This article proposes two types of unscented particle filters (UPFs) that leverage unscented transformation (UT) from a geometric perspective to compute the proposal distribution. An UPF on Lie groups is first developed. Specifically, both the propagation of the sigma points and the computation of the mean and covariance are performed on the Lie groups, while the weight update and resampling are conducted on the Lie algebra. Second, we introduce the log-linear property of group elements to streamline particle propagation by reducing redundant operations, thereby optimizing the proposed UPF framework. In the update process, intermittent measurements that are caused by factors such as packet dropouts and stochastic sensor scheduling are considered. While lowering computational demands, these measurements pose challenges to filter stability. To this end, the introduced property is used to prove that the estimation error remains bounded under certain assumptions. We further establish a critical threshold for the arrival rate of intermittent measurements and derive an upper bound for the expected state error covariance. Moreover, a detailed computational complexity analysis is conducted to evaluate the efficiency of the proposed method. Finally, with the original method serving as a benchmark, simulation and real-world GNSS/INS integrated navigation experiments confirm that the redesigned approach delivers comparable performance and significantly improved computational efficiency.

The complex industrial environment and poor data quality inherent in electrolytic copper foil production often lead to inaccurate energy consumption predictors, and consequently unreliable energy optimization. Given the difficulty in further improving the accuracy of energy consumption predictors, this article proposes a high-reliability stochastic prediction-based ensemble optimization (SPEO) method for energy saving of copper foil electrodeposition. The approach begins by introducing stochastic prediction to establish a standard stochastic prediction optimization framework, wherein filtering operations are applied to mitigate the adverse effects of prediction errors on the optimization process. Subsequently, reliability analysis methods are employed to derive reliability metrics based on optimization results generated by energy consumption predictors of varying accuracy. Finally, these metrics are comprehensively incorporated to integrate multiple optimizers, along with their respective optimal result feedback, thereby enhancing the generalizability and stability of the method. Experimental evaluations using real-world data from an industrial electrodeposition process demonstrate the superiority of the proposed SPEO method.

In low-altitude urban intelligent transportation systems, efficient cooperative task allocation and path planning for multiple unmanned aerial vehicles (UAV) are critical for ensuring the effective execution of complex tasks. This paper proposes a distributed decision-making and autonomous planning framework to achieve cooperative task allocation and path planning for multi-UAVs in low-altitude urban traffic environment. The mission requirements of task allocation and path planning are modeled using evolutionary potential games and show that there exists a Nash equilibrium for the proposed potential function. An Improved Log-linear Learning Algorithm (ILLA) is proposed, and suitable Boltzmann parameters are derived which will enable the proposed ILLA to converge to the optimal Nash equilibrium with a probability one. Furthermore, a Constraint-Based Multi-layer Bidirectional Adaptive A-Star (CBMBA A-Star) algorithm is designed to find optimal and collision free paths for each UAV. Compared with the baseline method, simulation results demonstrate that the proposed approach improves the task reward by 11.67%, reduces the task execution time by 37.41%, and decreases run time by 61.02%, confirming its effectiveness and efficiency in the complex low-altitude urban traffic scenario.

This article addresses the problem of cooperative path following for wheeled mobile robots (WMRs) under system constraints and external bounded disturbances within a switching communication network, by proposing a robust distributed model predictive control (DMPC) strategy. First, the cooperative path-following task is decoupled into two subtasks using a modified virtual structure: a cooperative task involving virtual reference robots and an individual path-following task between each actual robot and its corresponding virtual reference. A time-like path parameter is introduced to generate predefined path information for the virtual reference robot in advance, enabling dynamic formation tracking. Subsequently, discrete-time error dynamics subject to external bounded disturbances are derived for each robot, and a centralized predictive control problem is formulated as a baseline. A nominal DMPC strategy is then developed for the disturbance-free case, followed by an extension to a robust DMPC formulation that accounts for nonzero disturbances. In this context, a stability constraint is incorporated to ensure closed-loop stability without relying on neighboring agents’ real-time information. Theoretical analysis confirms the feasibility of the proposed scheme and guarantees the convergence of system trajectories to a disturbance invariant set. Finally, simulation and experimental results validate the effectiveness of the proposed strategy in cooperative path-following scenarios involving WMRs.

Distributed localization systems enable nodes to determine their locations by exchanging information with neighboring nodes, without relying on centralized infrastructure. While it enhances scalability and robustness, random data loss during in formation transmission can significantly degrade the localization performance. In this paper, to mitigate the impact of random data loss, we propose a Loss-Robust distributed localization method based on the Distributed Iterative Localization algorithm (LR DILOC). In LR-DILOC, each node updates its location estimate by leveraging the most recent available location information from its neighboring nodes, thereby improving the utilization of available data. We theoretically analyze the convergence of LR-DILOC, demonstrating that LR-DILOC maintains accurate localization even in the presence of random data loss. Numerical results further validate the theoretical analysis, demonstrating that LR-DILOC achieves higher localization accuracy and exhibits stronger robustness under random data loss.

This paper investigates the distributed secure fusion estimation problem under stochastic nonlinear hybrid attacks. Specifically, this work analyzes a hybrid attack scenario where the attacker employs a randomized approach to launch false data injection (FDI) attacks and Denial-of-Service (DoS) attacks on the measurement information communication channel. Then, an innovative distributed secure fusion estimation model is proposed, addressing three situations: DoS attacks, FDI attacks, and the absence of attacks. Following this, an existence condition is derived for the secure fusion estimator, utilizing probability-guaranteed set-membership filtering technology, to ensure that the fusion estimation error will consistently be bounded within an expected ellipsoid with the specified probability. Subsequently, a convex optimization problem involving constrained recursive matrix inequalities is formulated to compute the secure fusion estimation weight matrices. Finally, the effectiveness of the proposed probability-guaranteed set-membership secure fusion estimation (SSFE) algorithm is demonstrated through a simulation example. Note to Practitioners—The research in this paper is dedicated to addressing the problem of fusion state estimation in practical engineering tasks such as intelligent transportation, industrial manufacturing and military defense. With the increase in application requirements and process accuracy, the majority of projects demand that the true state must be bounded within a certain range, e.g., unmanned vehicle obstacle avoidance and missile precision strikes. To overcome this challenge, probability-guaranteed set-membership filtering is introduced to ensure that the fusion estimation error is bounded with a certain probability. However, due to the expanding scope of engineering applications, the system may be deployed to perform tasks in a non-secure environment, which increases the risk of malicious attacks that may lead to functional failures as well as performance degradation. Therefore, this paper simultaneously considers the scenario where the communication channels are subjected to the stochastic hybrid attacks, which can be effectively handled by utilizing the proposed probability-guaranteed SSFE algorithm. Preliminary simulations demonstrate the feasibility of the algorithm. Since the actual system may also encounter problems such as sensor energy constraints, bandwidth resource limitations, and data processing asynchrony. Therefore, in our future work, we will focus on addressing the various limitations present in the fusion estimation system and improving their resolution.

