This paper proposes a hybrid ensemble classifier with denoising autoencoder (ECDAE) framework to address reliability and robustness challenges in cooperative spectrum sensing (CSS) for cognitive radio networks (CRNs). The proposed framework first employs an ensemble classifier (EC) to dynamically reconfigure the sensing time, optimizing performance while minimizing cost. The EC accurately estimates the sensing samples based on target detection probabilities, false alarm rates, and channel conditions. Subsequently, a denoising autoencoder (DAE) eliminates soft-combined energies from false-sensing users (FSUs) before soft fusion. The results show that EC surpasses other methods, including random forest (RFC), neural networks (NN), decision trees (DT), k-nearest neighbors (KNN), Gaussian naive Bayes (GNB), RUSBoost and XGBoost, achieving an F1 score of 99.23%, an accuracy of 99.78%, and a Matthews correlation coefficient (MCC) of 99.6%. Furthermore, optimized sensing time through EC is combined with DAE reconstruction delivers superior sensing performance at the fusion center (FC) producing low error probabilities compared to traditional schemes such as identical gain combination (IGS), highest gain combination (HGS), particle swarm optimization (PSO), differential evolution-based machine learning (DE-ML) and convolutional neural networks (CNN). On average, the ECDAE framework achieves a 99.4% and 98.1% reduction in error probability compared to traditional schemes (IGS, HGS) and a 92.7% to 97.2% reduction compared to advanced methods (PSO, DE-ML, CNN), across all tested SNR conditions and false-sensing attack scenarios. The framework maintains robustness across four distinct false-sensing scenarios: (1) no false sensing (NFS) reporting an low-energy signals, (2) yes false sensing (YFS) reporting consistently an always high-energy signals, (3) opposite false sensing (OFS) reporting an always invert decisions to the true energy states, and (4) yes/no false sensing (YNFS) where FSU randomly alternates between YFS and NFS - ensuring minimal error probabilities in global decisions.

In cognitive radio networks (CRNs), collaborative spectrum sensing has emerged as a promising technique for detecting primary user activity. However, the effectiveness of user cooperation is compromised by the presence of malicious users, specifically False Sensing Users (FSUs). FSUs undermine the effectiveness of collaborative sensing by providing misleading information to the fusion center (FC) in an attempt to selfishly access spectrum resources. Therefore, this study focuses on three types of FSUs that exhibit distinct attack patterns: No False Sensing (NFS, i.e., Always-No), Yes False Sensing (YFS), and Yes/No false sensing (YNFS) users. The FC collects reports from both FSUs and legitimate sensing users at varying time intervals. This study employs a denoising autoencoder (DAE) to enhance sensing reliability by mitigating the effects of abnormal sensing reports and noise disturbances at the FC. While current validation employs synthetic data that closely approximates theoretical CRN conditions, real-world RF validation represents an important direction for future work. The autoencoder produces cleaned soft energy data, which is fed into a machine learning (ML) classifier to estimate channel availability and accumulate global decisions. The present study assesses the effectiveness of various classification techniques, including decision trees (DT), k nearest neighbor (KNN), neural networks (NN), ensemble classification (EC), Gaussian naive Bayes (GNB), and random forest classifier (RFC), to classify channel states. Additionally, this paper aims to provide a comprehensive evaluation of these methods. The integration of DAE and EC yields high accuracy, F1 score, and Matthew’s Correlation Coefficient (MCC), leading to a reliable global decision at the FC with minimal sensing error.

This paper presents a novel federated signal processing framework for multi-target tracking in distributed radar/sonar systems. Each sensor node independently tracks targets using an Extended Kalman Filter (EKF) that processes nonlinear range–bearing measurements, thereby generating refined state estimates and uncertainty covariances without transmitting raw sensor data. Instead, only these processed outputs—augmented by dynamically updated trust factors—are communicated to a central fusion center. The fusion center aggregates the local estimates via an inverse-covariance weighting scheme augmented by adaptive trust weights, yielding globally consistent target tracks with improved accuracy and robustness. Simulation results demonstrate that the proposed federated EKF approach significantly reduces communication overhead while maintaining high tracking performance under heterogeneous sensor conditions.

