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The target and background will change continuously in the long-term tracking process, which brings great challenges to the accurate prediction of targets. The correlation filter algorithm based on manual features is difficult to meet the actual needs due to its limited feature representation ability. Thus, to improve the tracking performance and robustness, an improved hierarchical convolutional features model is proposed into a correlation filter framework for visual object tracking. First, the objective function is designed by lasso regression modeling, and a sparse, time-series low-rank filter is learned to increase the interpretability of the model. Second, the features of the last layer and the second pool layer of the convolutional neural network are extracted to realize the target position prediction from coarse to fine. In addition, using the filters learned from the first frame and the current frame to calculate the response maps, respectively, the target position is obtained by finding the maximum response value in the response map. The filter model is updated only when these two maximum responses meet the threshold condition. The proposed tracker is evaluated by simulation analysis on TC-128/OTB2015 benchmarks including more than 100 video sequences. Extensive experiments demonstrate that the proposed tracker achieves competitive performance against state-of-the-art trackers. The distance precision rate and overlap success rate of the proposed algorithm on OTB2015 are 0.829 and 0.695, respectively. The proposed algorithm effectively solves the long-term object tracking problem in complex scenes.

Recently, deep learning (DL) has been successfully applied in automatic target recognition (ATR) tasks of synthetic aperture radar (SAR) images. However, limited by the lack of SAR image target datasets and the high cost of labeling, these existing DL based approaches can only accurately recognize the target in the training dataset. Therefore, high precision identification of unknown SAR targets in practical applications is one of the important capabilities that the SAR–ATR system should equip. To this end, we propose a novel DL based identification method for unknown SAR targets with joint discrimination. First of all, the feature extraction network (FEN) trained on a limited dataset is used to extract the SAR target features, and then the unknown targets are roughly identified from the known targets by computing the Kullback–Leibler divergence (KLD) of the target feature vectors. For the targets that cannot be distinguished by KLD, their feature vectors perform t-distributed stochastic neighbor embedding (t-SNE) dimensionality reduction processing to calculate the relative position angle (RPA). Finally, the known and unknown targets are finely identified based on RPA. Experimental results conducted on the MSTAR dataset demonstrate that the proposed method can achieve higher identification accuracy of unknown SAR targets than existing methods while maintaining high recognition accuracy of known targets.

The recent emergence of high-resolution Synthetic Aperture Radar (SAR) images leads to massive amounts of data. In order to segment these big remotely sensed data in an acceptable time frame, more and more segmentation algorithms based on deep learning attempt to take superpixels as processing units. However, the over-segmented images become non-Euclidean structure data that traditional deep Convolutional Neural Networks (CNN) cannot directly process. Here, we propose a novel Attention Graph Convolution Network (AGCN) to perform superpixel-wise segmentation in big SAR imagery data. AGCN consists of an attention mechanism layer and Graph Convolution Networks (GCN). GCN can operate on graph-structure data by generalizing convolutions to the graph domain and have been successfully applied in tasks such as node classification. The attention mechanism layer is introduced to guide the graph convolution layers to focus on the most relevant nodes in order to make decisions by specifying different coefficients to different nodes in a neighbourhood. The attention layer is located before the convolution layers, and noisy information from the neighbouring nodes has less negative influence on the attention coefficients. Quantified experiments on two airborne SAR image datasets prove that the proposed method outperforms the other state-of-the-art segmentation approaches. Its computation time is also far less than the current mainstream pixel-level semantic segmentation networks.

Synthetic aperture radar automatic target recognition (SAR-ATR) has made great progress in recent years. Most of the established recognition methods are supervised, which have strong dependence on image labels. However, obtaining the labels of radar images is expensive and time-consuming. In this paper, we present a semi-supervised learning method that is based on the standard deep convolutional generative adversarial networks (DCGANs). We double the discriminator that is used in DCGANs and utilize the two discriminators for joint training. In this process, we introduce a noisy data learning theory to reduce the negative impact of the incorrectly labeled samples on the performance of the networks. We replace the last layer of the classic discriminators with the standard softmax function to output a vector of class probabilities so that we can recognize multiple objects. We subsequently modify the loss function in order to adapt to the revised network structure. In our model, the two discriminators share the same generator, and we take the average value of them when computing the loss function of the generator, which can improve the training stability of DCGANs to some extent. We also utilize images of higher quality from the generated images for training in order to improve the performance of the networks. Our method has achieved state-of-the-art results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, and we have proved that using the generated images to train the networks can improve the recognition accuracy with a small number of labeled samples.

Accurate track segment association plays an important role in modern sensor data processing systems to ensure the temporal and spatial consistency of target information. Traditional methods face a series of challenges in association accuracy when handling complex scenarios involving short tracks or multi-target intersections. This study proposes an intelligent association method that includes a multi-dimensional track data preprocessing algorithm and the characteristic-aware attention long short-term memory (CA-LSTM) network. The algorithm can segment and temporally align track segments containing multi-dimensional characteristics. The CA-LSTM model is built to perform track segment association and has two basic parts. One part focuses on the target characteristic dimension and utilizes the separation and importance evaluation of physical characteristics to make association decisions. The other part focuses on the time dimension, matching the application scenarios of short, medium and long tracks by obtaining the temporal characteristics of different time spans. The method is verified on a multi-source track association dataset. Experimental results show that association accuracy rate is 85.19% for short-range track segments and 96.97% for long-range track segments. Compared with the typical traditional method LSTM, this method has a 9.89% improvement in accuracy on short tracks.