Stable dynamic systems enable robotic systems to plan and execute complex geometric motions in unstructured environments. However, in certain scenarios, such as precision assembly tasks, the constrained operational workspace of robotic arms, along with the presence of obstacles, may lead to unintended collisions. Furthermore, motion precision plays a crucial role in determining the success rate of such tasks. To address these challenges, we propose a novel Safe-Critical Dynamic System (SC-DS) framework. The SC-DS framework consists of a stable dynamic system and an obstacle avoidance controller. The stable dynamic system is formulated in a parametric nonlinear form, which enhances performance in terms of accuracy. Additionally, control barrier functions (CBF), corresponding to complex constraint spaces, are learned from demonstration data. By utilizing these learned CBFs, an obstacle avoidance controller is designed to ensure that the system trajectory remains within the learned safety boundaries. Moreover, the controller adaptively extends the effective range of control inputs, thus mitigating replication errors due to input limitations. Experimental results, both in simulation and with a physical robot, demonstrate that the SC-DS framework effectively reproduces trajectories with both stability and safety, outperforming existing methods in terms of overall task performance.Note to Practitioners—This study is motivated by the need to develop a safer and more precise skill-learning framework for practical applications, such as service robots and assembly robots. We propose the SC-DS framework, which integrates the challenges of uncertain environments into the learning of precise motion skills, ensuring both accuracy and safety in trajectory generation. This framework is particularly suitable for applications requiring strict performance and safety standards. By incorporating safety constraints directly into the learning process, our method provides a robust solution that effectively addresses robot-environment interactions, including obstacles, disturbances, and varying conditions. Our research enhances the reliability of dynamic system-based learning frameworks and offers practical tools for real-world applications, ensuring robots perform tasks efficiently while maintaining safety. Practitioners can leverage this framework to improve safety and maintain high-precision skill learning, ensuring effective handling of real-world challenges.

This paper addresses the problem of designing probability-guaranteed set-membership secure estimation algorithms for energy-constrained multi-sensor systems with multi-rate asynchronous samplings. An event-triggered strategy (ETS) is employed to minimize data transmission overhead while maintaining estimation accuracy by transmitting only essential data. A novel measurement model is proposed to accurately characterize the operation of the multi-sensor system under ETS, taking into account both high- and low-energy transmission (HLET) modes and random denial-of-service (DoS) attacks, which impact communication energy consumption and data security. To cope with the challenges posed by uncertain sampling periods, a new fusion estimation model is established, including a redefined fusion estimation weight matrix and the formulation of a probability-guaranteed set-membership secure fusion estimation algorithm. Furthermore, a recursive optimization algorithm based on linear matrix inequalities is utilized to determine the minimum ellipsoid of the design parameters. The effectiveness of the proposed algorithm is validated through simulation studies.

The coordinated operations of Stealth Unmanned Aerial Vehicle (SUAV) and Swarming Drones (SD) have demonstrated formidable power in the military domain. Efficient path planning is a critical technology that enhances combat effectiveness. This paper proposes a game-theoretic optimization approach to achieve cooperative penetration and target search path planning for swarm UAVs. Multi-task and multi-objective optimization models are developed for complex scenarios whose optimal solutions are NP-hard. Consequently, SUAV and SD are defined as leader and followers, respectively. The formulated Stackelberg game model enables distributed intelligent decision-making for SUAV and SD. We theoretically prove that by selecting an appropriate potential function, subgames within the leader-level and followers-level become an Ordinal Potential Game (OPG) with a Nash equilibrium, thereby ensuring the existence of a Stackelberg Equilibrium (SE) through leader-follower interactions. We propose a Gradient-based Hierarchical Learning and Optimization Algorithm (GHLOA) to achieve SE. At the leader-level, a Stochastic Gradient Ascent (SGA) algorithm optimizes the penetration path for SUAV, while in the followers-level, we demonstrate that the designed Hybrid Learning-based Multimodal Adaptive Pigeon-Inspired Optimization (HLMAPIO) algorithm converges with probability one to the suboptimal solution for each SD. Numerical results indicate that our approach is suboptimal, scalable, and fast adaptable to dynamical scenarios, and it outperforms the state-of-the-art techniques.

This article presents a comprehensive theoretical framework for addressing the problem of secure fusion estimation in energy-constrained multisensor systems, specifically targeting hybrid attacks in multiple transmission levels. The lifespan of sensor nodes is constrained by the availability of energy supply, and all the sensors have the flexibility to choose between high-energy or low-energy levels for transmitting their measurements. Sensor data becomes vulnerable to malicious tampering when operating in a low-energy level, whereas the high-energy transmission level enables accurate data transmission. By introducing a set of Bernoulli random variables and ternary random variables, a novel measurement model is proposed to characterize scenarios involving both dual-energy transmission modes and hybrid attacks, including three statuses: safe, deception attacks, and Denial-of-Service attacks. Based on the innovation analysis approach, local secure estimators are designed to ensure that the estimation errors are minimized locally. Then, an optimal secure fusion algorithm is provided to generate the final estimated value by fusing all the local estimates. Additionally, the proposed secure fusion estimation algorithm's stability and steady-state properties are investigated. Finally, two simulation cases are conducted to provide the empirical evidence of the superior performance of the proposed approach.

The spatiotemporal prediction of on-orbit satellites is crucial for intention understanding and ensuring the successful completion of missions. Current spatiotemporal prediction methods primarily use convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to process sequential observation images to predict future states. However, these methods often result in poor prediction performance due to the network’s inherent limited ability to express complex details. In this work, a residual spatiotemporal diffusion (RSTD) model is proposed to learn the spatial and temporal characteristics of targets from spaceborne imaging sequences. Leveraging a historical database, the model utilizes the patterns of image feature changes to assist in predicting target shape variations during the next observation period. A spatiotemporal perception module, capable of capturing long-term dependencies, is incorporated into the denoising process, thereby endowing it with forecasting capabilities. Furthermore, by incorporating a residual dual-stream structure, the model separates the prediction of the target’s overall shape and dynamic changes, thus overcoming the issue of overly smooth predicted images. Comprehensive experiments demonstrate that the proposed method achieves a peak signal-to-noise ratio (PSNR) of about 35 dB during uniform and uniformly variable motion. It also outperforms existing methods in subsequent feature extraction and attitude estimation, supporting the spatiotemporal attitude prediction of on-orbit satellites.

