Range Instantaneous Doppler Research Articles

ISAR Imaging Analysis of Complex Aerial Targets Based on Deep Learning

Traditional range–instantaneous Doppler (RID) methods for maneuvering target imaging are hindered by issues related to low resolution and inadequate noise suppression. To address this, we propose a novel ISAR imaging method enhanced by deep learning, which incorporates the fundamental architecture of CapsNet along with two additional convolutional layers. Pre-training is conducted through the deep learning network to establish the mapping function for reference. Subsequently, the trained network is integrated into the electromagnetic simulation software, Feko 2019, utilizing a combination of geometric forms such as corner reflectors and Luneberg spheres for analysis. The results indicate that the derived ISAR imaging effectively identifies the ISAR program associated with complex aerial targets. A thorough analysis of the imaging results further corroborates the effectiveness and superiority of this approach. Both simulation and empirical data demonstrate that this method significantly enhances imaging resolution and noise suppression.

Deep Learning-Based Enhanced ISAR-RID Imaging Method

Inverse synthetic aperture radar (ISAR) imaging can be improved by processing Range-Instantaneous Doppler (RID) images, according to a method proposed in this paper that uses neural networks. ISAR is a significant imaging technique for moving targets. However, scatterers span across several range bins and Doppler bins while imaging a moving target over a large accumulated angle. Defocusing consequently occurs in the results produced by the conventional Range Doppler Algorithm (RDA). Defocusing can be solved with the time-frequency analysis (TFA) method, but the resolution performance is reduced. The proposed method provides the neural network with more details by using a string of RID frames of images as input. As a consequence, it produces better resolution and avoids defocusing. Furthermore, we have developed a positional encoding method that precisely represents pixel positions while taking into account the features of ISAR images. To address the issue of an imbalance in the ratio of pixel count between target and non-target areas in ISAR images, we additionally use the idea of Focal Loss to improve the Mean Squared Error (MSE). We conduct experiments with simulated data of point targets and full-wave simulated data produced by FEKO to assess the efficacy of the proposed approach. The experimental results demonstrate that our approach can improve resolution while preventing defocusing in ISAR images.

ISAR Imaging of Precession Target Based on Joint Constraints of Low Rank and Sparsity of Tensor

Precession is a typical form of micro-motion that can bring about complex and time-varying Doppler modulation. The range instantaneous Doppler (RID) method, which uses time-frequency analysis instead of the Fourier transform to describe the time-varying Doppler, is typically used to obtain the high-resolution inverse synthetic aperture radar (ISAR) image of a precession target. However, the observation time of a specific target is often non-uniform due to various interference and channel switching among multi-channel radars, which will lead to a sparse aperture. Sparse aperture can cause sidelobe interference in the ISAR image obtained by the RID method, making it difficult to focus well. To solve the problem whereby the RID method fails to image a precession target with sparse aperture, this paper proposes a new method based on the joint constraints of low-rank and sparsity of tensor, and uses the alternating direction method of multipliers to solve the problem. The low-rank can constrain the correlation among consecutive ISAR images, and sparsity can remove the impact of sparse aperture. This effectively eliminates the micro-Doppler interference and sidelobe interference in ISAR image, and enables the reconstruction of precession target in a sequence of ISAR images with sparse aperture. Experimental results under both simulations and darkroom measurements verify that the proposed method performs well on ISAR images of conic precession target with sparse aperture.

Recognition of Micro-Motion Space Targets Based on Attention-Augmented Cross-Modal Feature Fusion Recognition Network

Narrowband and wideband waveforms are usually adopted simultaneously during the observation of micro-motion space targets by inverse synthetic aperture radar (ISAR), which can collect rich multimodal information in the time-Doppler, time-range, and range-instantaneous-Doppler domains. In order to exploit the electromagnetic scattering, shape, structure, and motion characteristics, this article proposes an attention-augmented cross-modal feature fusion recognition network, namely ACM-FR Net. Firstly, the ACM-FR Net adopts convolution neural network (CNN) to extract initial feature vectors from joint time-frequency (JTF) image, high resolution range profiles (HRRPs), and range-instantaneous-Doppler (RID) image, respectively. Then, it transforms the feature vectors of the three modalities into feature sequences. Finally, it achieves interactive feature fusion by implementing attention-augmented cross-modal feature fusion. In the four-category micro-motion space targets recognition experiments, the proposed ACM-FR Net has demonstrated high accuracy and noise robustness.

