Sensor scheduling plays a vital role in remote state estimation of networked systems with constrained communication bandwidth. Various event-triggered scheduling strategies have been proposed for different state estimation tasks. However, for nonlinear systems or systems with unknown noise, it is challenging to derive an exact estimator due to the intractability of noise evolution under the event-triggered mechanism. To address this limitation, we propose a learning-based stochastic event-triggered Kalman net scheme that models the estimation process with neural networks. The network is trained to learn the Kalman gain, which is then embedded into the Kalman filter for state estimation. To address the selective transmission in event-triggered mechanisms, a masking strategy is designed that uses the event-triggering sequence matrices to eliminate the impact of untransmitted data. Furthermore, the trade-off between communication rate and estimation accuracy can be flexibly tuned by adjusting the event-triggering decision matrix. The proposed method is applicable to both linear and nonlinear systems and does not rely on the noise statistics, as the noise characteristics are implicitly encoded in the hidden states of the recurrent neural network. Simulation results and real-world battery examples demonstrate the superiority of the proposed method and highlight its potential for advancing remote state estimation tasks.

The use of millimeter-wave radar (mmWave radar) for human motion detection is essential in the realm of smart home Internet-of-Things (IoT) scenarios due to its non-intrusive nature, privacy considerations, interactive capabilities, and cost-effectiveness. However, the existing methods fail to address the issue of low real-time detection rates. Moreover, the design of the action does not align with the requirements of smart home IoT systems, while the complexity of the algorithm leads to high computing hardware costs. To address these problems, we propose mmMotion, a novel mmWave radar-based human motion detection system capable of accurately estimating six well-designed human motions (sit down, stand up, get up, lie down, wave hands, and punch) commonly observed in smart home scenarios with cost-effectiveness and real-time processing speed. By incorporating the spatial feature of “Height”, we are the first to propose a novel feature extraction method using the “Doppler-Height-Time” (DHT) feature maps as system input for human motion detection. A combined lightweight Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture is developed to simultaneously capture spatial and temporal features, leveraging the strengths of both architectures. Furthermore, we propose a novel Real-time Classifier combined with the Static Clutter Removal (SCR) method and Duo Filter algorithm to enhance the real-time detection capability of mm-Motion. Experiment results demonstrate that mmMotion achieves an impressive offline training accuracy rate of 98.13% and a real-time motion recognition rate of 90.5% for six designated motions. Furthermore, it exhibits excellent generalization ability across different individuals and robustness to the interference of large and small actions during real-time performance testing.

Given 2D point correspondences between an image pair, inferring the camera motion is a fundamental issue in the computer vision community. The existing works generally set out from the epipolar constraint and estimate the essential matrix, which is not optimal in the maximum likelihood (ML) sense. In this paper, we dive into the original measurement model with respect to the rotation matrix and normalized translation vector and formulate the ML problem. We then propose an optimal two-step algorithm to solve it: In the first step, we estimate the variance of measurement noises and devise a consistent estimator based on bias elimination; In the second step, we execute a one-step Gauss-Newton iteration on manifold to refine the consistent estimator. We prove that the proposed estimator achieves the same asymptotic statistical properties as the ML estimator: The first is consistency, i.e., the estimator converges to the ground truth as the point number increases; The second is asymptotic efficiency, i.e., the mean squared error of the estimator converges to the theoretical lower bound - Cramer-Rao bound. In addition, we show that our algorithm has linear time complexity. These appealing characteristics endow our estimator with a great advantage in the case of dense point correspondences. Experiments on both synthetic data and real images demonstrate that when the point number reaches the order of hundreds, our estimator outperforms the state-of-the-art ones in terms of estimation accuracy and CPU time.

This letter investigates learning-based geometric tracking control for rigid body dynamics without precise system model parameters. Our approach leverages recent advancements in geometric optimal control and data-driven techniques to develop a learning-based tracking solution. By adopting Lie algebra formulation to transform tracking dynamics into a vector space, we estimate unknown parameters from data, achieving robust and efficient learning. Compared to existing learning-based methods, our approach ensures geometric consistency and delivers superior tracking accuracy. The simulation results validate the effectiveness of our method.

Accurate foot orientation and trajectory estimation are pivotal for advanced gait analysis, yet achieving this with inertial measurement units (IMUs) remains challenging due to their susceptibility to external acceleration, magnetic disturbances, and unbounded position errors. To address these limitations, we propose a multi-kernel correntropy smoother, which effectively mitigates unknown disturbances and enhances orientation accuracy. Furthermore, while the conventional zero-velocity update (ZUPT) method has been widely adopted in IMUs, the impact of incorporating position constraints has been largely overlooked. This paper demonstrates that by incorporating a single loop closure, the maximum positioning error can be reduced by up to 75%, with further reductions achievable through multiple position constraints. Comprehensive theoretical analysis and extensive experimental validation confirm the superior performance of the proposed methods.

Using underwater robots instead of humans for the inspection of coastal piers can enhance efficiency while reducing risks. A key challenge in performing these tasks lies in achieving efficient and rapid path planning within complex environments. Sampling-based path planning methods, such as Rapidly-exploring Random Tree* (RRT*), have demonstrated notable performance in high-dimensional spaces. In recent years, researchers have begun designing various geometry-inspired heuristics and neural network-driven heuristics to further enhance the effectiveness of RRT*. However, the performance of these general path planning methods still requires improvement when applied to highly cluttered underwater environments. In this paper, we propose PierGuard, which combines the strengths of bidirectional search and neural network-driven heuristic regions. We design a specialized neural network to generate high-quality heuristic regions in cluttered maps, thereby improving the performance of the path planning. Through extensive simulation and real-world ocean field experiments, we demonstrate the effectiveness and efficiency of our proposed method compared with previous research. Our method achieves approximately 2.6 times the performance of the state-of-the-art geometric-based sampling method and nearly 4.9 times that of the state-of-the-art learning-based sampling method. Our results provide valuable insights for the automation of pier inspection and the enhancement of maritime safety. (Video1).