The state estimation on$SE_{2}(3)$Lie Group has been proven to have the ability to improve the consistency of the estimated results. They are called invariant state estimation approaches, including filter-based ones and optimization-based ones. Precise Point Positioning (PPP) is a Global Navigation Satellite System (GNSS) positioning technology which can achieve high-precision positioning without commercial base stations. Visual-Inertial Odometry (VIO) combines Visual-SLAM and IMU, realizing a more robust local pose estimation than either of the two. In this paper, the invariant optimization approach has been applied to fuse PPP/INS/Visual-SLAM. The proposed positioning system in our paper is called In-P3VINS. All raw data of the In-P3VINS is modeled and optimized under an invariant factor graph framework. In particular, the carrier phase measurement is utilized by adding the phase ambiguity into the estimated states. Finally, In-P3VINS is evaluated in both simulation experiments and real-world experiments. In the simulation experiments, the accuracy and consistency of In-P3VINS are superior to the other compared methods. In the real-world experiments, In-P3VINS has the most accurate results.

Learning an autonomous dynamic system (ADS) encoding human motion rules has been shown as an effective way for human motion skills transfer. However, most existing approaches focus on goal-directed motion skills transfer, and the study on periodic motion skills transfer is rare. One popular approach for periodic motion skills transfer is learning periodic dynamic movement primitive (DMP); however, periodic DMP is sensitive to spatial disturbances due to the introduction of the phase parameters. To solve this issue, this brief presents a novel approach to learn an ADS with a stable limit cycle without introducing phase parameters. First, a data-driven Lyapunov function (energy function) is learned, such that one of its level surfaces is consistent with periodic human demonstration trajectories. Then, an ADS is learned by sequentially solving energy function-related constrained optimization problems. With a proper design of constraint functions, we can ensure that the trajectory generated by the ADS will converge to an energy function-level surface, of which the shape is similar to periodic human demonstration trajectories. Experiments are conducted to show the effectiveness of the proposed approach (PA).

This article is concerned with the co-design of privacy-preserving and resilient consensus protocol for a class of multiagent networks (MANs), where the information exchanges over communication networks among the agents suffer from eavesdropping and Sybil attacks. First, we introduce a new attack model in which an adversarial agent could launch a Sybil attack, generating a large number of spurious entities in the network, thereby gaining disproportionate influence. In this communication framework, a whole-process privacy-preserving mechanism is designed that is capable of protecting both initial and current states of agents. Then, instead of existing methods requiring identifying and mitigating Sybil nodes, a degree-based mean-subsequence-reduced (D-MSR) resilient strategy is implemented, showcasing its significant properties: 1) ensuring the effectiveness of aforementioned designed privacy protection strategy; 2) allowing the network to contain Sybil nodes without elimination; and 3) reaching consensus among the normal agents. Finally, several numerical simulations are provided to validate the effectiveness of the proposed results.

Existing data-driven bearing fault diagnosis studies are based on strong assumptions: complete fault samples are required. The number of fault data can be more or less, but the data of each fault class must be available. However, such a condition is difficult to meet in the industry. Therefore, this paper addresses an open issue: bearing fault diagnosis with incomplete training data. In other words, only partial fault data are available in the training process. This issue is more in line with the industrial situation, and the issue is worthy of in-depth research. In response to this issue, the Cepstrum-Scale-Distance based Framework (CSD-Framework) is proposed, including C-stage, S-stage, and D-stage. The three stages realized vibration signal transformation, multi-scale adaptive adjustment, and multi-metric distance matching, respectively. This is a general framework, suitable for analyzing vibration signals, and is convenient to be combined with advanced AI algorithms. On this basis, the Multi-Metric-Adaptive-Clustering (MMA-Clustering) algorithm and the Multi-Metric-Weight-Classify (MMW-Classify) algorithm are proposed to form the D-stage of CSD. The proposed method has three advantages: 1) generic; 2) scalable; 3) good ability to classify unseen data. Experimental results showed that the performance of CSD was better than a variety of existing AI algorithms, as well as Ceps-AI methods based on cepstrum and AI algorithms.Note to Practitioners—Diagnose bearing faults by using incomplete fault data (only partial fault diameters). Existing approaches require a complete dataset, that is, each fault (diameter) needs to be available, and to achieve accurate classification of high-similar data through strong learning ability, but it is helpless for unseen fault diameters. This paper proposes a fault diagnosis framework based on cepstrum, which focuses on highlighting key fault features with cepstrum analysis technique, and realizes fault diagnosis based on incomplete data by constructing a multi-scale and multi-metric adaptive distance matching method. The framework is validated on a public dataset and a real-world dataset, where only partial fault data (fault diameter) is required for training.

To defend the cyber–physical system (CPSs) from cyber-attacks, this work proposes an unified intrusion detection mechanism which is capable to fast hunt various types of attacks. Focusing on securing the data transmission, a novel dynamic data encryption scheme is developed and historical system data is used to dynamically update a secret key involved in the encryption. The core idea of the dynamic data encryption scheme is to establish a dynamic relationship between original data, secret key, ciphertext and its decrypted value, and in particular, this dynamic relationship will be destroyed once an attack occurs, which can be used to detect attacks. Then, based on dynamic data encryption, a unified fast attack detection method is proposed to detect different attacks, including replay, false data injection (FDI), zero-dynamics, and setpoint attacks. Extensive comparison studies are conducted by using the power system and flight control system. It is verified that the proposed method can immediately trigger the alarm as soon as attacks are launched while the conventional$\chi^{2}$detection could only capture the attacks after the estimation residual goes over the predetermined threshold. Furthermore, the proposed method does not degrade the system performance. Last but not the least, the proposed dynamic encryption scheme turns to normal operation mode as the attacks stop.

The Gaussian particle filter (GPF) is a type of particle filter that employs the Gaussian filter approximation as the proposal distribution. However, the linearization errors are introduced during the calculation of the proposal distribution. In this article, a progressive transform-based GPF (PT-GPF) is proposed to solve this problem. First, a progressive transformation is applied to the measurement model to circumvent the necessity of linearization in the calculation of the proposal distribution, thereby ensuring the generation of optimal Gaussian proposal distributions in sense of linear minimum mean-square error (LMMSE). Second, to mitigate the potential impact of outliers, a supplementary screening process is employed to enhance the Monte Carlo approximation of the posterior probability density function. Finally, simulations of a target tracking example demonstrate the effectiveness and superiority of the proposed method.