The rapid expansion of wireless communications has led to increasing demand and interference in the electromagnetic spectrum, raising the question of how to achieve reliable and adaptive monitoring in complex and dynamic environments. This study aims to investigate whether groups of unmanned aerial vehicles (UAVs) can provide an effective alternative to conventional, static spectrum monitoring systems. We propose a cooperative monitoring system in which multiple UAVs, integrated with software-defined radios (SDRs), conduct energy measurements and share their observations with a data fusion center. The fusion process is based on Dempster-Shafer theory (DST), which models uncertainty and combines partial or conflicting data from spatially distributed sensors. A simulation environment developed in MATLAB emulates UAV mobility, communication delays, and propagation effects in various swarm formations and environmental conditions. The results confirm that cooperative spectrum monitoring using UAVs with DST data fusion improves detection robustness and reduces susceptibility to noise and interference compared to single-sensor approaches. Even under challenging propagation conditions, the system maintains reliable performance, and DST fusion provides decision-supporting results. The proposed methodology demonstrates that UAV groups can serve as scalable, adaptive tools for real-time spectrum monitoring and contributes to the development of intelligent monitoring architectures in cognitive radio networks.

A response-threshold method for early cancer diagnosis and monitoring via swarms of nanorobots.

ABSTRACT This paper presents a distributed estimation framework for nonlinear systems over bandwidth‐limited sensor networks. Each node encodes its measurement innovation via multi‐level quantization (MLQ) and transmits a finite‐bit codeword, while the fusion center performs a quantization‐aware approximate minimum mean‐square error (MMSE) update and combines local posteriors using a cross‐covariance–free convex rule. We analyze the asymptotic behavior of quantization terms and derive a sufficient condition ensuring uniform boundedness of the fused error covariance, explicitly linking stability to quantizer resolution and system dynamics. The proposed method achieves low communication cost, local complexity, and scalability with the number of nodes. Simulation studies on maneuvering‐target tracking show consistent improvements over EKF, CI, and CKF across RMSE, IAE, ISE, ITAE, and ITSE metrics under matched bit budgets, and demonstrate robustness to noise and packet loss. The approach is well suited to real‐time control‐oriented applications such as UAV coordination, autonomous vehicles, and smart‐grid monitoring.

This paper investigates the joint state and fault estimation problem for a class of nonlinear systems subject to both measurement censoring (MC) and random missing measurements (MMs). Recognizing that state estimation for nonlinear systems in complex environments is frequently compromised by MMs, MC phenomena, and actuator faults, a novel joint estimation framework that integrates improved Tobit Kalman filtering and federated fusion is proposed, enabling simultaneous robust estimation of system states and fault signals. Among them, the Tobit measurement model is introduced to characterize the phenomenon of MC, a set of Bernoulli random variables is used to describe the MM phenomenon and common actuator faults (abrupt and ramp faults) are considered. In the fusion estimation stage, each sensor transmits observations to the local estimator for preliminary estimation, then sends the local estimated values to the fusion center for generating fusion estimates. The local filtering error covariance is ensured and the upper bound is minimized by reasonably determining the filter gain, while the fusion center performs fusion estimation based on the federated fusion criterion. In addition, this paper proves the boundedness of the filtering error of the designed estimator under certain conditions. Finally, the effectiveness of the estimation framework is demonstrated through two engineering experiments.

Radar networks, composed of multiple radar stations and a fusion center interconnected via communication technologies, are widely used in civil aviation and maritime operations. Ensuring the security of radar networks is crucial. While their strong anti-jamming capabilities make traditional electronic countermeasures less effective, the openness and vulnerability of their network architecture expose them to cybersecurity risks. Current research on radar network security risk analysis from a cybersecurity perspective remains insufficient, necessitating further study to provide theoretical support for defense strategies. Taking centralized radar networks as an example, this paper first analyzes their architecture and potential cybersecurity risks, identifying a threat where attackers could potentially execute false data injection attacks (FDIAs) against the fusion center via man-in-the-middle attacks (MITMAs). A threat model is then established, outlining possible attack procedures and methods, along with defensive recommendations and evaluation metrics. Furthermore, for scenarios involving single-link control without traffic increase, the impact of different false data construction methods is examined. Simulation experiments validate the findings, showing that the average position offset increases from 8.38 m to 78.35 m after false data injection. This result confirms significant security risks under such threats, providing a reference for future countermeasure research.

