Affected by the large dynamic range of gray values, strong scattering point edge effect, noise and clutter, inverse synthetic aperture radar (ISAR) images have problems such as boundary blurring and target discontinuity, which bring great challenges to ISAR space target component segmentation. In this paper, a novel ISAR space target component segmentation method, called scattering characteristics guided network (SCGN), is proposed. First, a cross-scale self-attention module (CSSAM) is proposed, which establishes global relationships in different dimensions during cross-scale feature fusion, refining the detailed features of the target while suppressing high sidelobe scattering points and noise. Second, a novel component scattering center extractor (CSCE) is proposed to combine scattering center distribution with the network via explicit supervision. Finally, a novel scattering characteristics-assisted segmentation head (SCASH) is proposed, which introduces the scattering characteristics of each component into the mask segmentation process and models the semantic interdependencies over long distances through a spatial attention mechanism to achieve fine-grained component segmentation. Experimental results on the ISAR simulation dataset and realistic ISAR images show that SCGN outperforms existing methods.

This letter investigates the low-variance broad beampattern design method in distributed phased multiple-input multiple-output (phased-MIMO) radar. The constant modulus constraint across multiple subarrays results in a low-rank and nonconvex objective function, which is traditionally addressed by reformulating it into a solvable semidefinite program through convex relaxation. In contrast, we propose a Riemannian manifold-based method to directly address the low-rank problem without relaxation. The low-variance broad beampattern design is first transformed into an unconstrained quadratic form on a complex constant modulus manifold. Then, a Riemannian conjugate gradient descent (RCGD)-based optimization is proposed to solve the nonconvex objective function by deriving the gradient descent direction and adaptive step size. Numerical simulations demonstrate the superior performance in terms of computation speed and accuracy compared to the conventional methods.

Accurate 3D multi-object tracking (MOT) is crucial for autonomous driving, as it enables robust perception, navigation, and planning in complex environments. While deep learning-based solutions have demonstrated impressive 3D MOT performance, model-based approaches remain appealing for their simplicity, interpretability, and data efficiency. Conventional model-based trackers typically rely on random vector-based Bayesian filters within the tracking-by-detection (TBD) framework but face limitations due to heuristic data association and track management schemes. In contrast, random finite set (RFS)-based Bayesian filtering handles object birth, survival, and death in a theoretically sound manner, facilitating interpretability and parameter tuning. In this paper, we present OptiPMB, a novel RFS-based 3D MOT method that employs an optimized Poisson multi-Bernoulli (PMB) filter while incorporating several key innovative designs within the TBD framework. Specifically, we propose a measurement-driven hybrid adaptive birth model for improved track initialization, employ adaptive detection probability parameters to effectively maintain tracks for occluded objects, and optimize density pruning and track extraction modules to further enhance overall tracking performance. Extensive evaluations on nuScenes and KITTI datasets show that OptiPMB achieves superior tracking accuracy compared with state-of-the-art methods, thereby establishing a new benchmark for model-based 3D MOT and offering valuable insights for future research on RFS-based trackers in autonomous driving.

Multiple extended target tracking (ETT) has gained increasing attention due to the development of high-precision LiDAR and radar sensors in automotive applications. For LiDAR point cloud-based vehicle tracking, this paper presents a probabilistic measurement-region association (PMRA) ETT model, which can describe the complex measurement distribution by partitioning the target extent into different regions. The PMRA model overcomes the drawbacks of previous data-region association (DRA) models by eliminating the approximation error of constrained estimation and using continuous integrals to more reliably calculate the association probabilities. Furthermore, the PMRA model is integrated with the Poisson multi-Bernoulli mixture (PMBM) filter for tracking multiple vehicles. Simulation results illustrate the superior estimation accuracy of the proposed PMRA-PMBM filter in terms of both the positions and extents of vehicles compared with PMBM filters using the gamma Gaussian inverse Wishart and DRA implementations.

