The problem of tracking multiple highly maneuverable targets in a distributed sensor network is addressed under constraints of limited field of view, computational capacity, and communication resources. First, a hybrid-driven labeled multi-Bernoulli (HDLMB) filter, driven by Gaussian processes and motion models, is proposed to track multiple highly maneuverable targets. Second, the local state estimates, rather than the local multitarget posterior densities, are fused by each node. This fusion strategy decouples distributed fusion from local estimates at individual nodes, aligning better with modular applications and reducing both fusion time and communication bandwidth. Finally, a suboptimal distributed fusion algorithm based on local track matching is developed. It is designed without the prerequisite of a known sensor field of view and effectively mitigates the NP-Hard problem associated with optimal matching while tracking multiple targets by multiple sensors. Numerical experiments have demonstrated that compared to advanced distributed fusion methods, the proposed approach achieves superior tracking accuracy and incurs lower fusion costs.

Super-resolution neural networks have recently achieved great progress in restoring high-quality remote sensing images at low zoom-in magnitude. However, these networks often struggle with challenges like shape distortion and blurring effects due to the severe absence of structure and texture details in large-factor remote sensing image super-resolution. Addressing these challenges, we propose a novel Two-Stage Spatial-Frequency Joint Learning Network (TSFNet). TSFNet innovatively merges insights from both spatial and frequency domains, enabling a progressive refinement of super-resolution results from coarse to fine. Specifically, different from existing frequency feature extraction approaches, we design a novel amplitude-guided-phase adaptive filter module to explicitly disentangle and sequentially recover both the global common image degradation and specific structural degradation in the frequency domain. Additionally, we introduce the cross-stage feature fusion design to enhance feature representation and selectively propagate useful information from stage one to stage two. Quantitative and qualitative experimental results demonstrate that our proposed method surpasses state-of-the-art techniques in large-factor remote sensing image super-resolution. Our code is available at https://github.com/likakakaka/TSFNet_RSISR.

Conventional chemical approaches could be limited in monitoring the concentration of pigments in plants in high volumes. To overcome these limitations, researchers often turn to non-invasive, high-throughput, and real-time monitoring techniques, such as spectroscopy and hyperspectral imaging, which allow for the assessment of pigment concentration in plants without the need for destructive sampling and offer the ability to monitor large volumes of plants efficiently. This research focused on the utilization of machine learning in conjunction with hyperspectral imaging to develop models for predicting the concentration of three pigments, namely chlorophylla, chlorophyll-b, and carotenoids, in tomato seedlings. The sample tomato seedlings were sourced from two distinct varieties: the wild type and the Long Hypocotyl-5-deficient (HY5) type. The spectral data were acquired using a near-infrared (NIR) camera with a spectral range spanning approximately 900-1700 nm. Machine learning algorithms such as partial least squares regression (PLSR) and extreme learning machine (ELM) were utilized to explore the latent relationship between hyperspectral information and chemical measurements. In addition, principal component analysis (PCA), independent component analysis (ICA), and competitive adaptive reweighted sampling (CARS) methods were used to extracted informative wavelengths from the reflectance spectrum. And a comprehensive analysis regarding to spectroscopy was conducted to investigate the validity and efficiency of the results of feature extraction. The ELM model demonstrated the highest effectiveness, achieving R2 values of 0.86, 0.83, and 0.83 for chlorophyll-a, chlorophyll-b, and carotenoids, respectively, on the test set. By integrating the predictive models with classifiers such as Logistic Regression, Support Vector Classifier (SVC), and K-nearest Neighbors (KNN), tomato seedlings were categorized into wild type and HY5 type. The findings showed that the proposed approach efficiently predicted the concentration of the pigments in tomato seedlings and explored the feasibility of using these results to identify tomato gene types.