This article investigates the secure and privacy-preserving consensus problem of multiagent systems (MASs) with directed interaction topologies under multiple cyberattacks, which contain deception attacks and DoS attacks. First, a unified attack model is introduced to characterize such a multiple attack phenomenon. Besides, considering the existence of eavesdroppers who can intercept the data transmitted on the links, a fully distributed agent value reconstruction method based on the idea of state decomposition is designed to prevent the leakage of the agent's initial information. Then, a novel privacy-preserving adaptive resilient consensus algorithm (PPARCA) with certain graph robustness condition for MASs under the multiple cyberattacks is proposed. The algorithm adaptively takes different countermeasures in the face of different cyberattacks. PPARCA uses the reconstructed agents' states and combines with the modified secure acceptance and broadcast algorithm (SABA). Theoretical analysis shows that the proposed algorithm can effectively protect the privacy of the initial state of the agents, and reach resilient consensus in the face of cyberattacks. Finally, numerical simulations and Raspberry Pi MASs practical application experiments demonstrate the effectiveness of the proposed results.

Detecting driver fatigue is critical for ensuring traffic safety. Electroencephalography (EEG) is the golden standard for brain activity measurement and is considered a good indicator of detecting driver fatigue. However, the current driver fatigue detection algorithm has limitations in mining and fusing the spatiotemporal characteristics of EEG signals. In this paper, we propose a multi-branch deep learning network named spatio-temporal fusion network with brain region partitioning strategy (STFN-BRPS) to improve the accuracy and robustness of driver fatigue recognition. Initially, we develop a recurrent multi-scale convolution module (RMSCM) comprising a multi-scale convolution sub-module, a CNN-Bi-LSTM sub-module, and a residual structure branch. RMSCM effectively extracts highly discriminative long short-term temporal feature information. Secondly, we propose a dynamic graph convolution module and a spatial graph edges’ importance weight assignment method based on brain region partitioning strategy, which can acquire intrinsic spatial feature information between electrodes. Thirdly, we design a feature fusion module (FFM) that utilizes channel attention to fuse long short-term temporal and spatial features. FFM learns and prioritizes the significance and relevance of each channel in the fused features. Finally, the fused spatio-temporal features are passed into the classification module to obtain the predicted driver fatigue state. Extensive comparison and ablation studies are conducted on EEG signals collected from real-world driving scenarios. The results demonstrate that the proposed STFN-BRPS model delivers superior classification performance compared to the mainstream methods. This study establishes a benchmark for EEG-based driver fatigue detection and related deep-learning modeling work.

The Dijkstra algorithm is a classic path planning method, which in a discrete graph space, can start from a specified source node and find the shortest path between the source node and all other nodes in the graph. However, to the best of our knowledge, there is no effective method that achieves a function similar to that of the Dijkstra's algorithm in a continuous space. In this study, an optimal path planning algorithm called convex dissection topology (CDT)-Dijkstra is developed, which can quickly compute the global optimal path from one point to all other points in a 2D continuous space. CDT-Dijkstra is mainly divided into two stages: SetInit and GetGoal. In SetInit, the algorithm can quickly obtain the optimal CDT encoding set of all the cut lines based on the initial point Xinit. In GetGoal, the algorithm can return the global optimal path of any goal point at an extremely high speed. In this study, we propose and prove the planning principle of considering only the points on the cutlines, thus reducing the state space of the distance optimal path planning task from 2D to 1D. In addition, we propose a fast method to find the optimal path in a homogeneous class and theoretically prove the correctness of the method. Finally, by testing in a series of environments, the experimental results demonstrate that CDT-Dijkstra not only plans the optimal path from all points at once, but also has a significant advantage over advanced algorithms considering certain complex tasks.

In this paper, using the ball and beam system as an illustration, a control scheme is developed on human-robot collaboration, i.e., a two-level hierarchical framework is proposed to establish a robust human-robot collaboration (HRC) policy. On the high level, a deep reinforcement learning (DRL) algorithm is presented to plan the desired beam rotational velocity. The low level is constructed by a human-intention perception module and a robust collaboration policy design module. For the first module, a probabilistic model is fitted by using the Gaussian process regression (GPR) approach to predict human-hand velocities, and prediction results follow Gaussian distributions where mean values and variances represent predicted human-hand velocities and corresponding prediction confidences, respectively. For the second module, a robust collaboration policy is established by fusing a proactive policy and a conservative policy, where the proactive policy is used to control the robot to achieve the desired beam rotational velocity by using the predicted human-hand velocities. The conservative policy is designed to ensure the collaboration safety. The weighted parameters for fusion are adaptively tuned based on the prediction precision and confidence. Experiments are conducted on controlling ball position on a beam jointly by a human and a robot with vision data, and experimental results show the effectiveness of the designed robust collaboration policy. Note to Practitioners—Predicting human future behaviors and moderating robot behaviors accordingly is a long-standing problem for human-robot collaboration (HRC) tasks, such as assembling, transporting, etc. Existing approaches generally regard human behaviors as noises or only build simple human models without prediction confidence. This paper proposes a learning-based hierarchical framework that will derive a robust and safe HRC policy considering human behaviors, prediction confidence, and task-related optimality. The framework is validated by a representative experiment where human and robot are asked to jointly control a ball and beam system.

This article presents an intelligent fault diagnosis method for wind turbine (WT) gearbox by using wavelet packet decomposition (WPD) and deep learning. Specifically, the vibration signals from the gearbox are decomposed using WPD and the decomposed signal components are fed into a hierarchical convolutional neural network (CNN) to extract multiscale features adaptively and classify faults effectively. The presented method combines the multiscale characteristic of WPD with the strong classification capacity of CNNs, and it does not need complex manual feature extraction steps as usually adopted in existing results. The presented CNN with multiple characteristic scales based on WPD (WPD-MSCNN) has three advantages: 1) the added WPD layer can legitimately process the nonstationary vibration data to obtain components at multiple characteristic scales adaptively, it takes full advantage of WPD and, thus, enables the CNN to extract multiscale features; 2) the WPD layer directly sends multiscale components to the hierarchical CNN to extract rich fault information effectively, and it avoids the loss of useful information due to hand-crafted feature extraction; and 3) even if the scale changes, the lengths of components remain the same, which shows that the proposed method is robust to scale uncertainties in the vibration signals. Experiments with vibration data from a production wind farm provided by a company using condition monitoring system (CMS) show that the presented WPD-MSCNN method is superior to traditional CNN and multiscale CNN (MSCNN) for fault diagnosis.