Frame Selection Method for ISAR Imaging of 3-D Rotating Target Based on Time–Frequency Analysis and Radon Transform

When a target has 3-D rotation accompanying roll–pitch–yaw movement, it yields an additional phase component, which cannot be compensated for by any of the existing inverse synthetic aperture radar (ISAR) imaging methods. Under this condition, the resulting ISAR image will be severely distorted. Accordingly, selecting a proper imaging frame, where the target has only 2-D rotation, becomes the only solution, especially for range-Doppler (RD) algorithm. To do this, the conventional approach is to directly use the phase of the received signal, and however, this approach is vulnerable to noise and the cross-term interference of multiple scattering centers (SCs) in the range bin. To address these issues, we propose a new frame selection method using the range-instantaneous Doppler (RID) technique and Radon transform to detect the presence of 3-D rotation of the target within imaging frame. The proposed method comprises three steps: 1) obtain joint time–frequency distribution (JTFD) via the smoothed pseudo Wigner–Ville distribution (SPWVD); 2) estimate the orientation angle of the Radon image of the JTFD spectrum; and 3) select the suitable imaging frame using the estimated angle. Furthermore, to demonstrate the effectiveness of the proposed method, experimental results based on simulated and real measured data are provided.

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.

A Novel ISAR Imaging and Scaling Approach for Maneuvering Targets Based on High-Accuracy Phase Parameter Estimation Algorithm

For inverse synthetic aperture radar (ISAR) imaging of maneuvering targets, the Doppler frequency induced by an arbitrary scatterer on the target is time-varying, and hence, the traditional range-Doppler (RD) algorithm is not appropriate for obtaining focusing ISAR images. In such a scenario, a novel ISAR technique, called the range instantaneous Doppler derivative (RIDD) algorithm, has been recently proposed, and two different kinds of well-focused ISAR images, named range-Doppler centroid (RDC) frequency image and range-Doppler frequency rate (RDR) image, can be obtained. In order to improve the accuracy of the target classification and recognition, the scaling approach for the RIDD algorithm must be taken into consideration. In this article, the point spread function (PSF) representations and cross-range scaling factors of the RDC image and the RDR image obtained by the RIDD algorithm are demonstrated analytically. On the basis of the PSFs and cross-range scaling factors, a novel imaging and scaling approach for the RIDD algorithm in the presence of a low SNR environment based on smoothed integrated high-resolution time–frequency-rate representation (SIHR) combined with coherent integrated smoothed generalized cubic phase function (CISCF) is proposed. As the proposed algorithm has high estimation accuracy and good antinoise capability, precise parameter estimation of the received signal can be achieved. Hence, high-quality scaled RDC and RDR images of the target can be reconstructed by the proposed approach. Experimental results of simulated data and real measured data verify the correctness of the PSF representations and demonstrate the effectiveness of the imaging and scaling approach proposed in this article.

InISAR Imaging for Maneuvering Target Based on the Quadratic Frequency Modulated Signal Model With Time-Varying Amplitude

Interferometric inverse synthetic aperture radar (InISAR) has proven to be an effective tool for 3-D imaging of noncooperative targets. The traditional InISAR imaging algorithms are almost entirely based on the assumption of a constant amplitude polynomial phase signal (PPS) model. However, target’s maneuverability tends to cause the echo signal to exhibit the characteristic of time-varying amplitude (TVA) in practice. To remedy this problem, a novel InISAR imaging algorithm for maneuvering targets based on quadratic frequency modulated (QFM) signal model with TVA is presented. First, the echo of each range cell is modeled as a multicomponent TVA-QFM signal. Then, through the matrix derivation, the parameter estimation problem of this signal is converted to a convex optimization problem. Subsequently, with the help of the scaled Fourier transform (SCFT), an efficient iterative update approach based on alternative direction method of multipliers (ADMM) framework is proposed to solve this optimization problem. Furthermore, associated with the range instantaneous Doppler (RID) method and multichannel interference technology, 2-D ISAR images and 3-D space shape of the target can be generated. Finally, some simulation results are provided to evaluate the effectiveness and robustness of the proposed algorithm.