This article develops a human-robot hybrid interaction system in order to involve both visual servoing task and human-robot interaction control tasks in one schema. A variable impedance strategy is proposed which is designed that the variable impedance parameter is adjusted by interaction behavior, so as to realize the target observation and sight wandering in visual tasks. The adaptive recurrent-neural-network-based(RNN-based) force estimator is adopted to estimate the human-robot interaction force in the sliding mode controller, in order to ensure the performance of the sliding mode controller of the robot. Experimental results verify the effectiveness of the proposed force-estimator-based control and adaptive variable impedance regulation in physical human-robot interaction tasks.

In recent years, the mining industry has encountered challenges, such as a shortage of human resources, an ongoing emphasis on safety enhancements, and increased ecological preservation requirements. Autonomous mining trucks have emerged as a novel solution to effectively address these issues within open-pit mining operations. To meet the demanding conditions of open-pit mines, characterized by intense vibrations and extreme temperature variations, hybrid solid-state LiDAR has emerged as the primary choice for perception sensors. Recognizing the distinct data structure and distribution disparities between point clouds obtained through nonrepetitive scanning methods of hybrid solid-state LiDAR and traditional mechanical LiDAR, this paper proposed an innovative LiDAR 3D object detection model, PointPillars-HSL (PointPillars-Hybrid Solid-state LiDAR). This approach harmonizes the unique characteristics of open-pit mining environments and hybrid solid-state LiDAR point clouds. It optimizes the model's preprocessing methodology, augments the dimensionality of pillar features, fine-tunes the loss function, and employs transfer learning techniques to reduce the reliance on specific datasets. The result is the effective deployment of a 3D object detection algorithm customized for hybrid solid-state LiDAR within the specific operational framework of open-pit mining. This achievement has yielded a noteworthy overall vehicle recognition rate of 89.72%.

The mega-constellation is a major future development direction for space-based technologies in communications, navigation,remote sensing, and other fields. However, there are marked security threats to the mega-constellation. Traditional password-based security protection techniques are inefficient for vast node access authentication because they lack a unified management system and methodology. To address the aforementioned issues, this work presents a mega-constellation node security access authentication technique based on sharding blockchain via the “1 + N + 1” mega-constellation security and trustworthiness architecture. We build a distributed node security access authentication system based on functional domains and functional cross-domains, and we develop mathematical models for the complexity of messaging and space, the throughput of transactions, and the overall estimation of sharding blockchain systems. The results demonstrate that every indicator outperforms conventional blockchain techniques, which has major implications for mega-constellation by creating a complete link security and trustworthiness system. A universal solution for the number of consensus nodes I and the number of shards N is found, which can be used to guide parameter design in mega-constellation sharding blockchain systems.

Abstract A well‐liked maneuvering target tracking algorithm is a variable structure multi‐model (VSMM). One of the crucial elements determining the tracking effect is the successful model set adaptation (MSA). The ability to further enhance tracking accuracy for the conventional VSMM method is constrained by the absence of a mechanism to thoroughly utilise observation and tracking data to optimise MSA. We incorporate the Reinforcement learning (RL) approach into the MSA procedure to address this issue and provide a VSMM algorithm based on Monte Carlo (MC) learning. To formulate the challenge of optimising the number of effective models as a RL problem, we first used the prediction error, the number of effective model sets, and tracking accuracy to build the models of the appropriate state space, decision space, and reward. The number of efficient models was optimised using MC learning, and the entire VSMM algorithm was then created. The proposed approach was compared to the simulated experiment's five maneuvering target tracking algorithms. The outcomes demonstrate that the suggested algorithm has a lower computation scale and accurate tracking accuracy.

The performance of existing maneuvering target tracking methods for highly maneuvering targets in cluttered environments is unsatisfactory. This paper proposes a hybrid-driven approach for tracking multiple highly maneuvering targets, leveraging the advantages of both data-driven and model-based algorithms. The time-varying constant velocity model is integrated into the Gaussian process (GP) of online learning to improve the performance of GP prediction. This integration is further combined with a generalized probabilistic data association algorithm to realize multi-target tracking. Through the simulations, it has been demonstrated that the hybrid-driven approach exhibits significant performance improvements in comparison with widely used algorithms such as the interactive multi-model method and the data-driven GP motion tracker.

