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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. [ABSTRACT FROM AUTHOR]

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.

Accurate translational motion compensation is critical for inverse synthetic aperture radar (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 multiobjective 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.

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.

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.

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.

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. [ABSTRACT FROM AUTHOR]

In this work, a parametric-based approach is proposed to perform joint range alignment and phase adjustment based on the intention of fully exploiting the energy of all the scatterers in the moving target and the two-dimensional coherent accumulation gain of both range and azimuth compressions. To that end, first, translational motion is modeled as a polynomial signal, and inspired by the fact that all the scatterers in the moving target experience the same translational range history, the phase difference operation and keystone transform (KT) are utilized to transform the energy of all the scatterers into one range cell. Second, by the virtue of the fractional Fourier transform (FrFT), the energy of all the scatterers is coherently accumulated into a peak point, and from which the polynomial coefficients can be obtained accurately. With the estimated polynomial coefficients, the dechirp operation and KT are applied jointly to compensate range misalignment and phase error. The analysis of the proposed method shows that it is of low computational complexity due to avoiding multidimensional search and improves the output SNR providing satisfactory low SNR performance. The experimental results are provided to demonstrate the performance of the proposed method compared with the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Under low signal-to-noise ratio (SNR), the performance of conventional envelop-based range alignment methods for inverse synthetic aperture radar (ISAR) imaging degrades, resulting in the following phase adjustment or autofocus inapplicable. In this paper, a novel method for the translational motion compensation of ISAR imaging under low SNR is proposed. Translational motion is first modeled as a polynomial, and image quality evaluation metric (IQEM) such as image entropy, contrast, or peak value is utilized as the objective function to estimate the polynomial coefficient vector based on the particle swarm optimization (PSO). A PSO-based iteration process is presented to determine the polynomial order adaptively. Meanwhile, the computation burden of the proposed method is analyzed. In addition, a coarse estimation method of the polynomial coefficient vector is also discussed. Extensive experimental results verify the effectiveness and robustness of the proposed method. [ABSTRACT FROM PUBLISHER]

High resolution inverse synthetic aperture radar (ISAR) imaging of targets with complex motion is a main topic in the radar imaging domain. In fact, the traditional range‐Doppler algorithm is not appropriate to generate a focused ISAR images because of the time‐varying Doppler shifts caused by the target's complex motion. In this study, the azimuth received signal is modelled as multi‐component amplitude‐modulated and frequency‐modulated (AM–FM) signal, and a novel algorithm for the cubic Chirplet decomposition based on generalised cubic phase function is proposed to investigate the AM–FM signal analytically. Then, the corresponding ISAR imaging algorithm associated with the range‐instantaneous‐Doppler technique is proposed. Results of simulated and real data demonstrate the effectiveness of the presented algorithm. [ABSTRACT FROM AUTHOR]

Inverse synthetic aperture radar (ISAR) images can be generated by radar which radiates the electromagnetic wave to a target and receives signal reflected from the target. ISAR images can be widely used to target detection and recognition. This paper proposed a method of generation of high resolution ISAR images by synthesizing frequency spectrums of each stepped chirp waveform in one burst and sub-sampling in frequency domain. This process is performed over entire bursts during coherent processing interval. Conventional ISAR image generation method using stepped frequency waveform has a severe problem of short unambiguous range, loading to ghost phenomenon. However, this problem can be resolved by the proposed method. In simulations, we generate high resolution ISAR image of the moving target which is Boeing-737 aircraft model composed of several ideal point scatterers. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of visual target following for a nonholonomic wheeled mobile robot with a single fixed camera. For the problem, we propose a control scheme composed of vision-based trajectory planning and tracking control. We present online trajectory planning based on image-based visual servoing. The key idea is to compensate the target object motion. In particular, we focus on visual feature motion resulting from the target object motion. Tracking control to the generated trajectory can therefore achieve visual target following precisely even if the target object is moving. Our proposed scheme can be applied to, e.g. robotic systems for autonomous guard and observation. The effectiveness of the control system is evaluated experimentally. © 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]