Liquid Nitrogen Superconducting Test Facility at the MIT Plasma Science and Fusion Center

Indonesia's intelligence cycle has historically relied on a classical model characterized by linearity, operational closure, and limited adaptability to contemporary threat complexity and information technology advancements. This model demonstrates significant limitations in facilitating essential interinstitutional collaboration and information integration. This study reconstructs the classical framework into the Collaborative Intelligence Model—a cyclical paradigm grounded in collaboration and intelligence product inclusiveness. Employing a qualitative descriptive approach with model reconstruction, data were gathered via in-depth interviews and analysis of intelligence regulations and practices, both within Indonesia and internationally. Findings reveal a model structured around four core elements—Planning/Direction, Access/Collect, Analyze/Elucidate, and Store/Memory—integrated within a Collaboration Space. Key supporting mechanisms include the Secrecy & Trust principle, robust internal and external monitoring systems, and a Fusion Center serving as an integrated analytical hub. The model further necessitates a collaborative work culture, performance-based incentives, and safeguards for civil rights and data security. This research outlines a three-stage transformation roadmap toward inclusive, adaptive, and accountable intelligence governance. The Collaborative Intelligence Model offers a strategic solution for addressing modern national security challenges, prioritizing cross-sector cooperation and measurable public participation.

This paper addresses the problem of target localization in a distributed waveform diverse array radar system, exploiting the technique of sparse reconstruction. At the configuration stage, the distributed radar system consists of two individual Frequency Diverse Array Multiple-Input Multiple-Output (FDA-MIMO) radars and one single Element-Pulse Coding MIMO (EPC-MIMO) radar. To obtain the angle and incremental range (i.e., the range offset between the sampling point and actual position within the range bin) of the targets in each local radar, two sparse reconstruction-based algorithms, including the grid-based Iterative Adaptive Approach (IAA) and gridless Atomic Norm Minimization (ANM) algorithms, are implemented. Furthermore, multiple sets of local statistics are fused at the fusion center, where a Weighted Least Squares (WLS) method is performed to localize targets. At the analysis stage, the estimation performance of the proposed methods, encompassing both IAA and ANM algorithms, is evaluated in contrast to the Cramér–Rao Bound (CRB). Numerical results and parametric studies are provided to demonstrate the effectiveness of the proposed sparse reconstruction methods for target localization in the distributed waveform diverse array system.

Multi-dimensional classification (MDC), in which the training data are concurrently associated with numerous label variables across many dimensions, has garnered significant interest recently. Most of the current MDC methods are based on the framework of supervised learning, which induces a predictive model from a large amount of precisely labeled data. So, they are challenged to obtain satisfactory learning results in the situation where the training data are not annotated with precise labels but assigned with ambiguous labels. Besides, the current MDC algorithms only consider the scenario of centralized learning, where all training data are handled at a single node for the purpose of classifier induction. However, in some real applications, the training data are not consolidated at a single fusion center, but rather are dispersedly distributed among multiple nodes. In this study, we focus on the problem of decentralized classification involving partial multi-dimensional data that have partially accessible candidate labels, and develop a distributed method called dPL-MDC for learning with these partial labels. In this algorithm, we conduct one-vs.-one decomposition on the originally heterogeneous multi-dimensional output space, such that the problem of partial MDC can be transformed into the issue of distributed partial multi-label learning. Then, by using several shared anchor data to characterize the global distribution of label variables, we propose a novel distributed approach to learn the label confidence of the training data. Under the supervision of recovered credible labels, the classifier can be induced by exploiting the high-order label dependencies from a common low-dimensional subspace. Experiments performed on various datasets indicate that our proposed method is capable of achieving learning performance in distributed partial MDC.

