Distributed Cubature Kalman Filter Based on MEEF With Adaptive Cauchy Kernel for State Estimation

The pulse arrival time (PAT) has been considered a surrogate measure for pulse wave velocity (PWV), although some studies have noted that this parameter is not accurate enough. Moreover, the inter-beat interval (IBI) time series obtained from successive pulse wave arrivals can be employed as a surrogate measure of the RR time series avoiding the use of electrocardiogram (ECG) signals. Pulse arrival detection is a procedure needed for both PAT and IBI measurements and depends on the proper fiducial points chosen. In this paper, a new set of fiducial points that can be tailored using several optimization criteria is proposed to improve the detection of successive pulse arrivals. This set is based on the location of local maxima and minima in the systolic rise of the pulse wave after fractional differintegration of the signal. Several optimization criteria have been proposed and applied to high-quality recordings of a database with subjects who were breathing at different rates while sitting or standing. When a proper fractional differintegration order is selected by using the RR time series as a reference, the agreement between the obtained IBI and RR is better than that for other state-of-the-art fiducial points. This work tested seven different traditional fiducial points. For the agreement analysis, the median standard deviation of the difference between the IBI and RR time series is 5.72 ms for the proposed fiducial point versus 6.20 ms for the best-performing traditional fiducial point, although it can reach as high as 9.93 ms for another traditional fiducial point. Other optimization criteria aim to reduce the standard deviation of the PAT (7.21 ms using the proposed fiducial point versus 8.22 ms to 15.4 ms for the best- and worst-performing traditional fiducial points) or to minimize the standard deviation of the PAT attributable to breathing (3.44 ms using the proposed fiducial point versus 4.40 ms to 5.12 ms for best- and worst-performing traditional fiducial points). The use of these fiducial points may help to better quantify the beat-to-beat PAT variability and IBI time series.

Introduction With the increasing complexity of underwater operations, remotely operated vehicles systems face the dual challenges of multi-source interference and component failures in unknown environments. Methods To achieve high-precision control of remotely operated vehicles arms under fault conditions, this paper proposes an online fault compensation control method based on a decoupling algorithm. This method separates the end-effector position and attitude control of the master and slave arms through a pose decoupling algorithm, constructs an observer-based fault diagnosis mechanism, and combines H∞ robust control and online adaptive strategies to achieve dynamic compensation for combined sensor and thruster faults. Results The results show that in dual-arm cooperative operation, the spatial trajectory tracking deviation of the robotic arm can be controlled within 4.3 mm, with a maximum deviation of 2.643 mm in the X-axis direction and a planning deviation of 3.075 mm in the Y-axis direction. Compared with backstepping fault-tolerant control and power sliding mode control, the method used in this study has a maximum deviation of only 0.01° in yaw angle control, a position control error reduced to 1.2 mm, and a maximum trajectory tracking error of 2.1 mm, which is significantly better than the comparative methods. Furthermore, the system can rapidly approach the desired posture within 50 seconds and maintains stable operation under various fault scenarios. Discussion This demonstrates that the proposed method can effectively improve the operational accuracy and fault “tolerance of remotely operated vehicles in complex environments, providing a new technology for solving the control problems of robot systems under fault conditions.”

In the realm of unmanned vehicles, challenges persist regarding their limited perception and real-time obstacle detection capabilities. This paper introduces the YOLOv5-SA object recognition algorithm, built upon YOLOv5, which addresses these issues. Leveraging Laplace transformation and SSR image enhancement on the training dataset, coupled with the integration of the SiAM attention mechanism at the network's output layer, and the replacement of the SPPF module with the AIFI module, the proposed model significantly enhances detection speed and accuracy. Experimental results demonstrate lower target omission rates and faster detection in various complex environments.

Multi-objective path planning in complex three-dimensional environments is a key challenge for improving the application efficiency of unmanned aerial vehicles in rescue, logistics and other fields. This paper proposes a dynamic multi-objective ant colony optimization algorithm to address the problems of traditional ant colony algorithms such as getting stuck in local optimum, slow convergence and difficulty in adapting to dynamic environments. This paper systematically constructs a multi-objective model that integrates path length, height constraints and threat avoidance, and designs a hierarchical pheromone update strategy based on path quality and a hybrid heuristic function for environmental adaptation to balance global exploration and local optimization capabilities. Simulation experiments show that compared with traditional ant colony, particle swarm and other algorithms, this method has significant improvements in path length, smoothness and computational efficiency, especially showing better obstacle avoidance success rate and real-time re-planning ability in high dynamic scenarios. This method effectively enhances the intelligent decision-making level of unmanned aerial vehicles in complex environments and has practical application value for improving the reliability of emergency response and logistics distribution.