This paper presents a new trajectory Poisson multi-Bernoulli (TPMB) filter for group target tracking. Due to the collective behavior and dense spatial distribution, exact trajectory estimation is an extremely challenging task in group target tracking. Aiming for improved performance in group trajectory estimation, the virtual leader-follower model is incorporated into the standard TPMB filter in this paper to address the coordinated motion within groups. Moreover, the Gaussian implementation and $\boldsymbol{L}$-scan approximation for the proposed group target trajectory PMB (GTTPMB) filter are also provided. Finally, a simulation scenario with splitting and merging of groups is established to evaluate the proposed GTTPMB filter. The results demonstrate that the proposed filter can effectively estimate the trajectories of group members without introducing additional computational burden.

Online 3D multi-object tracking (MOT) has recently received significant research interests due to the expanding demand of 3D perception in advanced driver assistance systems (ADAS) and autonomous driving (AD). Among the existing 3D MOT frameworks for ADAS and AD, conventional point object tracking (POT) framework using the tracking-by-detection (TBD) strategy has been well studied and accepted for LiDAR and 4D imaging radar point clouds. In contrast, extended object tracking (EOT), another important framework which accepts the joint-detection-and-tracking (JDT) strategy, has rarely been explored for online 3D MOT applications. This paper provides the first systematical investigation of the EOT framework for online 3D MOT in real-world ADAS and AD scenarios. Specifically, the widely accepted TBD-POT framework, the recently investigated JDT-EOT framework, and our proposed TBD-EOT framework are compared via extensive evaluations on two open source 4D imaging radar datasets: View-of-Delft and TJ4DRadSet. Experiment results demonstrate that the conventional TBD-POT framework remains preferable for online 3D MOT with high tracking performance and low computational complexity, while the proposed TBD-EOT framework has the potential to outperform it in certain situations. However, the results also show that the JDT-EOT framework encounters multiple problems and performs inadequately in evaluation scenarios. After analyzing the causes of these phenomena based on various evaluation metrics and visualizations, we provide possible guidelines to improve the performance of these MOT frameworks on real-world data. These provide the first benchmark and important insights for the future development of 4D imaging radar-based online 3D MOT algorithms.

With the rapid development of deep learning technology, many synthetic aperture radar (SAR) target recognition algorithms based on convolutional neural networks have achieved exceptional performance on various datasets. However, conventional neural networks are repeatedly iterated on a fixed dataset until convergence, and once they learn new tasks, a large amount of previously learned knowledge is forgotten, leading to a significant decline in performance on old tasks. This article presents an incremental learning method based on strong separability features (SSF-IL) to address the model’s forgetting of previously learned knowledge. The SSF-IL employs both intraclass and interclass scatter to compute the feature separability loss, in order to enhance the linear separability of features during incremental learning. In the process of learning new classes, an intraclass clustering loss is proposed to replace the conventional knowledge distillation. This loss function constrains the old class features to cluster around the saved class centers, maintaining the separability among the old class features. Finally, a classifier bias correction method based on boundary features is designed to reinforce the classifier’s decision boundary and reduce classification errors. SAR target incremental recognition experiments are conducted on the MSTAR dataset, and the results are compared with several existing incremental learning algorithms to demonstrate the effectiveness of the proposed algorithm.

Recently, deep learning methods have been widely adopted for ship detection in synthetic aperture radar (SAR) images. However, many of the existing methods miss adjacent ship instances when detecting densely arranged ship targets in inshore scenes. Besides, they suffer from the lack of precision in the instance indication information and the confusion of multiple instances by a single mask head. In this paper, we propose a novel center point prediction algorithm, which detects the center points by finding a long distance variation relationship between two points. The whole prediction process is anchor-free and does not require additional bounding box (BBox) predictions for non-maximum suppression (NMS). Therefore, our algorithm is BBox-free and NMS-free, solving the problem of low recall rates when conducting NMS for densely arranged targets. Furthermore, to tackle the deficiency of position indication information in localization tasks, we introduce a feature fusion module with feature decoupling (FD). This module uses classification branch to provide guidance information for localization branch, while suppressing the influence of the gradient flow mixing, effectively improving the algorithm’s segmentation performance of ship contours. Finally, through principal component analysis (PCA) of the Gaussian distribution covariance matrix, we propose a loss function based on the distance between centroids and the difference of angle, called centroid and angle constraint (CAC). CAC guides the network in learning the criterion that a single dynamic mask head is only valid for a single instance. Experiments conducted on PSeg-SSDD and HRSID demonstrate the effectiveness and robustness of our method.