This paper investigates the distributed state estimation problem for the linear system against non-Gaussian noise, where every sensor commutates information only within its adjacent sensors without the need for a fusion center. Correntropy is a similarity metric based on a kernel function that has symmetry. Symmetry means that for any two data points, the output value of the kernel function does not depend on the order of the data points. By adopting a correntropy cost function based on the rational quadratic kernel function approximation to restrain non-Gaussian heavy-tailed noise, a centralized maximum correntropy Kalman filter is first derived for the linear sens+or network system at first. Then the corresponding centralized maximum correntropy information filter is attained by employing the information matrices, which is a foundation for further designing distributed information algorithms under multi-sensor networks. Thirdly, the distributed rational quadratic maximum correntropy information filter and distributed adaptive rational quadratic maximum correntropy information filter are designed by exploiting the weighted census average to solve the non-Gaussian heavy-tailed noise interference in sensor networks. Finally, the performance of the proposed algorithms is illustrated through numerical simulations on the sensor network system.

Due to its fast processing time and robustness against harsh environmental conditions, the frequency modulated continuous waveform (FMCW) multiple-input multiple-output (MIMO) radar is widely used for target localization. For high-accuracy localization, the two-dimensional multiple signal classification (2D MUSIC) algorithm can be applied to signals received by a single FMCW MIMO radar, achieving high-resolution positioning performance. To further enhance estimation accuracy, received signals or MUSIC spectra from multiple FMCW MIMO radars are often collected at a data fusion center and processed coherently. However, this approach increases data communication overhead and implementation complexity. To address these challenges, we propose an efficient high-resolution target localization algorithm. In the proposed method, the target position estimates from multiple FMCW MIMO radars are collected and combined using a weighted averaging approach to determine the target’s position within a unified coordinate system at the data fusion center. We first analyze the achievable resolution in the unified coordinate system, considering the impact of local parameter estimation errors. Based on this analysis, weights are assigned according to the achievable resolution within the unified coordinate framework. Notably, due to the typically limited number of antennas in FMCW MIMO radars, the azimuth angle resolution tends to be relatively lower than the range resolution. As a result, the achievable resolution in the unified coordinate system depends on the placement of each FMCW MIMO radar. The performance of the proposed scheme is validated using both synthetic simulation data and experimentally measured data, demonstrating its effectiveness in real-world scenarios.

Multi-dimensional classification (MDC) aims to simultaneously train a number of multi-class classifiers for multiple heterogeneous class spaces. However, as supervised learning methods, the existing MDC algorithms require that all the training data be precisely labeled in multi-dimensional class spaces, which can be impractical in many real applications sometimes. The lack of high-quality labeled data may negatively affect their learning performance. Additionally, the existing MDC algorithms only address scenarios of centralized processing, where all training data must be centrally stored at a single fusion center. Nowadays, however, the training data are typically distributed at multiple nodes within a network, making it challenging to transmit them to a fusion center for further processing. To address these issues, in this paper, we propose a novel algorithm called distributed semi-supervised partial multi-dimensional learning (dS2PMDL), which is designed to handle distributed classification of a small proportion of partially multi-dimensional (PMD) data and a large proportion of unlabeled data across a network. In our proposed algorithm, an in-network framework of subspace learning is formulated for label recovery. By tracking the representations of non-noisy label vectors in the learned subspace, the reliable labels of training data can be recovered. Subsequently, the multi-dimensional classifier modeled by the random feature map can be adaptively trained using a two-level label dependencies exploitation strategy. The convergence performance and communication complexity of the dS2PMDL algorithm are analyzed. Furthermore, experiments on multiple datasets are performed to validate the effectiveness of the proposed algorithm in semi-supervised partial multi-dimensional classification.