Relying only on inertial measurement units (IMUs) for robust state estimation is critical to vehicle safety when imaging sensors abruptly fail. In this paper, we propose to consider learning-based method as a complement to the kinematic model, and obtain ego-motion based on the nonlinear filter pipeline. To be specific, we first model the state of the IMU on the manifold such that the beliefs of prior model are propagated correctly. Then, we construct an uncertainty-aware network to simultaneously learn the integral terms in the kinematic equations, and recursively compute the rigid body position and velocity as pseudo-measurements. We additionally use a nonlinear estimator to properly fuse the model with the learned observations, whose prior information is endowed by model on the manifold, while the updating correction signals are provided by the network on pattern learning, and finally, the split covariance intersection (SCI) is utilized to reasonably handle the unknown correlated information in both. The performance of method is evaluated in terms of accuracy, robustness, extensibility and server aspects using both simulated and real-world dataset. Experimental results demonstrate a promising performance of the proposed method to traditional or learning-based ones.

Industry 4.0 requires new production models to be more flexible and efficient, which means that robots should be capable of flexible skills to adapt to different production and processing tasks. Learning from demonstration (LfD) is considered as one of the promising ways for robots to obtain motion and manipulation skills from humans. In this article, a framework that enables a wheel mobile manipulator to learn skills from humans and complete the specified tasks in an unstructured environment is developed, including a high-level trajectory learning and a low-level trajectory tracking control. First, a modified dynamic movement primitives (DMPs) model is utilized to simultaneously learn the movement trajectories of a human operator's hand and body as reference trajectories for the mobile manipulator. Considering that the auxiliary model obtained by the nonlinear feedback is hard to accurately describe the behavior of mobile manipulator with the presence of uncertain parameters and disturbances, a novel model is established, and an unscented model predictive control (UMPC) strategy is then presented to solve the trajectory tracking control problem without violating the system constraints. Moreover, a sufficient condition guaranteeing the input to state practical stability (ISpS) of the system is obtained, and the upper bound of estimated error is also defined. Finally, the effectiveness of the proposed strategy is validated by three simulation experiments.

Learning a stable autonomous dynamic system (ADS) encoding human motion rules has been shown as an effective way for demonstration learning. However, the stability guarantee may sacrifice the demonstration learning accuracy. This article solves the issue by learning a stability certificate, represented by a neural energy function, on the demonstration set. We propose a polarlike space analysis approach to derive parameter constraints to guarantee the unique-minimum property of the neural energy function, which is essential for it to be a cogent stability certificate. Then, the neural energy function is learned to capture the demonstration preferences via constrained optimization algorithms. With the learned neural energy function, a globally asymptotically stable ADS with predefined position constraint is further formulated. We also quantitatively analyze the generalization ability of the learned ADS by utilizing the substantial flexibility of the neural energy function. The effectiveness of the proposed approach is validated on the LASA dataset and two representative robotic experiments.

Covariance intersection (CI) fusion is one of the most popular methods for combining estimates when the correlations among local estimation errors are unknown. Considering practical communication constraints, CI fusion tends to be performed in hierarchical and sequential forms, i.e., hierarchical CI (HCI) fusion and sequential CI (SCI) fusion. However, existing HCI and SCI fusion are sensitive to some uncertainties, i.e., the hierarchy structure and the fusion order, which make their fusion performances unreliable. To solve this problem, this article proposes hierarchy-structure-independent HCI fusion and fusion-order-independent SCI fusion by analogy with batch CI fusion, which can avoid possible negative effects caused by the aforementioned uncertainties. Finally, two simulations verify the effectiveness and advantages of the proposed methods.

This article addresses the problem of cooperation and coordination transportation for decoupling nonholonomic mobile manipulators (NMMs) in a workspace with obstacles. We propose a distributed model predictive control (MPC) approach for a team of NMMs to transport a target object while satisfying significant constraints and limitations, such as the feasible state and control input constraints, parameter synchronization constraints, and obstacles within the workspace. First, under the framework of the decoupling dynamics, an auxiliary dynamics model for task-space end-effectors and null-space mobile bases is obtained by the nonlinear feedback technique based on the Euler–Lagrange description of the NMMs. Using the modified virtual structure method, the cooperation and coordination transportation problem for NMMs is simplified as two independent synchronization tracking control problems for task-space end-effectors and null-space mobile bases. A distributed constrained optimization problem is established by taking the parameter synchronization and system constraints into the cost function. A general projection neural network (GPNN) approach is employed to solve the optimization problem and obtain the optimal control input. Moreover, a sufficient condition that guarantees the stability of the closed-loop system is further developed. Simulation results show that the proposed cooperation and coordination transportation strategy is feasible and effective.

This paper proposes a quadratic programming (QP)-based variable impedance control (VIC) algorithm to solve contact-rich trajectory tracking problems with impedance, position and velocity constraints. To the best of our knowledge, the impedance constraints which are significant to ensure the worst contact compliance have never been considered in other previous works. To handle the impedance constraints of the VIC algorithm, a novel impedance model where the impedance parameters are directly served as the control input is established. The impedance-constrained VIC design problem is then formulated as a QP problem which can be efficiently solved. To handle the position and velocity constraints, a complementary force is introduced into the novel impedance model. The complementary force will appear to prevent the constraints violation when the robot approaches the constrained area. The design problem of the complementary force is also transformed into a QP problem. Combing these two QP solutions, the VIC algorithm with both impedance, position and velocity constraints can be obtained. Finally, various experiments are conducted to show the effectiveness of the proposed QP-based constrained VIC algorithm.

In cooperative localization, the sharing of location information has raised the risk of the privacy breach. Geo-indistinguishability, as a formal notion of location privacy, can protect the user’s location privacy by adding random noise into his true location, while it degrades the localization accuracy inevitably. This article considers the tradeoff between the cooperative nodes’ privacy preservation level and the target’s localization accuracy. Since it incurs privacy cost for the cooperative nodes to report their location information, an incentive mechanism for the cooperative nodes to contribute their data is necessary. In this article, we propose a feasible incentive mechanism, named CLAP, based on contract theory to reward the cooperative nodes and ensure the expected localization accuracy. Specifically, we establish an optimization problem of minimizing the payment from the target, while guaranteeing the expected localization accuracy. By simplifying the constraints and transforming the original problem into a convex optimization problem, a closed-form solution is given. Extensive simulations evaluate the performance and validate the feasibility of our proposed contract-based incentive mechanism.