Spaceborne multichannel synthetic aperture radar (SAR) is an effective means to realize high-resolution and wide-swath imaging. However, for spaceborne multichannel SAR imaging of maritime moving targets, the target motion will cause undesired channel imbalance, i.e., phase error, and further introduce the spurious targets in the image. To solve this problem, this article proposes a novel spaceborne multichannel SAR imaging algorithm for maritime moving targets, which consists of sequential coarse imaging and accurate imaging. The key strategies are to separate different moving targets by coarse imaging and to estimate the phase error based on the relationship between phase errors and amplitudes of spurious targets. First, the quantitative relationship between phase errors and amplitudes of spurious targets is established. Second, based on coarse imaging results, different maritime targets are effectively separated. Then, based on the measured amplitudes of spurious targets, a cost function, which represents the difference between the real target velocity and estimated target velocity, is constructed and minimized to separately estimate the velocities and phase errors of targets. Moreover, to further improve the accuracy of estimation and to suppress the undesired effects caused by target defocusing, clutter, and noise, an iterative strategy is adopted. Finally, by autofocusing, a well-focused SAR image is obtained. The GF-3 dual-channel real data experiment is conducted. The results indicate that the spurious targets are well suppressed, which validates the effectiveness of the proposed algorithm.

Introduction Pinus elliottii × P. caribaea is one of the major tree species in commercial forest bases in developed countries. However, in the process of sapling cultivation, nutrients cannot be accurately detected and supplied to individual saplings, resulting in reduced yield and quality. Methods In this paper, visible-near-infrared (Vis-NIR) hyperspectral imaging (HSI) combined with ensemble learning (EL) was used to solve this problem. The content and distribution of nitrogen (N), phosphorus (P), and potassium (K) in the canopy needles of Pinus elliottii × P. caribaea saplings were obtained through HSI data analysis, and the nutritional needs of individual plants were reflected to provide a basis for nutritional supply decisions. The saplings were treated with deficient, sufficient, and excessive N, P, and K single-element fertilizers. After collecting the Vis-NIR hyperspectral images of these saplings, a variety of pre-processing, feature selection, and ensemble learning algorithms were used to establish predictive models. The R 2 and RMSE were used to evaluate the performance of the prediction models. Results The results showed that the multiple scattering correction-competitive adaptive reweighted sampling-Stacking (MSC-CARS-Stacking) model had the best results among the three nutrient elements prediction models (Rp 2 -N = 0.833, RMSEP = 0.380; Rp 2 -P = 0.622, RMSEP = 0.101; Rp 2 -K = 0.697, RMSEP = 0.523). When studying the sensitive bands of N, P, and K, we found that the common characteristic wavelengths were 675.3 and 923.9 nm, while the non-common characteristic wavelengths were located at 550 nm (green peak), 680 nm (red valley), and 960 nm (water peak). In studying the generalization ability of the model, only the nitrogen group data were used to train the MSC-CARS-Stacking model for nitrogen prediction, which was then used to predict the nitrogen content in the phosphorus and potassium groups, obtaining good results (Rc 2 -N = 0.841, Rp 2 -P = 0.814, Rp 2 -K = 0.801). It showed a strong generalization ability for the prediction of nitrogen, and similarly, phosphorus and potassium. Discussion In conclusion, this study verifies that the Vis-NIR HSI combined with EL is indeed a reliable and stable method to predict the contents of N, P, and K in the needles of Pinus elliottii × P. caribaea sapling canopy.

In the complex countermeasure environment, the pulse description words (PDWs) of the same type of multi-function radar emitters are similar in multiple dimensions. Therefore, it is difficult for conventional methods to deinterleave such emitters. In order to solve this problem, a pulse deinterleaving method based on implicit features is proposed in this paper. The proposed method introduces long short-term memory (LSTM) neural networks and statistical analysis to mine new features from similar PDW features, that is, the variation law (implicit features) of pulse sequences of different radiation sources over time. The multi-function radar emitter is deinterleaved based on the pulse sequence variation law. Statistical results show that the proposed method not only achieves satisfactory performance, but also has good robustness.