In recent years, deep learning (DL) algorithms have been successfully applied in synthetic aperture radar automatic target recognition (SAR-ATR) owing to its powerful and excellent target feature extraction and representation ability. However, these DL-based models merely exploit the intensity (magnitude) information of SAR target, without fully considering the domain characteristics underlying the SAR images, for example, azimuth, scattering center, phase and so on. To address this issue, this paper proposes a novel information dissemination networks, called IDNets, by both considering the azimuth and strong scatter centers of SAR target in a multi-scale information dissemination mechanism to improve the representation capability of SAR recognition model. Moreover, IDNets introduces a stream-based self-attention (SSA) mechanism to adaptively learn the attention distribution of the multi-streams multi-scale sematic features, further enhancing the performance of SAR-ATR system. Experimental results conducted on the MSTAR dataset demonstrate the effectiveness and superiority of the proposed IDNets compared to the current state-of-the-art DL-based SAR-ATR methods.

In synthetic aperture radar automatic target recognition (SAR-ATR), the limitations of imaging environment and observation conditions make it challenging to acquire a substantial amount of high-value targets, resulting in a severe shortage of datasets. This scarcity leads to poor performance and instability in few-shot SAR target recognition. To address these shortcomings, this paper proposes Mada-SGD, a novel inner-loop parameter update approach based on meta adaptive hyper-parameter learning. By considering the correlation information between multiple update steps, Mada-SGD learns the weight distribution information of initialization parameters across previous and current update steps, akin to a memory mechanism. This approach enhances feature extraction and representation ability for few-shot SAR targets. Additionally, an adaptive hyper-parameter update strategy is introduced to simultaneously learn the initialization, weight factor, update factor, and update direction in the meta-learner. This effectively resolves parameter updating issues in meta-learning models while improving fast adaptation for few-shot SAR targets. Experimental results on the specialized MSTAR-FSL dataset demonstrate that Mada-SGD outperforms the latest few-shot SAR target recognition model in terms of SAR target recognition performance, validating its advancement and superiority.

In airborne terahertz synthetic aperture radar (THz-SAR), the phase error caused by high-frequency vibration of the platform can result in not only defocusing, but also the emergence of unwanted ghost targets. To tackle this problem, we propose a vibration estimation and compensation method for the dual-channel THz-SAR based on along-track interferometry (ATI). Compared with the state-of-art single-channel-based compensation methods, the proposed method is advantageous in that it can stay effective without isolated dominant scatter points in the scene. This letter first analyzes the relationship between the along-track interferometric phase of the stationary targets’ echo and the instantaneous radial velocity of the platform’s high-frequency vibration. Then, by using the interferometric phase, the phase error caused by vibration is estimated and compensated. Simulation results show that the proposed method can effectively compensate the phase error caused by multicomponents high-frequency vibration without using the information of dominant scatter points.

Over the past decades, radar high-resolution range profile (HRRP) has been one of the research highlights in the field of radar automatic target recognition (RATR) due to its advantages of easy acquisition, small amount of data, and rich target structure information. However, most of existing methods only consider its amplitude (time domain) characteristics, thereby neglecting the temporal dependence and multi-domain features inside the HRRP sequence. To this end, we propose an end-to-end multi-input convolutional gated recurrent unit neural network, called MIConvGRU, for RATR by both exploiting the multi-domain and temporal information to improve the recognition performance of HRRP target. Initially, the data-preprocessing module is employed to extract the multi-domain features of the target, including time domain, frequency domain, and time-frequency domain features, in order to further enhance the target representation. In addition, a cascaded multi-input GRU structure is designed to acquire the multi-domain temporal dependence feature of HRRP sequence from low to high level. Finally, these temporal features are adaptively fused by a parameter learnable strategy. The experimental results show that the proposed MIConvGRU can effectively learn the multi-domain temporal dependence correlation features in HRRP sequences, improving the target recognition performance.