Currently, a nonconvex economic dispatch problem is one of the research focuses in the field of smart grid (SG). A variety of algorithms are developed to solve it. However, these algorithms are prone to suffering from high computation cost and slow convergence rate, which creates an inevitable gap between theoretical analysis and practical real-time operations. In this article, we aim at providing an ensemble deep-learning-based approach to tackle such a challenging issue. First, a novel ensemble method is presented to explore the ground truth of nonconvex economic dispatch problems. Second, considering the time-varying total load demand, cost coefficients, and dispatchability of all generation units in a practical SG system as the features, a new deep neural network structure is proposed to learn the complex mapping from instant features to an optimal nonconvex economic dispatch solution. If such a mapping is well approximated by the designed deep neural network, no significant effort is required to solve a new economic dispatch problem, and the solution is obtained on the scale of milliseconds. Third, analyzing that a single deep neural network may be weak to a small part of the mapping space of the nonconvex economic dispatch problem, we further present an ensemble of multiple parallel deep neural networks trained sequentially with a simplified Adaboost.R2 algorithm. Finally, case studies reveal that the proposed approach achieves orders of magnitude speedup in computational time while guaranteeing similar or better performance on minimizing the overall generation cost compared to the state-of-the-art nonconvex economic dispatch algorithms.

This article investigates the distributed state estimation problem for large-scale power systems with the appearance of bad data. The power system is decomposed into several nonoverlapping agents and these agents interact with each other through transmission lines to form an interconnected multiagent power system (IMAPS). The measurement at each agent is local measurement, and the measurement in transmission line is edge measurement. To obtain an accurate state estimation of each agent in a distributed manner when the measurements are coupled with bad data, a bad data detection process should be designed. The difficulty is how to detect the bad data in edge measurement in a distributed scheme. To solve this problem, the characteristics of the edge measurement residual is analyzed, and a distributed bad data detection strategy is presented based on a novel iterative distributed Kalman-like filter (IDKF). It is proved that the IDKF algorithm can converge in finite steps when the communication graph of the IMAPS is acyclic, and the estimation accuracy is similar to that of the centralized Kalman filter. In addition, the IDKF algorithm shows excellent performance even when bad data appears. Simulation tests conducted on the IEEE 118-bus power system verify the theoretical findings.

Currently, numerical optimization methods are used to solve distributed optimal power allocation (OPA) problems for islanded microgrid (MG) systems. Most of them are developed based on rigorous mathematical derivation. However, the complexity of such optimization algorithms inevitably creates a gap between theoretical analysis and real-time implementation. In order to bridge such a gap, in this article we provide a new distributed learning-based framework to solve the real-time OPA problem. Specifically, inspired by the human-thinking scheme, distributed deep neural networks (DNNs) together with a dynamic average consensus algorithm are first employed to obtain an approximate OPA solution in a distributed manner. Then a distributed balance generation and demand algorithm is designed to fine-tune it to obtain the final optimal feasible solution. In addition, it is theoretically proved that the proposed DNN can well approximate one existing OPA algorithm (Guo et al. 2018), where quantitative numbers of at most how many hidden layers and neurons are provided. Several experimental case studies show that our proposed distributed learning framework can achieve similar optimal results to those obtained by using typical existing distributed numerical optimization methods while it is superior in terms of simplicity and real-time capability.

This article tries to provide a new alternative approach to solve the economic dispatch (ED) problem in a smart grid system. Such a problem has been widely studied recently with several advanced numerical optimization algorithms being proposed. However, most of these numerical algorithms may suffer from high computational cost for on-line optimization. In this article, we aim to address this problem by proposing a learning-based optimization strategy. The key idea is to regard the optimization strategy of the ED problem as an unknown mapping relationship. With the help of traditional ED optimization algorithms to obtain the ground truth, we employ a deep neural network (DNN) to learn the ED optimization strategy and use it for online ED. In particular, our main contribution in this article is to theoretically show that one popular ED algorithm, i.e.,$\lambda $-iteration algorithm, can be accurately approximated by a well-constructed DNN with finite network size. Moreover, dynamic units status of dispatchable generators is also considered and can be well solved by our proposed approach. Furthermore, several simulation case studies implemented on a 3-unit power system and an IEEE-30 bus power system validate the effectiveness of our proposed method.

This article studies the intrusion detection problem for industrial control systems (ICSs) with repetitive machining under false data injection (FDI) attacks. A data-driven intrusion detection method is proposed based on both time- and frequency-domain analysis. The proposed method only utilizes the sensor measurements required in closed-loop control, and does not consume additional system resources or rely on the system model. In addition, features in time and frequency domain are extracted at the same time, having higher reliability than the intrusion detection methods which only utilize the features in time domain. After feature extraction, hidden Markov models (HMMs) are established by using the feature vectors under normal operating conditions of the ICS, and then the trained HMMs are utilized in real-time intrusion detection. Finally, experiments are carried out on a networked multiaxis engraving machine with FDI attacks. The experimental results show the effectiveness and superiority of the proposed intrusion detection method.

This article presents a distributed consensus-based successive convex approximation (DSCA) algorithm to solve nonconvex nondifferentiable economic dispatch (ED) problems. The ED model formulated incorporates generation constraints, valve-point effects, and multiple fuel types. A perturbation technique enables the proposed DSCA to tackle such a nondifferentiable and nonconvex optimization, which paves the way to solving more complicated optimization problems that occur in practical applications. The local generation constraint is taken care by a local surrogate convex optimization directly. The global equality constraint is handled based on a consensus protocol, where the local generation-demand mismatch among all dispatchable generators (DGs) is shared in a distributed manner. As a result, the power distribution of DGs is updated, and the generation cost is minimized. Several case studies show that the proposed DSCA algorithm can achieve superior ED solutions and computational efficiency over existing nonconvex optimization algorithms.

Existing distributed localization methods rarely consider the location privacy preservation problem, which however is nonnegligible. Regarding location privacy, typical solutions rely on a curious-but-honest model, requesting that all participants follow the rule. Different from the existing studies, both honest and dishonest models are considered in this article. We first propose a privacy-preserving distributed localization algorithm (PP-DILOC) by adopting a noise-adding mechanism under the curious-but-honest model. The performance of localization and privacy preservation of PP-DILOC are both theoretically analyzed. Then, in the presence of dishonest nodes, we propose a resilient PP-DILOC (RPP-DILOC), where a time-varying relax factor and an adversary detection procedure are added into PP-DILOC. Theoretical results provide sufficient conditions for the convergence of RPP-DILOC. The privacy levels and the localization performance in the absence/presence of dishonest nodes are evaluated through numerical and experimental results.

In wireless localization systems, location information with respect to anchors will be exposed to untrusted target or third party inevitably leading to location privacy leakage. Differential privacy based techniques can provide theoretical guarantee to privacy preservation, while such privacy preservation will degrade localization accuracy. We note that the quantitative relationship between localization accuracy and privacy level is still unclear. In this paper, we derive the Cramér-Rao lower bound (CRLB) about the target location when the anchors take a privacy preservation mechanism satisfying geo-indistinguishability, which is an application of differential privacy on Euclidean metric. The closed-form relationship between the target localization accuracy and the anchors' location privacy level is provided respectively for range-only and bearing-only localization. Numerical results in further verify our theoretical results.