Super-Resolution ISAR Imaging for Maneuvering Target Based on Deep-Learning-Assisted Time–Frequency Analysis

Traditional range-instantaneous Doppler (RID) methods for maneuvering target imaging suffer from the problems of low resolution and poor noise suppression. We propose a new super-resolution inverse synthetic aperture radar (ISAR) imaging method based on deep-learning-assisted time–frequency analysis (TFA). Our deep neural network resembles the basic structure of a U-net with two additional convolutional-upsampling layers and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula> -norm loss function for super-resolution generation and noise suppression. The neural network is trained in advance to learn the mapping function between the low-resolution time–frequency spectrum inputs and their high-resolution references. Then, the linear TFA assisted by the pretrained network is integrated into the RID-based ISAR imaging system and is found to achieve sharply focused and denoised target image with super-resolution. Both the simulated and real radar data are used to evaluate the performance of the proposed method. Numerical experimental results demonstrate the superiority of the proposed ISAR imaging method over traditional ones.

High-Resolution ISAR Imaging for Maneuvering Targets Based on Iterative Adaptive Processing

Inverse synthetic aperture radar (ISAR) imaging for maneuvering targets undergoing significant angular motions in a high-resolution radar system is a complicated task due to the presence of time-varying Doppler phases, resulting in the substantial performance degradation by using the conventional range-Doppler (RD) algorithm. In this paper, a novel range instantaneous Doppler (RID) technique based on high-resolution time-frequency distribution (TFD) is proposed to realize the ISAR imaging for maneuvering targets with complex phase fluctuation. After motion compensation, the target signal in a range unit is transformed into high-resolution time-frequency domain by means of the short-time iterative adaptive approach (STIAA), which can avoid the troubled cross-term interference and effectively break through the time-frequency resolution restriction in comparison with the conventional short-time Fourier transform (STFT). Finally, the reconstruction of high-resolution ISAR image for a maneuvering target can be obtained in the RID domain. The effectiveness of the proposed ISAR imaging algorithm is validated by both simulated and real-measured SAR/ISAR data processing results.

Ship Target Imaging in Airborne SAR System Based on Automatic Image Segmentation and ISAR Technique

The airborne synthetic aperture radar (SAR) image of ship target will be blurred for the complex motion of the target, which will influence the performance of feature extraction and target classification. In this article, a refocusing method of ship target in airborne SAR image though the inverse synthetic aperture radar (ISAR) technique is presented. The novel contributions of this article can be summarized as follows: 1) the residual phase of processed signal is analyzed in detail to illustrate the necessity of adopting ISAR technique; 2) a valid, labor-saving, and automatic image segmentation approach via clustering algorithm is put forward for automatic data extraction and inverse mapping. The clustering algorithm we choose is the agglomerative hierarchical cluster algorithm, which does not require the number of categories in advance; 3) the refocused individual ISAR image of individual ship target is yielded though the range-instantaneous Doppler algorithm. Finally, the simulated and real measured data are processed, and imaging results verify the effectiveness of the novel algorithm presented in this article.

JTF Analysis of Micromotion Targets Based on Single-Window Variational Inference

This article addresses the problem of joint time–frequency (JTF) analysis of micromotion targets in complex environments in an approximate Bayesian inference framework. First, the sparse observation model is constructed, which is then decomposed into a series of single-window-JTF (SW-JTF) analysis problems to tackle the high dimension of the over-complete dictionary. On this basis, the probabilistic graphical model is constructed by imposing the Gamma-complex Gaussian prior to the JTF distribution. Finally, the model parameters are solved effectively by single-window variational inference (SWVI). Compared with the available methods, the proposed method could obtain better-focused JTF signature for narrowband data and higher quality range-instantaneous Doppler (RID) image for wideband data, especially in low signal-to-noise ratio (SNR) and data corruption scenarios.