In recent years, there has been growing interest in developing oriented bounding-box (OBB) based deep learning approaches to detect arbitrary-oriented ship targets in synthetic aperture radar (SAR) images. However, most existing OBB-based detection methods suffer from boundary discontinuity problems for bounding box angle prediction and key point regression challenges. In this paper, we present a novel OBB-based detection algorithm that utilizes ellipse encoding to effectively exploit the geometric and scattering properties of ship targets. Specifically, the ship contour is fitted by an OBB inscribed ellipse that is encoded as a set of distances between dynamic key points on the bow and target center. By combining the bow angle interval and the decoding process, the negative impact of the boundary discontinuity problem is avoided. In addition, we propose an elliptical Gaussian distribution heatmap and a pooling strategy termed double peaks max-pooling (DPM), to deal with the challenge of separating densely distributed ships in inshore scenes. The former can enhance the heatmap’s ship-side score gap between neighboring ship targets, while the latter can solve the problem of target center responses being suppressed after max-pooling. Simulation experiments conducted on the benchmark Rotating SAR Ship Detection Dataset (RSSDD) and Rotated Ship Detection Dataset in SAR Images (RSDD-SAR) demonstrate the superior performance of our method for ship target detection compared to several state-of-the-art OBB-based algorithms. Ablation experiments show that elliptical Gaussian distribution heatmap and DPM can further improve the inshore detection performance.

In synthetic aperture radar automatic target recognition (SAR-ATR), target information is usually propagated and reserved in complex-valued form, namely magnitude information and phase information. However, most of the existing SAR target recognition methods only focus on real-valued (magnitude information) calculations and ignore the phase information of targets, yielding poor recognition performance. To overcome this limitation, this paper proposes a multi-stream feature fusion SAR target recognition method based on complex-valued operations, called MS-CVNets, to utilize the phase information of the target effectively. First of all, a series of complex-valued operation blocks are constructed to satisfy the network training in the complex field, such as complex convolution, complex batch normalization, complex activation, complex pooling, complex full connection, etc. Besides, a multi-stream structure is employed by applying different convolution kernels to extract multi-scale information of targets, further enhancing the representation ability of the model. Experimental results on the MSTAR dataset illustrate that, compared with current state-of-the-art real-valued based models, MS-CVNets can achieve better recognition results under both standard operating conditions (SOC) and extended operating conditions (EOC), validating the effectiveness and superiority of the proposed method.

Compared with the conventional 3-D synthetic aperture radar (SAR) system, the arc 3-D SAR can achieve a wide azimuth observation extent. In this letter, a far-field pseudopolar format imaging algorithm for the ground-based arc 3-D SAR is presented. Different from the existing algorithms, the proposed algorithm exploits keystone formatting and fast Fourier transforms (FFTs) to obtain a pseudopolar grid in the elevation direction, then complex multiplications and FFTs are performed in the range-azimuth wavenumber domain to complete 3-D focusing. The advantages of this method are its low computational complexity and high efficiency. The sampling criteria and computational complexity are also discussed in this letter. Finally, the performance of the proposed algorithm is validated with numerical simulations.

Since the width of range swath of synthetic aperture radar (SAR) is restricted by the pulse repetition frequency, there exists a tradeoff between the azimuth resolution and the range swath width. As a result, conventional SAR imaging methods based on the Nyquist sampling theorem can hardly achieve the high resolution and wide swath simultaneously. In this paper, we propose an algorithm of high-resolution and wide-swath SAR imaging based on the combination of Poisson disk sampling and iterative shrinkage thresholding. Poisson disk sampling adopted in the azimuth direction can ensure that the interval between any two adjacent pulses is longer than the Nyquist sampling interval, which provides the potential to widen SAR imaging swath in the range direction. The imaging formation is carried out by performing the inverse operator of the chirp scaling algorithm and the shrinkage thresholding in an iterative fashion. Compared with the existing SAR imaging methods, the proposed method can realize high-resolution and wide-swath SAR imaging simultaneously with affordable computational cost. Simulations and experiments on real SAR data demonstrate the effectiveness of the proposed method.