This paper is concerned with the distributed estimation problem in sensor networks subjected to unknown attacks. Network attacks are considered to exist in two classes of channels: 1) communication channels from the plant to sensors and 2) communication channels among sensors. The status of an attack is viewed as a stochastic phenomenon, and the transmitted information will be affected when the attacker successfully carries out an attack on the related data packet. Based on the sensors' own measurements and their neighbors' local information, a novel distributed estimation model against two-channel stochastic attacks is presented. A sufficient condition on the existence of the desired distributed H∞estimators is derived and the distributed estimator gains are designed by solving a linear matrix inequality. Two illustrative examples are provided to demonstrate the effectiveness of the new design techniques.

This paper studies the position synchronization control problem for networked multiaxis motion systems (NMAMSs). First, a position synchronization error model is established for the multiaxis motion system, and the uncertainty induced by the network-induced delay is modeled as an additive disturbance of the system. Second, the delay-induced uncertainty and the external disturbances such as load torque variation are lumped together as a total disturbance in the system model. Based on the established position synchronization error model, a generalized extended state observer (GESO) is designed to estimate the lumped disturbance and system states simultaneously. Then, the GESO-based synchronization controller is designed to achieve the objective of position synchronization and disturbance rejection, and the effect of the network-induced delay in the synchronization performance is significantly reduced. Moreover, an input-to-state stability condition is presented for the position synchronization system. Finally, experiments on a four-motors position synchronization control platform are presented to demonstrate the effectiveness and superiority of the proposed method.

This article is concerned with the multisensor fusion estimation for target tracking with range-only wireless sensor networks. By employing a nonlinear transformation and a measurement fusion, the nonlinear distance measurements are transformed into a linear measurement with respect to the position of the target, which avoids the instability problem of nonlinear filtering. However, after the transformation, the new measurement noises are no longer Gaussian and cross uncorrelated. Taking the unmodeled disturbances into account, as well as the new noise properties, an adaptive factor is introduced by hypothesis test based on the posterior residual to improve the estimation performance, where only the root of a quadratic equation is required to be solved. Finally, both simulations and experiments of a target tracking example are presented to show the effectiveness of the proposed methods.

This paper is concerned with the set-membership estimation problem for complex networks subject to unknown but bounded attacks. Adversaries are assumed to exist in the nonsecure communication channels from the nodes to the estimators. The transmitted measurements may be modified by an attack function with added noise that is determined by the adversary but unknown to the estimators. A novel set-membership estimation model against unknown but bounded attacks is presented. Two sufficient conditions are derived to guarantee the existence of the set-membership estimators for the cases that the attack functions are linear and nonlinear, respectively. Two strategies for the design of the set-membership estimator gains are presented. The effectiveness of the proposed estimator design method is verified by two simulation examples.

This paper studies the extended state observer (ESO)-based distributed model predictive control (DMPC) approach to deal with multiple mobile robot formation with unknown disturbances. The distributed control problem with path parameters synchronization and disturbance rejection is formulated for formation system according to the tracking error dynamic model, where the reference paths are parameterized. A local distributed controller is designed by using DMPC strategy for each mobile robot in the absence of disturbance by including parameter synchronization constraints in the quadratic performance index as coupling terms. The DMPC optimization problem is solved by using Nash-optimization iteration strategy with the maximum number of iteration constraint. To improve the ability of anti-jamming, a feedforward compensation controller is designed by using ESO method, where the ESO is designed by pole assignment. The convergence of the proposed iterative algorithm is given. Furthermore, the input-to-state stability property of the proposed composite controller, combining a feedforward compensation controller and local distributed controller, is analyzed for the closed-loop system. Finally, the validity of the proposed algorithm is verified by two simulation examples.

This paper presents a hierarchical estimation method for maneuvering target tracking in event-triggered wireless sensor networks. First, several process noise covariances are chosen to characterize the dynamic characteristic of the target in the presence of maneuvers, and a bank of decentralized extended information filters (DEIFs) are used to generate state estimates of the target. Second, the estimates from the DEIFs are combined by covariance intersection (CI) to obtain an improved state estimate while still maintaining a consistent estimate. Thus, the DEIF and the CI methods form complementary advantages by satisfying the requirement of the consistency in the hierarchical estimation framework. Finally, both simulations and experiments of a target tracking example demonstrate that the proposed method is more suitable for applications to the maneuvering target tracking and it achieves a more satisfactory performance than the conventional DEIF method.

This paper studies the distributed dimensionality reduction fusion estimation problem for a class of cyber-physical systems (CPSs) under denial-of-service (DoS) attacks. The problem is modeled under the resource constraints (i.e., bandwidth or energy) for the defender and attacker. Based on a new attack and compensation model, a recursive distributed Kalman fusion estimator (DKFE) is designed for the addressed CPSs. Though the optimization objects of the defender and attacker are opposite, the corresponding optimization problems are established based on different available information. In this case, an explicit form of suboptimal dimensionality reduction is given against DoS attacks, while an effective attack strategy is proposed for the attacker. A stability condition is derived such that the mean square error of the designed DKFE is bounded. Two illustrative examples are given to show the effectiveness of the proposed methods.

This paper studies the observer based fault tolerant tracking control problem for linear multiagent systems with multiple faults and mismatched disturbances. A novel distributed intermediate estimator based fault tolerant tracking protocol is presented. The leader's input is nonzero and unavailable to the followers. By applying a projection technique, the mismatched disturbances are separated into matched and unmatched components. For each node, a tracking error system is established, for which an intermediate estimator driven by the relative output measurements is constructed to estimate the sensor faults and a combined signal of the leader's input, process faults, and matched disturbance component. Based on the estimation, a fault tolerant tracking protocol is designed to eliminate the effects of the combined signal. Besides, the effect of unmatched disturbance component can be attenuated by directly adjusting some specified parameters. Finally, a simulation example of aircraft demonstrates the effectiveness of the designed tracking protocol.

In this paper, we consider a periodic estimation problem in sensor networks with a shared communication channel. The transmission constraint is inevitable in a single-channel-based sensor network if the sensors are heterogeneous or deployed far away from each other. A novel stochastic competitive transmission strategy is presented to deal with the transmission constraint, such that the sensors communicate with the fusion center (FC) in a strict asynchronous manner. A periodic mixed storage strategy combing the zero-input and the hold-input mechanisms is presented to describe periodic updating of the stored information in the sensors' buffers. A recursive Kalman filtering algorithm is derived for the FC to periodically generate estimates of state variables describing an object by using a linear continuous-time stochastic model. Two simulation examples are presented to show the effectiveness of the proposed results.