We present a recursive path-planning method that efficiently generates a path by using reduced states of the search space and taking into account the kinematics, shape, and turning space of a car-like vehicle. Our method is based on a kinematics-aware node expansion method that checks for collisions based on the shape and turning space of a vehicle. We present two heuristics that simultaneously consider the kinematics of a vehicle with and without obstacles. In particular, for challenging environments containing complex obstacles and even narrow passages, we recursively identify intermediate goals and nodes that allow the vehicle to compute a path to its destination. We show the benefits of our method through simulations and experimental results by using an autonomous ground vehicle. Furthermore, we show that our method can efficiently generate a collision-free path for vehicles in complex environments with passageways.

Predicting the future trajectories of target agents is critical for the safe operation of autonomous vehicles in complex environments. Accurate trajectory prediction requires the extraction of both temporal and spatial information. To address the limitations of existing static attention methods, which are constrained by fixed historical ranges and spatial radii under limited computational resources, a novel trajectory prediction approach using dynamic spatio-temporal attention is proposed. DSTNet incorporates a multi-scale spatial sparse attention module and a multi-scale historical causal attention module, both aimed at dynamically encoding the spatio-temporal dependencies of the data. These modules are unified with multi-modal interactive attention in a three-attention spatio-temporal module. Additionally, DSTNet includes an anchor-based trajectory refinement module, which adaptively optimizes the coarse-grained trajectories generated by the backbone network. Compared with state-of-the-art methods, DSTNet demonstrates outstanding performance on the Argoverse 1 and Argoverse 2 motion prediction benchmarks, delivering accurate and stable future trajectories. Notably, the model has been successfully deployed on an autonomous vehicle.

With the growing size of the system, this distributed Kalman filter (DKF) is widely used in multi-sensor networks. However, it is difficult for DKF to accurately estimate state values in non-Gaussian noise environments. In this paper, a regression equation is first constructed to contain all sensor node information. Then, by bringing the minimum error entropy with fiducial points (MEEF) standard into the process of information fusion, a robust algorithm named centralized MEEF KF (CMEEF-KF) is presented, which is robust to non-Gaussian noise and unusual data. Furthermore, to overcome the communication burden of CMEEF-KF in sensor networks, the distributed MEEF-KF (DMEEF-KF) is developed, which construct a framework of consensus average method for node information fusion. Specifically, each sensor only exchanges the key information with its neighborhoods. In addition, in order to make the algorithm able to cope with the nonlinear state estimation problem, the distributed MEEF extended Kalman filter is also proposed. Eventually, the effectiveness of the suggested algorithms is demonstrated by land vehicle navigation and power system tracking state estimation using a 10-node sensor network.

The results of estimating the efficiency of digital filtration algorithms (median filtration, simple moving average and exponential moving average) by simulation under the influence of various types of noise (fluctuating, pulse and quasi-harmonic) are presented. As an estimation of efficiency, the attenuation coefficient as the ratio of the noise standard deviation at the input and output of the filters is used. The quantitative estimates of efficiency for various algorithms and their parameters, recommendations on the application of algorithms and the choice of their parameters are presented.

For the multiple source information fusion in complex environment problem, An information representation model based on the uncertain basis and a robust fusion algorithm are presented. First, a definition of uncertain basis is given and a realization method with kernel function is proposed. Based on the uncertain basis, an uncertain information description model is presented. Second, an adaptive kernel bandwidth robust fusion technique is proposed. The kernel bandwidth is initially decided by sample covariance and then is adjusted after each iterative to describe the estimated uncertainty. Theory analysis and simulation show that, the proposed method equals to the existing techniques in good scenario and takes advantage in complex environment.