The multiple hypothesis tracker (MHT) is a popular algorithm for solving multi-target tracking (MTT) problem in cluttered environment. It is known as a maximum a posterior (MAP) estimator which enumerates all possible global hypotheses and dedicates to find the most likely solution based on the received reports. However, its practical application is often limited by the complexity of data association step. This paper describes an efficient MHT data association algorithm which based on the “track-oriented” MHT framework. The proposed approach translates the data association problem to the maximum weight independent set problem (MWISP) and introduces a graph representation to describe the track hypotheses and the compatibility restrictions between them. In this way, the MAP assignment in tracking application can be solved by applying max-product belief propagation (MPBP) inference algorithm to the corresponding graph. Empirical results demonstrate that the MPBP-MHT algorithm outperforms other algorithms in tracking performance even in challenging closely-spaced MTT case.

In order to improve the comprehensive defense capability of netted radar system while tracking the multi-targets cooperatively, a more reasonable evaluation indicator is required to evaluate the overall performance of the netted radar system. The new evaluation indicators for radar deployment and allocation in the netted radar management are proposed quantitatively in this paper. As they consist of coverage ratio, tracking accuracy and anti-jamming capability, the new evaluation indicators can ensure the netted radar system possesses an outstanding anti-jamming capability through radar management. Simulations show that the arrangement result is more reasonable when the new evaluation indicators are applied relative to the convectional indicators. And the optimization process based on the new indicators can make the netted radar system more suitable to the complex situations in practice.

Ground-based SAR has become an important remote sensing technology means for deformation monitoring in recent years. However, in order to achieve higher efficiency data acquisition and wider observation, arc antenna array technology is applied to ground-based SAR. In this paper, a polar format imaging algorithm for arc antenna array ground-based SAR is proposed. Firstly, a brief background on ground-based SAR and arc antenna array are provided. Then, the signal model and imaging algorithm of arc antenna array ground based-SAR are introduced. Finally, simulation result is presented.

When the airborne or missile borne radar works at the forward-looking imaging mode, the common techniques for improving azimuth resolution are invalid, because the difference between the Doppler frequencies of targets in different azimuths is very small. Meanwhile, other real beam sharpening methods only have very limited effect on azimuth resolution enhancement. For the problem of forward-looking imaging of ship targets at sea surface, a L1/2regularization based azimuth resolution enhancement algorithm is proposed in this paper. This algorithm can make full use of the obvious sparsity in the imaging area. The linear observation signal model for forward-looking imaging is built, and the iterative calculation process of L1/2regularization and the detailed steps of multi-channel radar forward-looking imaging are provided in this paper. Finally, the effectiveness of the proposed algorithm is tested and verified with simulation data and real data.

As a new application platform of synthetic aperture radar (SAR), the near-space hypersonic vehicle has a more serious range migration problem than the traditional airborne platform due to its curved-flight motion characteristic, and its imaging results will be further affected under the condition of high resolution demand. Since the SAR slant range of hypersonic platform is relatively complicated, the azimuth and range in the two-dimensional spectrum obtained by means of traditional wavenumber domain imaging algorithm are mutually coupled. As a result, it is difficult to accurately correct the range migration by interpolation operation. Therefore, an improved wavenumber domain imaging algorithm is proposed in this paper. Through the appropriate approximation of the two-dimensional spectrum of the SAR echo signal, the reference function is designed and the well focus of targets in the observation scene is achieved by the Stolt interpolation. Finally, simulation results are given to verify the effectiveness of the algorithm.