To address the challenge of risk minimization associated with investors’ irrational investment decisions in the face of wealth fluctuations, this article integrates prospect theory and disappointment theory into the framework of multi-objective portfolio selection. By concurrently incorporating prospect theory and disappointment theory, this conceptual framework not only considers emotional factors but also offers a comprehensive depiction of individuals’ decision-making processes amidst uncertainty. The proposed portfolio selection model aims to balance return and risk by maximizing both expected and total utility. The effectiveness of the proposed model is validated through comparisons with three other methods.

Intent perception is a novel task that aims to understand the intention of images, regular classification methods usually perform unsatisfactorily on intent perception due to the semantic ambiguity problem, i.e. the intra-class variety problem in which images of the same intent class may contain objects of different semantic categories and the inter-class confusion problem in which images of different intent classes may contain objects of similar semantic categories. To address this problem, this paper introduces prototype learning into the intent perception and proposes a unified framework named PIP-Net to reduce the influence of semantic ambiguity. Specifically, for each intent class, we first filter semantic ambiguity samples which are far away from the cluster center. Then we use features of the filtered samples to generate prototypes via clustering algorithm. Besides, we enhance the diversity between prototypes of different classes to better handle the inter-class confusion problem. To update the prototypes in the training process, we introduce a global matching algorithm to holistically match each feature with class prototypes, and use the momentum update strategy to stably update prototypes. Experimental results on the Intentonomy dataset demonstrate that our method can consistently outperform the traditional classification paradigm in multiple baseline models, and verify the effectiveness of our proposed prototype learning paradigm in addressing the intent perception problem. Our proposed PIP-Net achieves a new state-of-the-art performance on Intentonomy, including Macro F1 score of 31.57% and averaging F1 score of 41.85%.

Litchi is one of the most common economic fruits in southern China, however, the growth of late-autumn shoots of litchi hinders flower bud differentiation and reduces yield of fruit. The early identification of the late-autumn shoots is of great significance for orchard management to control shoots and then increase fruit yield. At present, the identification of late-autumn shoots still relies on manual methods, which is not suitable for smart orchard management in a large area due to low recognition efficiency and high subjectivity. Therefore, a convenient, fast and cost-effective method is urgently needed. In response to this problem, the paper proposes a method based on the combination of unmanned aerial vehicle (UAV) remote sensing and object detection algorithm to detect late-autumn shoots. For this purpose, a remote sensing dataset of late-autumn shoots of litchi is first constructed by UAV. An improved YOLOv5 algorithm called YOLOv5-SBiC is then developed for late-autumn shoots identification. In the YOLOv5-SBiC algorithm, the transformer module is introduced to speed up the convergence of the network and improve detection accuracy, the attention mechanism module is employed to help the model extracting details, and BiFPN is used to better solve the multi-scale problem in detecting and then improve the recognition effect of small-sized objects. In addition, CIOU is selected as the loss function of bounding boxes regression to achieve high-precision localization of the boxes. The test results demonstrate that the recognition accuracy of YOLOv5-SBiC reaches a relatively high value of 79.6%, which is 4.0% higher than that (75.6%) of the original YOLOv5 algorithm and 15.9% higher than that (63.7%) of the pure transformer algorithm. It's also demonstrated that YOLOv5-SBiC is more competitive than the mainstream target detection algorithms in the dataset of late-autumn shoots.