Fusion centers and network science tools offer complementary strengths in countering both domestic extremism and foreign-influenced terrorism. This article examines three distinct yet interconnected studies that highlight how intelligence sharing, targeted disruption, and community engagement can prevent radicalization and dismantle violent networks. Drawing from criminological and leadership frameworks, Eberhardt (2025) explores the organizational, strategic, and ethical implications of preemptive law enforcement responses to hate crimes and terror threats.

This paper studies kernel PCA in a decentralized setting, where data are distributively observed with full features in local nodes, and a fusion center is prohibited. Compared with linear PCA, the use of kernel brings challenges to the design of decentralized consensus optimization: the local projection directions are data-dependent. As a result, the consensus constraint in distributed linear PCA is no longer valid. To overcome this problem, we propose a projection consensus constraint and obtain an effective decentralized consensus framework, where local solutions are expected to be the projection of the global solution on the column space of the local dataset. We also derive a fully non-parametric, fast, and convergent algorithm based on the alternative direction method of multiplier, of which each iteration is analytic and communication-efficient. Experiments on a truly parallel architecture are conducted on real-world data, showing that the proposed decentralized algorithm is effective in utilizing information from other nodes and takes great advantages in running time over the central kernel PCA.

Intelligence fusion centers are one approach to providing intelligence, indicationsand warning, and analytical support to government agencies at all levels of government, andacross jurisdictional boundaries, to address a range of issues , including terrorism and violentextremism. This article provides a case study of the Los Angeles Terrorism Early WarningGroup (LA TEW). The LA TEW was a pioneer in developing and providing comprehensive, all-source intelligence support to a metropolitan region. The TEW model included lawenforcement (police and corrections), fire service, emergency medical services, public health,and emergency management agencies, along with a network of subject matter experts toprovide insight into terrorist threats, extremism, critical infrastructure protection and emergingthreats. The LA TEW also developed a range of analytical models and approaches to provideintelligence support for civil protection and counterterrorism that are reviewed in this article.

Abstract This paper investigates the fusion filtering algorithm for multi-antenna measurement information of an unmanned surface vehicle (USV) and proposes an optimal Kalman sequential fusion filtering (KSFF) algorithm with minimal linear covariance. The measurement information from each antenna on the USV is transmitted to the information fusion center and integrated in the order of reception. After fusing all the information, the state information of the USV based on global observations can be obtained and then updated for the next sampling period. Given the high computational complexity of KSFF, a measurement sequential fusion filtering (MSFF) algorithm with reduced computational complexity is introduced. The error covariance of the proposed algorithm has an upper bound, which has been demonstrated through mathematical induction, and the accuracy of state estimation is numerically equivalent to centralized fusion (CFF). Both simulation and physical experiment utilizing a USV are designed to verify that the KSFF and MSFF algorithm exhibit equivalent accuracy to the CFF algorithm. The experimental results show that the position and velocity accuracy of KSFF and MSFF algorithms are improved by 76.37% and 64.75%, respectively, compared with the results of single antenna. Additionally, a comparative analysis of the time consuming of the proposed algorithms is conducted, highlighting their computational advantages over the CFF algorithm.

Diffusive molecular communication (DMC) is an emerging paradigm in nanotechnology, which provides biocompatibility and nanoscale communication for many promising applications, such as targeted drug delivery, environmental monitoring, etc. However, detecting and localizing abnormalities in most of these applications is challenging, such as identifying tumor cells within the body or detecting pollution in air or water. In this paper, we introduce a method for detecting and localizing abnormalities in three dimensional DMC system with multiple sensors, receivers and one fusion center by adopting Transformer-based model with attention mechanism. We make full use of the attention mechanism to capture the inter-symbol interference (ISI) to improve the accuracy of detection and localization. In addition, we simplify the model structure to significantly reduce the complexity of this model. Furthermore, two strategies that different types of molecules (DMT) and same type of molecules (SMT) are released by sensors are considered. The training dataset and testing dataset are generated under these two strategies. Simulation results show that the information about the abnormality detection and localization can be obtained at the same time based on the Transformer-based model under DMT and SMT. Especially, our model outperforms the Informer-based model, deep neural networks (DNN)-based model and log-likelihood ratio (LLR) method.