In radar detection, small and slow targets are easily overwhelmed by strong clutter. Traditional methods, such as singular value decomposition (SVD) and robust principal component analysis (RPCA), can suppress clutter and recover targets by using low-rank and sparse models. However, these methods rely on fixed prior information, which lacks adaptivity and suffers from unfavorable extensive manual hyperparameter tuning. To address these issues, a data-driven deep network model combined with an iterative algorithm called unfolding joint sparse recovery network (UFJSR-Net) is proposed to achieve the improved target detection performance. First, a joint sparse recovery (JSR) model is established and the fast iterative shrinkage/thresholding algorithm (FISTA) is derived to solve this model. Then, one iteration consisting of a linear operation and a nonlinear one can be recast into a single network layer and the stacking and combination of all layers will form the UFSJR-Net. Finally, the properties of the target and clutter can be learned by paired inputs and outputs training data to optimize the hyperparameters in the iterative algorithm to obtain the JSR model. Experiments on simulation data and measured radar data demonstrate that the proposed method exhibits advantages in detection performance over traditional decomposition methods under strong clutter with different intensities.

A novel approach for the gain-phase error calibration with a large-scale planar array is proposed in this letter. We show that minimizing the direction-of-arrival (DOA) estimation error is asymptotically equivalent to minimizing the sample second moment of the phase error. Therefore, with the mixing matrix obtained by blind signal separation, the 2-D DOA is estimated by finding the minimum point of a binary function with a large-scale planar array. Subsequently, the gain-phase error is obtained using the estimated DOAs. The proposed noniterative method requires neither prior calibration information nor the DOA of the signal source, and one incident signal is sufficient for the joint estimation of DOAs and gain-phase errors. Finally, the performance of the proposed approach is evaluated via numerical simulations.

Interrupted and multi-source track segment association (TSA) are two key challenges in target trajectory research within radar data processing. Traditional methods often rely on simplistic assumptions about target motion and statistical techniques for track association, leading to problems such as unrealistic assumptions, susceptibility to noise, and suboptimal performance limits. This study proposes a unified framework to address the challenges of associating interrupted and multi-source track segments by measuring trajectory similarity. We present TSA-cTFER, a novel network utilizing contrastive learning and TransFormer Encoder to accurately assess trajectory similarity through learned Representations by computing distances between high-dimensional feature vectors. Additionally, we tackle dynamic association scenarios with a two-stage online algorithm designed to manage tracks that appear or disappear at any time. This algorithm categorizes track pairs into easy and hard groups, employing tailored association strategies to achieve precise and robust associations in dynamic environments. Experimental results on real-world datasets demonstrate that our proposed TSA-cTFER network with the two-stage online algorithm outperforms existing methods, achieving 94.59% accuracy in interrupted track segment association tasks and 94.83% in multi-source track segment association tasks.

In radar and communication systems, coherently distributed (CD) sources are widespread due to multipath effects; however, online direction-of-arrival (DOA) estimation for CD sources has not been thoroughly investigated in consideration of array errors. In this letter, a central DOA estimator in the presence of gain-phase error is proposed. A new spatial spectrum function is constructed using the coupling relationship between the phase error and the central DOA obtained by blind signal separation. Subsequently, the spectrum ridge is removed to improve the angular resolution. The proposed noniterative algorithm requires neither prior error information for CD sources nor the prior angular distribution of CD sources and is not sensitive to the degree of gain-phase errors. Simulation results are given to demonstrate the superiority of the proposed algorithm over other algorithms.

Synthetic aperture radar (SAR) systems are highly sensitive to interference, making interference suppression a critical concern. While significant research has been conducted on SAR interference suppression using parametric, non-parametric, semi-parametric methods, and machine learning, these studies often neglect the impact of high-power interference, which can saturate SAR receivers due to their limited dynamic range. This saturation can lead to the model mismatch and performance degradation of many previously studied suppression methods. In this paper, we provide a detailed analysis of saturated interference in SAR systems. We develop time-domain saturation signal models for different types of interference, and analyze the frequency domain spurious characteristics of saturated interference. Based on this analysis, we propose novel interference suppression method adapted for saturated narrowband and wideband interference. We validate our methods using semi-actual data from Radarsat-1, demonstrating their effectiveness in suppressing both narrowband and wideband saturated interference. Besides, we also conducted analysis and verification using the real saturated interference data of Gaofen-3. The results confirm the proposed methods' capability to enhance SAR performance under saturation conditions.

Lakes play an important role in the water ecosystem on earth, and are vulnerable to climate change and human activities. Thus, the detection of water quality changes is of great significance for ecosystem assessment, disaster warning and water conservancy projects. In this paper, the dynamic changes of the Poyang Lake are monitored by Synthetic Aperture Radar (SAR). In order to extract water from SAR images to monitor water change, a water extraction algorithm composed of texture feature extraction, feature fusion and target segmentation was proposed. Firstly, the fractal dimension and lacunarity were calculated to construct the texture feature set of a water object. Then, an iterated function system (IFS) was constructed to fuse texture features into composite feature vectors. Finally, lake water was segmented by the multifractal spectrum method. Experimental results showed that the proposed algorithm accurately extracted water targets from SAR images of different regions and different imaging modes. Compared with common algorithms such as fuzzy C-means (FCM), the accuracy of the proposed algorithm is significantly improved, with an accuracy of over 98%. Moreover, the proposed algorithm can accurately segment complex coastlines with mountain shadow interference. In addition, the dynamic analysis of the changes of the water area of the Poyang Lake Basin was carried out with the local hydrological data. It showed that the extracted results of the algorithm in this paper are a good match with the hydrological data. This study provides an accurate monitoring method for lake water under complex backgrounds.