysis, and then design HLPT-GSCFT for translation parameter estimation and parametric translational motion compensation. Specifically, HLPT is designed to estimate the acceleration and third-order acceleration of the translational motion and GSCFT is introduced to estimate the second-order acceleration. Both HLPT and GSCFT have a strong ability for cross-term suppression. In addition, we use a minimum weighted entropy algorithm to estimate the velocity of the translational motion, which can improve the noise robustness of the parameter estimation. Experimental results based on a measured dataset prove that the proposed method is effective and noise-robust.

A reweighted alternating direction method of multipliers for joint phase adjustment and ISAR imaging

ABSTRACT In view of inverse synthetic aperture radar (ISAR) imaging, sparse aperture not only affects the imaging quality of traditional range Doppler (RD) algorithm but also increases the difficulty of translational motion compensation (TMC). For the purpose of reducing the influence of motion errors on the target imaging, reweighted alternating direction method of multipliers (RADMM) is proposed to realize the joint processing of phase adjustment and ISAR imaging. Combined with pattern-coupled sparse structure (PCSS) information of the target, the reweighted norm minimization problem is established first. Then, the minimum entropy method is adopted to estimate the phase error, and the iterative process of RADMM is deduced. Finally, the experimental results based on two sets of measured data demonstrate the validity of the proposed algorithm under the conditions of noise and sparse aperture.

An Efficient Translational Motion Compensation Approach for ISAR Imaging of Rapidly Spinning Targets

For inverse synthetic aperture radar (ISAR) imaging of rapidly spinning targets, the large migration through range cells (MTRC) results in weak coherence between adjacent echoes, which makes the conventional envelope alignment method unable to be applied. By analyzing the correlation between the echoes, a translational motion compensation (TMC) method for rapidly spinning targets is proposed. Firstly, the rotation period of the target is estimated by the incoherent accumulation method for the echo signal after range compression. Secondly, Kalman filtering is performed on the shift values required to maximize the correlation coefficient of the echoes with one rotation period difference in azimuth time to obtain the relative translational motion of the radar and the target. Finally, a translational compensation function is constructed according to the results of Kalman filtering to compensate the phase items caused by translational motion. Furthermore, the covariance matrix of observation noise required by Kalman filtering is also provided. This method is used to achieve high-precision envelope alignment, and the effectiveness of the proposed method is validated by simulations.

Novel approach for shipborne‐passive bistatic imaging with DVB‐T signals based on MIAA–RID technique

The shipborne-passive bistatic inverse synthetic aperture radar system with digital video broadcasting terrestrial (DVB-T) signals is very important in the fields of radar imaging, but it suffers from the problems of grating lobes caused by the spectral gaps when multiple DVB-T channels are applied, and the non-stationary echo signal for the complex motions of the ship. In this paper, a new algorithm based on the Missing Data Iterative Adaptive Approach (MIAA) combined with the Range Instantaneous Doppler (RID) technique is proposed to address these problems. First, the authors establish the imaging geometry and signal model. Based on these models, the range resolution and Doppler frequency are analysed. Then, the bandwidth synthesis in the frequency domain for the DVB-T signal is derived. Finally, the MIAA method is used to recover the spectral gaps of the range profile for multiple DVB-T channels, and the azimuth compression is carried out by the RID method after the translational motion compensation. Simulation results show the effectiveness of the algorithm proposed in this paper.

Translational motion estimation and compensation in distributed inverse synthetic aperture radar imaging of ship target with complex motion

A translational motion estimation and compensation method is proposed based on the parametric method by multiple stations in the distributed inverse synthetic aperture radar (ISAR) imaging. For ship targets with complex motion, the distributed ISAR increases the cross-range resolution of the image by expanding the observation angle. However, the translation compensation methods by a single station will destroy the correlation between the echoes, which reduces the image quality, and a more accurate joint translation compensation method is required. Multi-static observation is obtained in the Distributed system, which we can utilise in the parameter estimation: 1) joint Doppler estimation, 2) station angle estimation, and 3) rotation velocity estimation. Based on the above estimations, the velocity vector and the acceleration vector of the target can be obtained, and then the translational motion compensation can be realised parametrically. The algorithm processing chain is presented in this paper. Finally, the numerical simulations with four stations are presented to verify the effectiveness and the robustness of the proposed algorithm.