Synthetic aperture radar (SAR) is a widely used active high-resolution microwave imaging technique that has alltime and all-weather reconnaissance ability. Compared with traditionally matched filtering (MF)-based methods, Lq(0 ≤ q ≤ 1) regularization technique can efficiently improve SAR imaging performance e.g., suppressing sidelobes and clutter. However, conventional Lq-regularization-based SAR imaging approach requires transferring the 2-D echo data into a vector and reconstructing the scene via 2-D matrix operations. This leads to significantly more computational complexity compared with MF, and makes it very difficult to apply in high-resolution and wide-swath imaging. Typical Lqregularization recovery algorithms, e.g., iterative thresholding algorithm, can improve imaging performance of bright targets, but not preserve the image background distribution well. Thus, image background statistical-property-based applications, such as constant false alarm rate (CFAR) detection, cannot be applied to regularization recovered SAR images. On the other hand, complex approximated message passing (CAMP), an iterative recovery algorithm for L1regularization reconstruction, can achieve not only the sparse estimation of the original signal as typical regularization recovery algorithms but also a nonsparse solution simultaneously. In this paper, two novel CAMP-based SAR imaging algorithms are proposed for raw data and complex radar image data, respectively, along with CFAR detection via the CAMP recovered nonsparse result. The proposed method for raw data can not only improve SAR image performance as conventional L1regularization technique but also reduce the computational cost efficiently. While only when we have MF recovered SAR complex image rather than raw data, the proposed method for complex image data can achieve a similar reconstructed image quality as the regularization-based SAR imaging approach using the full raw data. The most important contribution of this paper is that the proposed CAMP-based methods make CFAR detection based on the regularization reconstruction SAR image possible using their nonsparse scene estimations, which has a similar background statistical distribution as the MF recovered images. The experimental results validated the effectiveness of the proposed methods and the feasibility of the recovered nonsparse images being used for CFAR detection.

Histogram probabilistic multi‐hypothesis tracker (H‐PMHT), based on probabilistic multi‐hypothesis tracker, is a track‐before‐detect processing approach to detect dim targets. For the problem that H‐PMHT cannot initiate new tracks and terminate tracks of disappeared targets, the authors propose a new dynamic programming (DP)‐based H‐PMHT algorithm, which can locate new targets by dealing with a few frames of sensor images and confirm disappeared targets according to their energy accumulation values along the existing tracks. With this sort of track initiation and termination mechanism, H‐PMHT can be directly applied to realistic environments. Simulation results show that the DP‐based H‐PMHT algorithm can rapidly locate new targets and initiate tracks with a low false alarm rate, and quickly terminate tracks of disappeared targets with a low false termination probability.

Speckle is a granular disturbance that affects synthetic aperture radar (SAR) images. Over the last three decades, many methods have been proposed for speckle reduction, where a tradeoff between despeckling and detail preservation is required. As an attempt to balance the performance on both sides, in this paper, we propose a 2-D S transform shrinkage algorithm using adaptive soft threshold for SAR image despeckling. It follows the idea of the wavelet shrinkage algorithm, but extends its major steps to take into account the peculiarities of S transform, i.e., adding adaptivity in the estimation of speckle standard deviation and threshold function, in an optimized computation procedure. Homogeneous and heterogeneous SAR images are used for quantitative evaluations, and both vintage and prevailing algorithms are used for comparison, which demonstrates the validity of the proposed method. Additionally, some instructive pieces of advice are given on the selection of suitable parameters of the proposed method under different circumstances.

In a conventional synthetic aperture radar (SAR) image, a moving target may be smeared and displaced. Taking direct action on the defocused region of interest (ROI) data from the result of a conventional imaging algorithm, this paper presents an imaging method of the ground moving target in high-resolution SAR. A 2-D equivalent velocity parameter space is built along the azimuth and range directions with the derivation of an exact analytic expression of the ROI. In each pair of equivalent velocity parameters, the Stolt interpolation is used herein to remove the residual phase error. After that, a graph of the ROI complex subimage contrast is produced with respect to the equivalent velocity parameter space. Based on the maximum contrast principle, the desired equivalent velocity is then estimated and applied for deblurring the ROI. Finally, we can achieve the refocused SAR image of the moving target. Different from the conventional approach of moving target autofocusing that requires resynthesizing back to the full data from the cropped ROI data, the proposed method directly operates on the small-sized defocused ROI subimage without any resynthesizing operations. It is helpful for the computational burden reduction, procedure simplification, and clutter interference suppression. The experiments on synthetic and real data are carried out to validate the effectiveness of the proposed method.