The aperiodic optimal linear estimator design problem is investigated in this paper for networked systems with communication uncertainties, including delays and data losses, where the sampling and estimation are nonuniform and asynchronous. Based on the idea of measurement fusion, two approaches are proposed to design the aperiodic estimators, and it is shown that the estimator is equivalent to that designed by the measurement augmentation method in performance. Moreover, the estimation performance is improved by using a newly proposed measurement retransmission scheme as compared with the commonly used hold-input and zero-input schemes, by which the lost measurements are never used once they are lost.

This paper is concerned with the energy-efficient distributed filtering in sensor networks, and a unified switched system approach is proposed to achieve this goal. For the system under study, the measurement is first sampled under nonuniform sampling periods, then the local measurement elements are selected and quantized for transmission. Then, the transmission rate is further reduced to save constrained power in sensors. Based on the switched system approach, a unified model is presented to capture the nonuniform sampling, the measurement size reduction, the transmission rate reduction, the signal quantization, and the measurement missing phenomena. Sufficient conditions are obtained such that the filtering error system is exponentially stable in the mean-square sense with a prescribed H∞ performance level. Both simulation and experiment studies are given to show the effectiveness of the proposed new design technique.

This paper studies the distributed robust fusion estimation problem with stochastic and deterministic parameter uncertainties, where the covariance of the Gaussian white noise is unknown, and the covariances of the random variables in the stochastic uncertainties are in a bounded set. By using the discrete-time stochastic bounded real lemma and the matrix analysis approach, each local robust estimator is derived to guarantee an optimal estimation performance for admissible uncertainties, and then necessary and sufficient condition for the distributed robust fusion estimator is presented to obtain an optimal weighting fusion criterion. Note that the local robust estimation problem and the distributed robust fusion estimation problem are both converted into convex optimization problems, which can be easily solved by standard software packages. The advantage and effectiveness of the proposed methods are demonstrated through state monitoring for target tracking system and stirred rank reactor system.

This paper is concerned with the sequential fusion estimation for mobile sensor node localizations with received signal strength measurements in mobile wireless sensor networks (MWSNs). The modeling errors induced by the communication uncertainties are considered and the process noise covariance is assumed to follow a uniform distribution. A sequential fusion estimation method based on a novel square root cubature Kalman filter is presented, where the process noise covariance is generated randomly. Moreover, a lower bound of the distribution is given to improve the stability and performance of the estimator. An E-puck robot-based MWSN experiment platform is designed, and both simulations and experiments show that the proposed sequential fusion estimation method help simplify the determination of the process noise covariance while maintaining a satisfactory estimation performance.

This study investigates the signal estimation problem in noisy sensor networks with quantised communications. The sensors are subject to random sensor failures, and synchronously take noisy measurements to produce local estimates by using a Kalman filtering scheme at each sampling instant. A quantiser is considered to be embedded in each sensor, and the probabilistic quantisation strategy is adopted to reduce the energy consumption. In between two sampling instants, each sensor collects quantised local estimates from its neighbours and runs a consensus‐based fusion algorithm to generate a fused estimate. The process noises and measurement noises are considered to be spatially uncorrelated, a recursive equation is presented to calculate the estimation error covariance matrix and an upper bound is derived for the estimation performance index. Moreover, a sufficient condition for the convergence of the upper bound of the estimation performance index is also presented. Two types of optimisation problems are constructed for cases of infinite and finite recursions, respectively, where the former one focuses on minimising the derived upper bound of the estimation performance index, and the latter one aims to minimise the energy consumption subject to a constraint on the estimation performance. Illustrative examples are provided to demonstrate the effectiveness of the proposed theoretical results.

This letter investigates the hierarchical fusion estimation for clustered sensor networks. The sensors within the same cluster are connected to a local estimator, and all the local estimators are linked with a fusion center. The fusion center and the local estimators are not required to be synchronous. During each estimation interval, the sensors are allowed to communicate with the local estimator several times. A minimum variance estimation algorithm is presented for each cluster to aperiodically generate local estimates. A covariance intersection fusion strategy is presented for the fusion center to generate fused estimates by using asynchronous local estimates and previous fused estimates, without knowing the cross-covariances among the local estimation errors.

This paper is concerned with the signal to interference and noise ratio (SINR) estimation for wireless networks with SINR constraints and packet losses. A new SINR estimation method is proposed by using moving horizon estimation (MHE), where the SINR estimation system is described as a stochastic parameter system model. By choosing a stochastic cost function, the SINR estimator is obtained by solving a regularized least-squares problem. Considering the coupling of state variable and process noise, a one-step MHE algorithm is presented to solve the constrained SINR optimization problem by using LOQO algorithm. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed method.

This paper presents a switched system approach to solving the distributed sampled-data$\mbi{H_\infty }$filtering problem for sensor networks with nonuniform sampling periods. The sensor network is considered to be a peer-to-peer network without an estimation center. The measurements are sampled with nonuniform sampling periods, and each sensor in the network collects the sampled measurements only from its neighbors and runs a distributed$\mbi{H_\infty }$filtering algorithm to generate estimates. A stochastic switched system model is proposed to describe the aperiodic sampled-data filtering system with random packet losses. A sufficient existence condition for the distributed$\mbi{H_\infty }$filters is derived by using the average dwell time method, and it is shown that the obtained condition critically depends on the sampling periods and the packet loss probabilities. The design of the filters is accomplished by solving a convex optimization problem, and the designed filters guarantee that the filtering system is mean-square exponentially stable and all the filtering errors satisfy an average$\mbi{H_\infty }$noise attenuation level. An illustrative example is finally given to show the effectiveness of the proposed results.

This paper is concerned with the design of networked multi-sensor fusion estimation system (NMFES). The Kalman filtering problem is considered for the NMFES with random observation delays, packet dropouts and missing measurements caused by sensor failures. For each observation subsystem, the sensor failure phenomenon is described by a Bernoulli distributed white sequence with a known conditional probability, and the packet dropout phenomenon and randomly delayed measurements are described by multiple binary random variables. Without resorting to the augmentation technique, an optimal recursive fusion filter for NMFES is obtained in the linear minimum variance sense by using the innovation analysis method. The dimension of the designed filter is the same to the original system, which can help reduce computation costs as compared with the augmentation method. Moreover, the performance of the designed Kalman filter is dependent on the missing rates of the measurements, the upper bounds of random delays and the occurrence probabilities of delays. Finally, the effectiveness of the proposed results is demonstrated by an illustrative example.