An Improved Parametric Translational Motion Compensation Algorithm for Targets With Complex Motion Under Low Signal-to-Noise Ratios

Translational motion compensation plays an important role in inverse synthetic aperture radar (ISAR) imaging. However, existing translational motion compensation algorithms cannot work well when the signal-to-noise ratio (SNR) is low and the target has complex motion at the same time, as the algorithms usually assume that these two situations do not occur simultaneously. To address this problem, an improved parametric translational motion compensation algorithm based on signal phase order reduction (SPOR) and minimum entropy is proposed. The key is to decrease the phase order of the signal, which has a nonlinear phase and corresponds to the complex motion, and then obtain the signal with a linear phase corresponding to the noncomplex motion. Subsequently, the signal is transformed into the Doppler domain to generate the SPOR result. Obviously, when the translational motion is well compensated, the SPOR result will be coherently accumulated and has the best quality, which means that the SPOR result has good robustness against the low SNR. Thus, the translational motion is modeled as a polynomial model, the entropy of the SPOR result is taken as the optimizing target, and the relationship between the translational motion compensation parameters and the entropy is established. Finally, coarse search and particle swarm optimization (PSO) are sequentially performed to optimize the entropy of the SPOR result and estimate the translational motion compensation parameters accurately and efficiently. Computer simulation results and experimental results based on unmanned aerial vehicle (UAV) radar validate the proposed algorithm.

Translational Motion Compensation for ISAR Imaging Based on Range Joint Fast Orthogonal Matching Pursuit Algorithm

Accurate translational motion compensation (TMC) is the key procedure of inverse synthetic aperture radar (ISAR) imaging. Under the condition of sparse aperture, the correlation of adjacent pulses is severely destroyed, which brings a few challenges to TMC and imaging. Thus, this paper proposes a novel TMC method for ISAR imaging. In this technique, the signal model of joint phase adjustment and ISAR imaging is established first. Then, the process of TMC is designed. That is, after range alignment with improved global minimum entropy (IGME), the range joint fast orthogonal matching pursuit (RJFOMP) algorithm is applied to ISAR imaging, and the minimum entropy method (MEM) is adopted to estimate the phase error. Meanwhile, the joint optimization of phase adjustment and ISAR imaging is realized through the cyclic iteration. Extensive experiments based on both simulated and measured data demonstrate that the proposed TMC method is effective for two modes of motion errors, which are named as coherent mode (CM) and non-coherent mode (NCM), respectively. When RJFOMP is used for joint phase adjustment and ISAR imaging, it has excellent imaging performance under noisy and sparse conditions.

Joint Translational Motion Compensation Method for ISAR Imagery Under Low SNR Condition Using Dynamic Image Sharpness Metric Optimization

Translational motion compensation plays an important role in the inverse synthetic aperture radar (ISAR) imagery. In this study, a new translational motion compensation algorithm for ISAR imaging under low signal-to-noise ratio (SNR) conditions is proposed. This method is formed based on the optimization of dynamic image sharpness metric, by which the translational parameters are accurately estimated from the returned signals. The important properties of the locally and globally optimal points of dynamic image sharpness function are proved and discussed by first using the dominant point-targets model. These properties are employed in the scheme to search for the globally optimal point and prevent the optimization being trapped at a locally optimal point. Based on the properties of optimal points and Gauss–Newton method, the algorithm to estimate the translational parameters by dynamic image sharpness metric optimization (DISMO) is devised. The DISMO can find the accurate translational parameters corresponding to the globally optimal point without being affected by local optima under low SNR conditions with high efficiency. Further, the translational compensation is completed based on the estimates. The proposed method is applied to simulated and real data. The processing results confirm the effectiveness of this new algorithm.

A Robust Translational Motion Compensation Method Based on Weighted Optimization Framework—Second-Order Drift Particle Swarm Optimization

Accurate translational motion compensation is critical for ISAR imaging, especially for datasets with low signal-to-noise ratio (SNR) or sparse aperture. Among the existing translational motion compensation methods, the methods that based on the polynomial model have profound noise robustness but are easily affected by the variation of the reference distance. The methods that based on the range profile alignment have good versatility but are far from ideal under low SNR and sparse aperture environment. Therefore, we propose a novel and robust translational motion compensation method to overcome the shortcoming of the existing methods. Specifically, we estimate the translational phase error sequence by maximizing Laplacian average gray level and peak value. It can improve the robustness of translational motion compensation for noise and sparse aperture. Since such principle creates a large-scale multi-objective optimization problem, we design weighted optimization framework - second-order drift particle swarm optimization (WOF - SDPSO) to solve it accurately. WOF-SDPSO achieves full exploration of high-dimensional variable space by variable dimension reducing, random drift update, and second-order oscillation convergence. These ensure the global optimal solution easier to be found. Experimental results of the measured dataset prove that the proposed method is effective and accurate under the conditions of low SNR and sparse aperture. Moreover, the proposed method is noise robust for polynomial translational motion model with sparse aperture.