Paired echoes are the typical manifestation of Doppler characteristics caused by vibrating targets in high-resolution synthetic aperture radar (SAR). Conventional imaging algorithms produce smeared paired echoes. It results in not only the inconvenience of vibration parameter analysis but also the emergence of unwanted ghost targets to degrade SAR image quality. This paper proposes a method on paired echo focusing and suppression of vibrating targets. After demodulation and range compression, the signal is decomposed into the form of Bessel series in the azimuth direction. Then, the range walk is compensated in the 2-D frequency domain by Doppler keystone transform. Next, the range curvature is corrected in the range-Doppler domain by using range cell migration correction to achieve the focus of paired echoes. Compared with conventional SAR imaging algorithms, the focused paired echoes could reach system nominal resolution. Furthermore, this focusing method, which works without prior knowledge of vibrating targets, is suitable for various vibrating states of multiple targets. On the basis of paired echo focusing, the residual video phase of paired echoes is also eliminated. Then, vibration parameters, including vibration frequency, amplitude, and initial phase, are estimated. These parameters are used herein to construct the reference function to compensate the sinusoidal modulation phase in the range-Doppler domain. Finally, deghosted vibrating targets can be obtained. Simulations show that paired echoes could be successfully focused within one resolution unit as well as the robustness of its application. At last, the real SAR data from a moving truck are used to further validate the effectiveness of the proposed method.

Radio Frequency Interference (RFI) can deteriorate imaging quality of low frequency band synthetic aperture radar (SAR). For SAR using Linear Frequency Modulation (LFM) signals, this paper proposes an RFI suppression method based on clutter estimation in range frequency domain. This method uses eigen-decomposition to complete RFI detection and gets the maximum likelihood estimation (MLE) of clutter intensity for imaging scene. After determining spectrum intervals of RFI signals by the judging method of threshold, our method uses wiener filter to filter the RFI signals in the range frequency domain. The processing results of L-band SAR data verify the effectiveness of our method.

This letter proposes a new method for multiband radar signal fusion by making use of all-phase fast Fourier transform (apFFT) algorithm and iterative adaptive approach (IAA). Central to the proposed method are, first, the mutual incoherence compensation between various subbands by apFFT algorithm, and second, the application of IAA to the mutually coherent subband measurements for signal fusion. Taking advantage of both algorithms, the proposed method effectively improves the range resolution with low sidelobes and performs robustly in the presence of noise. In particular, it requires no model information and therefore enables flexible implementation for practical application. The feasibility and effectiveness of the proposed algorithm are validated through both numerical simulations and raw data processing results.

In this paper, an estimation method of micromotion parameters for free rigid targets using micro-Doppler (mD) features is investigated. These parameters include spin rate, precession rate, nutation angle, and inertia ratio. They represent the microdynamic characteristics and intrinsic properties of targets. The time variation of mD frequency is found complicated yet valuable to estimate the micromotion parameters. From the viewpoint of the spectra of mixed mD time-frequency (TF) data sequences, the theoretical analysis and mathematical derivation are conducted in detail according to the scatterer distribution of rigid bodies. We then present an approach to realize the micromotion parameter estimation from radar mD echoes. It mainly consists of TF transform, TF image processing, mixed mD TF data sequence formation, and spectral estimation. Simulation experiments and result discussion are carried out to demonstrate the effectiveness of the proposed estimation method.

Radar micro-Doppler (mD) provides a promising approach to parameter estimation and classification of micro-dynamic targets. The micro-motion states and characteristics of instantaneous mD frequency are investigated to free symmetric rigid bodies. They are found to take the precession motion, and thus induce complicated mD features with non-sinusoidal variation. Based on theoretical analysis of the spectral structure of mD time-frequency (TF) sequence, this paper proposes a rotation parameter estimation method for complicated micro-motions. The estimation method includes TF analysis, spectrogram processing, projection mapping and spectral estimation. The spin and precession rates of micro-dynamic targets can then be extracted from their radar echoes. Finally, the effectiveness of the proposed method is verified by Monte-Carlo simulations and further discussion.