Rotation Parameters Estimation and Cross-Range Scaling Research for Range Instantaneous Doppler ISAR Images

The maneuverability of targets cannot be neglected in many circumstances for Inverse Synthetic Aperture Radar (ISAR) imaging procedure. For these circumstances, the Range Instantaneous Doppler (RID) algorithm is required to form the well-focused ISAR images. Simultaneously, as the inevitable means of obtaining the target’s cross-range size, the cross-range scaling approach for the RID images also needs to be considered consequently. Since there are few cross-range scaling methods focused on the RID images, we propose a novel method in this paper based on the phase coefficients estimation and scatterers pairing process to solve the problem. Without the special requirements for the scatterers or complicated optimization process, the proposed approach is widely adaptive and efficient. Otherwise, besides the novel estimation method for the rotation parameters, we also propose the novel technique for calculating the cross-range scaling factor of the RID images, which is quite different from that of the Range Doppler (RD) images. Finally, by using the recommended RID imaging algorithm and the proposed cross-range scaling approach, the results of ISAR imaging and cross-range scaling experiments on the simulation and real-data experiment are represented, which demonstrate the effectiveness of the proposed approach in this paper.

Novel Approach for ISAR Cross-Range Scaling of Maneuvering Target

Cross-range scaling is very important for the inverse synthetic aperture radar (ISAR) imaging of non-cooperative target, and it is useful for the target feature extraction and recognition. In this paper, a novel approach for the ISAR cross-range scaling of maneuvering target is proposed. It assumes that the target has a non-uniform rotation, and the initial rotation velocity and rotation acceleration can be estimated. Through the rotational correlation of the instantaneous ISAR images sequence generated by the range-instantaneous-Doppler (RID) method, an approximate linear relationship between the time and the rotation velocity can be obtained to estimate the rotational parameters. The experimental results demonstrate the effectiveness of the novel algorithm proposed in this paper.

Super-Resolution Sparse Aperture ISAR Imaging of Maneuvering Target via the RELAX Algorithm

A novel super-resolution sparse aperture inverse synthetic aperture radar (SA-ISAR) imaging algorithm with relaxation (RELAX) technique is proposed. First, the motion compensation by the minimum entropy for range alignment (MERA) and the minimum entropy for phase adjustment (MEPA) is employed. Then, the received echo in one range cell is characterized as a multi-component linear frequency-modulated (LFM) signal model, and the RELAX method is adopted successfully to acquire the estimated parameters of it with random sparsely recorded samples. Finally, by the range-instantaneous-Doppler (RID) technique, the super-resolution SA-ISAR images of maneuvering target can be generated consequently. Through the experimental results of raw and simulated data, we validate that the presented SA-ISAR imaging algorithm with the RELAX technique can be efficiently utilized to obtain super-resolution images of maneuvering target.

PWFT-RD: A High-Quality and Low-Complexity ISAR Imaging Approach for Maneuvering Target

For inverse synthetic aperture radar imaging, high-quality and real-time performances are two essential indicators in many real applications. Due to the complex motion of the maneuvering target, the Doppler frequency is generally time varying. Consequently, it is difficult for most imaging algorithms to satisfy these two indicators simultaneously. The approaches based on the traditional range-Doppler (RD) algorithm can generally achieve rapid imaging but often encounter image blurring problems while the range-instantaneous-Doppler (RID) algorithms can generate high-quality images but at the cost of tremendous computational complexity. Aiming at this issue, we propose a new approach, termed power-weighting Fourier transform-based range-Doppler (PWFT-RD), to achieve a better comprehensive performance of the image quality and computational cost simultaneously. Specifically, we first build up the generalized signal model to describe the target motion with high-order dynamics. Second, we propose the PWFT which has the property of modulating expanded the signal spectrum into a keen-edged envelope. After that, we implement high-accuracy motion compensation (MOCOMP) by formulating the high-order parameter estimation as the least-squares problem and finally develop the PWFT-RD approach. Analysis and comparison experiments are designed to validate the effectiveness of our approach on employing PWFT for improving MOCOMP accuracy and enhancing the azimuth focus quality. In particular, the PWFT-RD approach shows much better real-time performance than RID algorithms thanks to its linear and blind processing attributes with quite low computational complexity.