Aiming at maximizing waveform diversity gain when designing a phase-coded multiple-input multiple-output (MIMO) radar waveform set, it is desirable that all waveforms are orthogonal to each other. Hence, the lowest possible peak cross-correlation ratio (PCCR) is expected. Meanwhile, low peak auto-correlation side-lobe ratio (PASR) is needed for good detection performance. However, it is difficult to obtain a closed form solution to the waveform set from the expected values of the PASR and PCCR. In this paper, the waveform set design problem is modeled as a multi-objective, NP-hard constrained optimization problem. Unlike conventional approaches that design the waveform set through optimizing a weighted sum objective function, the proposed optimization model evaluates the performance of multi-objective functions based on Pareto level and obtains a set of Pareto non-dominated solutions. That means that the MIMO radar system can trade off each objective function for different requirements. To solve this problem, this paper presents a multi-objective quantum genetic algorithm (MoQGA) based on the framework of quantum genetic algorithm. A new population update strategy for the MoQGA is designed based on the proposed model. Compared to the state-of-the-art methods, like BiST and Multi-CAN, the PASR and PCCR metrics of the waveform set are 0.95–3.91 dB lower with the parameters of the numerical simulation. The MoQGA is able to minimize PASR and PCCR of the MIMO radar waveform set simultaneously.

Distributed radar networks have emerged as a key technology in remote sensing and surveillance due to their high transmission power and robustness against node failures. When performing coherent beamforming with multiple radars, frequency and phase deviations introduced by independent oscillators lead to a decrease in transmission power. This paper proposes an optimized diffuse Kalman filter (ODKF) for the frequency and phase synchronization. Specifically, each radar locally estimates its frequency and phase, then shares this information with neighboring nodes, which are used for incremental update and diffusion update to adjust local estimates. To further reduce synchronization errors, we incorporate a self-feedback strategy in the diffusion step, in which each node balances its own estimate with neighbor information by optimizing the diagonal weights in the diffusion matrix. Numerical simulations demonstrate the superior performance of the proposed method in terms of mean squared deviation (MSD) and convergence speed.

This paper presents a new group target tracking method based on the standard multi-sensor multi-target multi-Bernoulli (MS-MeMBer) filter. In the prediction step, the group structure is used to constrain the movement of the constituent members within the respective groups. Specifically, the group of members is considered as an undirected random graph. Combined with the virtual leader-follower model, the motion equation of the members within groups is formulated. In the update step, the partitioning problem of multiple sensors is transformed into a multi-dimensional assignment (MDA) problem. Compared with the original two-step greedy partitioning mechanism, the MDA algorithm achieves better measurement partitions in group target tracking scenarios. To evaluate the performance of the proposed method, a simulation scenario including group splitting and merging is established. Results show that, compared with the standard MS-MeMBer filter, our method can effectively estimate the cardinality of members and groups at the cost of increasing computational load. The filtering accuracy of the proposed method outperforms that of the MS-MeMBer filter.

Ischemic stroke poses significant challenges in terms of mortality and disability rates globally. A key obstacle to the successful treatment of ischemic stroke lies in the limited efficacy of administering therapeutic agents. Leveraging the unique properties of nanoparticles for brain targeting and crossing the blood–brain barrier, researchers have engineered diverse nanoparticle-based drug delivery systems to improve the therapeutic outcomes of ischemic stroke. This review provides a concise overview of the pathophysiological mechanisms implicated in ischemic stroke, encompassing oxidative stress, glutamate excitotoxicity, neuroinflammation, and cell death, to elucidate potential targets for nanoparticle-based drug delivery systems. Furthermore, the review outlines the classification of nanoparticle-based drug delivery systems according to these distinct physiological processes. This categorization aids in identifying the attributes and commonalities of nanoparticles that target specific pathophysiological pathways in ischemic stroke, thereby facilitating the advancement of nanomedicine development. The review discusses the potential benefits and existing challenges associated with employing nanoparticles in the treatment of ischemic stroke, offering new perspectives on designing efficacious nanoparticles to enhance ischemic stroke treatment outcomes. Graphical Abstract

In this paper, an improved differential evolution (DE) algorithm with the successful-parent-selecting (SPS) framework, named SPS-JADE, is applied to the pattern synthesis of linear antenna arrays. Here, the pattern synthesis of the linear antenna arrays is viewed as an optimization problem with excitation amplitudes being the optimization variables and attaining sidelobe suppression and null depth being the optimization objectives. For this optimization problem, an improved DE algorithm named JADE is introduced, and the SPS framework is used to solve the stagnation problem of the DE algorithm, which further improves the DE algorithm’s performance. Finally, the combined SPS-JADE algorithm is verified in simulation experiments of the pattern synthesis of an antenna array, and the results are compared with those obtained by other state-of-the-art random optimization algorithms. The results demonstrate that the proposed SPS-JADE algorithm is superior to other algorithms in the pattern synthesis performance with a lower sidelobe level and a more satisfactory null depth under the constraint of beamwidth requirement.