The signal-in-space (SIS) anomaly of BeiDou navigation satellite system (BDS) is an important factor affecting its high accuracy SIS quality assessment. Detecting and eliminating SIS anomaly is not only an important method to build SIS fault model of BDS, but also helps to guarantee the integrity of BDS navigation and positioning. Based on the problem that the traditional empirical threshold method cannot accurately identify the start and end times of anomalies in anomaly detection, which leads to anomaly detection leakage, a combined detection method based on autoregressive distributed lag model and empirical threshold is proposed in this paper. Before the calculation, the spurious anomalies of SIS are removed by data cleaning. The high-precision SIS ranging error (SISRE) is recovered by Space State Representation (SSR) correction number, and then projected to the user's line of sight direction, and the anomaly detection threshold is determined by using the combined threshold of empirical threshold and autoregressive distributed lag (ARDL) model. The feasibility and effectiveness of the method were analyzed by using the data collected in 2021. The test results show that compared with the traditional threshold method, the proposed method can more accurately detect the start and end points of SIS anomalies caused by clock anomalies, thereby improving the detection accuracy. In addition, the anomaly detection method proposed in this paper is used to count the anomalies throughout the year, and the results show that the highest frequency of anomalies is found in geostationary orbit (GEO) and inclined geosynchronous orbit (IGSO), and these anomalies are mainly caused by satellite clocks.

地基分布式雷达是由多部单元雷达分置部署、协同工作实现目标探测的雷达系统,部署在地基平台,照射源除自身还可用合作、非合作跨平台照射源,各单元雷达间可信号级相参融合.传统集中式雷达在复杂场景中难以探测识别低可观测目标,分布式雷达有望解决上述瓶颈问题,并实现侦干探通一体化,因此分布式相参雷达体制是未来雷达形态发展的重要方向之一.分布式雷达体系复杂,涉及维度、要素多,须研究探索合理有效的工作范式.本文系统介绍了地基分布式相参雷达发展的新趋势和新技术,通过信号级融合处理,可形成大口径稀疏阵列,获得极窄的高增益波束,可采用更多更先进的信号处理算法探测和精确定位目标.本文介绍该领域最新的研究进展和代表性成果,围绕分布式相参增程探测、分布式相参成像、分布式相参抗干扰、分布式系统同步校准等四个方面的关键问题和技术难点展开论述,并对分布式雷达体制的前景进行了展望.

The paper designs a peripheral maximum gray difference (PMGD) image segmentation method, a connected-component labeling (CCL) algorithm based on dynamic run length (DRL), and a real-time implementation streaming processor for DRL-CCL. And it verifies the function and performance in space target monitoring scene by the carrying experiment of Tianzhou-3 cargo spacecraft (TZ-3). The PMGD image segmentation method can segment the image into highly discrete and simple point targets quickly, which reduces the generation of equivalences greatly and improves the real-time performance for DRL-CCL. Through parallel pipeline design, the storage of the streaming processor is optimized by 55% with no need for external memory, the logic is optimized by 60%, and the energy efficiency ratio is 12 times than that of the graphics processing unit, 62 times than that of the digital signal proccessing, and 147 times than that of personal computers. Analyzing the results of 8756 images completed on-orbit, the speed is up to 5.88 FPS and the target detection rate is 100%. Our algorithm and implementation method meet the requirements of lightweight, high real-time, strong robustness, full-time, and stable operation in space irradiation environment.

面向变后掠翼飞行器在跨速域、变体过程中飞行稳定和准确跟踪需求,提出一种基于干扰补偿的跨速域变后掠翼飞行器跟踪控制方法.考虑到变体过程对飞行器气动、结构参数的影响,将变体附加力、力矩和系统不确定性视为系统未知干扰,建立变后掠翼飞行器多体动力学模型.设计非线性干扰观测器,实现对未知干扰的准确估计.定制二阶指令滤波辅助系统补偿状态约束、输入饱和影响,设计了基于干扰观测补偿的指令滤波跟踪控制器,并基于Lyapunov稳定性定理分析了所设计控制器的稳定性.通过仿真实验结果可知,所提控制算法可以保证跨速域变后掠翼飞行器对参考高度信号的准确跟踪,高度信号的稳态跟踪误差不大于0.1 m,随机参数扰动下的蒙特卡洛仿真跟踪误差不大于0.5 m,验证了所提方法的有效性和鲁棒性.所提方法在跨速域变体飞行器控制领域具有一定的工程实用性和应用前景.