Boundary kernels for adaptive density estimators on regions with irregular boundaries

Finite-time output feedback leader-following consensus for high-order time-varying nonlinear multi-agent systems

Distributed control and optimization form the backbone of many critical control systems, with typical examples including sensor networks, power grids, and intelligent transportation systems. As these systems grow in scale and complexity, ensuring the privacy of individual agents becomes increasingly essential. Privacy protection is no longer an optional add-on; it is a crucial element in the design of these systems. Sensitive data, such as states, measurements, and decisions, must be shielded from unauthorized access or malicious attacks, while still allowing the system to function effectively. This special issue aims to explore novel techniques and strategies that protect privacy in distributed control and optimization, showcasing how these approaches can be seamlessly integrated into real-world applications. The diverse contributions in this issue demonstrate the growing importance of privacy in the evolving field of distributed control systems. RNC7747 introduces a privacy-preserving method for coverage control in multi-agent systems with limited communication ranges, ensuring efficient operation without compromising agents' privacy. RNC7798 proposes a privacy-preserving consensus mechanism based on pulse-coupled oscillators, helping maintain privacy while agents coordinate in distributed networks. RNC7778 presents privacy preservation techniques for cloud-based cooperative LQG control systems, enabling privacy protection even when leveraging cloud resources. RNC8051 discusses achieving privacy-preserving average consensus in unreliable network environments, ensuring privacy despite communication challenges. RNC7929 introduces a novel approach to open privacy-preserving consensus via state decomposition, allowing agents to reach consensus while maintaining privacy. RNC7966 investigates leader-follower consensus in multi-agent systems with distributed event-triggered observations, ensuring privacy during agent coordination in real-time systems. RNC7789 discusses the tradeoff between privacy protection strength and control performance in control systems. RNC8029 proposes a new privacy mechanism for distributed average consensus on time-varying directed graphs. RNC7776 investigates event-triggered distributed optimization with differential privacy, reducing communication costs while safeguarding sensitive data. RNC7885 develops an event-triggered privacy-preserving optimization method for directed communication networks, emphasizing privacy during optimization tasks. RNC7791 focuses on robust optimization for virtual power plant scheduling under uncertainty, incorporating privacy preservation into the optimization process. RNC7730 explores prescribed-time distributed optimization with set constraints, offering time guarantees while maintaining privacy in dynamic optimization scenarios. RNC7926 provides a privacy-preserving algorithm for constrained resource allocation with communication delays, crucial for real-time decision-making in distributed systems. RNC7848 presents an adaptive event-triggered control strategy for cyber-physical systems under DoS and deception attacks, ensuring privacy and system security. RNC8068 addresses initial state privacy in nonlinear systems on Riemannian manifolds, offering methods to protect privacy in complex, high-dimensional control systems. RNC7906 focuses on dynamic consensus-based formation control for multi-robot systems, preserving privacy through output masking techniques while achieving coordination. RNC7688 introduces a periodic dynamic encoding mechanism for stealthy attack detection in distributed state estimation, focusing on security and privacy under attack scenarios. RNC70033 tackles robust consensus Kalman filtering for distributed state-saturated systems, incorporating dynamic-disturbed saturation levels and censored measurements to ensure privacy under uncertain conditions. The contributions in this special issue span a broad spectrum of topics related to privacy in distributed control and optimization. As distributed systems continue to evolve, these papers contribute to the ongoing conversation about balancing privacy protection with efficient system operation, highlighting the importance of privacy as a key consideration in the design and optimization of future distributed control systems. The authors declare no conflicts of interest.

The Cucker-Smale (C-S) model describes an interacting particle system in which the connection weights decrease with increasing distance. This model features emergent behaviors by which the velocities of the particles converge to a common value without a central command. However, the consensus value of the original C-S flocking model is restricted to the leader-following consensus or average consensus. Moreover, for the short-range communication-based C-S model, consensus can only be obtained for specific initial configurations. In this paper, the short-range communication-based C-S model is extended to achieve distributed optimization, where the consensus value optimizes the objective function of the group for any bounded initial configuration. Simulation examples are provided to demonstrate the effectiveness of our approach.

Linear discriminant analysis (LDA) has long been used to derive data-driven temporal filters in order to improve the robustness of speech features used in speech recognition. In this paper, we proposed the use of new optimization criteria of principal component analysis (PCA) and the minimum classification error (MCE) for constructing the temporal filters. Detailed comparative performance analysis for the features obtained using the three optimization criteria, LDA, PCA, and MCE, with various types of noise and a wide range of SNR values is presented. It was found that the new criteria lead to superior performance over the original MFCC features, just as LDA-derived filters can. In addition, the newly proposed MCE-derived filters can often do better than the LDA-derived filters. Also, it is shown that further performance improvements are achievable if any of these LDA/PCA/MCE-derived filters are integrated with the conventional approach of cepstral mean and variance normalization (CMVN). The performance improvements obtained in recognition experiments are further supported by analyses conducted using two different distance measures.

Local to global purification strategy to realize collaborative camouflaged object detection