ISAR Imaging of Ship Targets Based on an Integrated Cubic Phase Bilinear Autocorrelation Function.

For inverse synthetic aperture radar (ISAR) imaging of a ship target moving with ocean waves, the image constructed with the standard range-Doppler (RD) technique is blurred and the range-instantaneous-Doppler (RID) technique has to be used to improve the image quality. In this paper, azimuth echoes in a range cell of the ship target are modeled as noisy multicomponent cubic phase signals (CPSs) after the motion compensation and a RID ISAR imaging algorithm is proposed based on the integrated cubic phase bilinear autocorrelation function (ICPBAF). The ICPBAF is bilinear and based on the two-dimensionally coherent energy accumulation. Compared to five other estimation algorithms, the ICPBAF can acquire higher cross term suppression and anti-noise performance with a reasonable computational cost. Through simulations and analyses with the synthetic model and real radar data, we verify the effectiveness of the ICPBAF and corresponding RID ISAR imaging algorithm.

ISAR Imaging of Maneuvering Target Based on the Quadratic Frequency Modulated Signal Model With Time-Varying Amplitude

Inverse synthetic aperture radar (ISAR) imaging of maneuvering target plays an important role in the field of national defense and surveillance. In this case, the conventional range Doppler algorithm is unsuitable to generate a well-focused ISAR image due to the time-varying property of the Doppler frequency during the integrated imaging interval. Furthermore, the dihedral or trihedral components of the imaging target or the phenomena of migration through resolution cell for the target's complex motion will induce the time-varying performance of the amplitude for the received signal. In this paper, the received signal in a range bin is depicted as multicomponent quadratic frequency-modulated signal with time-varying amplitude, and the generalized cubic phase function is used to estimate the parameters of it. Associated with the range instantaneous Doppler technique, the images quality can be enhanced distinctly compared with the conventional constant amplitude signal model. Results of simulated and read data demonstrate the effectiveness of the novel algorithm proposed in this paper.

Radar imaging of micromotion targets from corrupted data

When echoes of micromotion targets are corrupted with only randomly positioned samples due to interference or sensor failure, the state-of-art radar imaging techniques become invalid. To solve this problem, this paper constructs the nonparametric dictionary in the joint time-frequency domain and then proposes the modified augmented Lagrangian method to obtain the range-instantaneous Doppler (RID) imaging. Simulation results have shown that the proposed method can obtain well-focused imaging of micromotion targets with relatively low computational burden.

A Novel Doppler Frequency Model and Imaging Procedure Analysis for Bistatic ISAR Configuration With Shorebase Transmitter and Shipborne Receiver

In order to avoid bad bistatic geometry cases and offer better concealment and anti-interference ability, a novel kind of bistatic inverse synthetic aperture radar (ISAR) configuration, named shorebase–shipborne bistatic ISAR, is presented. In this paper, the Doppler frequency model of the received signal for shorebase–shipborne bistatic ISAR is proposed, when both the target and the receiver ship have nonstationary three-dimensional (3-D) rotational motion. An analysis of the Doppler shift and image aliasing induced by the receiver ship’s rotational motion is given. To eliminate the effects of receiver ship’s rotational motion, a new image reconstruction method in combination with range-instantaneous Doppler (RID) technique and rotational motion compensation is proposed. The simulated results demonstrate the effects of the receiver ship’s rotational motion and the effectiveness of new image reconstruction method.