Trajectory planning for quadrotors in cluttered environments has been challenging in recent years. While many trajectory planning frameworks have been successful, there still exists potential for improvements, particularly in enhancing the speed of generating efficient trajectories. In this paper, we present a novel hierarchical trajectory planning framework to reduce computational time and memory usage called MINER-RRT*, which consists of two main components. First, we propose a sampling-based path planning method boosted by neural networks, where the predicted heuristic region accelerates the convergence of rapidly-exploring random trees. Second, we utilize the optimal conditions derived from the quadrotor’s differential flatness properties to construct polynomial trajectories that minimize control effort in multiple stages. Extensive simulation and real-world experimental results demonstrate that, compared to several state-of-the-art (SOTA) approaches, our method can generate high-quality trajectories with better performance in 3D cluttered environments (https://youtu.be/fXuuMRX19q0). Note to Practitioners—The motivation is the problem of planning trajectories for quadrotor autonomous flight in 3D cluttered and complex scenarios such as wild forest exploration and subterranean environment search-and-rescue. Sampling-based path planning methods are suitable for dealing with the complexity of the physical environment but are not convenient for computing dynamics and their differentials. Optimization-based trajectory generation methods are appropriate for handling various high-order constraints but rely on high-quality initial path solutions. Therefore, this paper combines the advantages of the two methods to propose a novel trajectory planning framework that can generate high-quality trajectories for quadrotors faster than many previous algorithms. We conduct numerous simulations and real-world experiments to verify that our method can be effectively deployed in real scenarios and empower quadrotors for complex autonomous tasks in the future.

The containment of multirobot systems (MRSs) has a wide range of applications. However, time delays in communication among robots introduce difficulties to the system to accomplish containment. In addition, the specific dynamics of robot models pose new nonlinear and nonholonomic challenges. To solve these problems, a containment control law is proposed first for double-integrator MRSs subject to nonuniform time-varying delays. In contrast to impractical uniform delays, nonuniform time-varying delays are considered more deeply from the perspective of the Laplacian matrix in this article. The stability is proved by the Lyapunov–Krasovskii function and linear matrix inequalities. The proposed control law is further refined into a dual-loop structure for multi-nonholonomic-mobile-robot systems, addressing the problem of nonholonomic constraints. Specifically, the first loop decouples the control inputs in a finite time, and then the nonholonomic robot models are regarded as linear models, which facilitates the proof of system stability. The effectiveness of the aforementioned two control laws is validated through simulations and experiments. Under these containment control laws, followers in the system reach the convex hull formed by leaders and meet the convergence objective despite the constraint of nonuniform time-varying delays.

Due to the complex entanglement between distributed control and distributed estimation, adaptive multiagent dynamics over leaderless directed graphs are yet not completely understood. This happens even when the adaptive dynamics are based on established tools like model reference adaptive control (MRAC). This work starts from the observation that existing MRAC-based leaderless designs stop at asymptotic consensus, lacking of any guarantee for exponential consensus or parameter identification. The main contribution of this work is to show a new design departing from the existing ones in terms of exploiting persistence of excitation (PE): the proposed design is the first one attaining exponential consensus with exponential parameter identification over leaderless directed graphs. In the special case that the unknown parameters are homogeneous, PE can be relaxed to a weaker cooperative PE (C-PE) condition. The design is illustrated and verified alongside the state-of-the-art.

The Coastal underwater evidence search system with surface-underwater collaboration is designed to revolutionize the search for artificial objects in coastal underwater en-vironments, overcoming limitations associated with traditional methods such as divers and tethered remotely operated vehicles. Our innovative multi-robot collaborative system consists of three parts: an autonomous surface vehicle as a mission control center, a towed underwater vehicle for wide-area search, and a biomimetic underwater robot inspired by marine organisms for detailed inspections of identified areas. We conduct extensive simulations and real-world experiments in pond environments and coastal fields to demonstrate the system's potential to sur-pass the limitations of conventional underwater search methods, offering a robust and efficient solution for law enforcement and recovery operations in marine settings. (Video11https://youtu.be/tEtTLpGrO2c)

This paper investigates the properties of the distributed filter under scaled noise covariances. Initially, three performance indices, along with three auxiliary indices, are introduced to evaluate the performance of the distributed filter. By providing the specific assumptions on the scaled parameters, the proportional relations among three performance indices are revealed. The relations of the gain matrices under the nominal and actual parameter scenarios are also examined. Furthermore, the impact of the fusion step on these relations is elucidated. These results provide guidance for designing the nominal noise covariance and evaluating the performance of the distributed filter under the scaled noise covariances. The theoretical results are validated through simulations.

This paper considers the design of false data injection attacks towards cyber-physical systems without knowing system parameters. The existing research usually assumes that the attacker knows the plant's parameters. However, this is often not the real case since the plant's parameters are sensitive information that is not transmitted and thereby cannot be eavesdropped by external attackers. We propose an inference-based CPS attack mechanism where an attacker simultaneously updates an inference system and designs the injection attacks. More specifically, we introduce the notion of system imitator to cyber-security for the first time and propose a bilevel imitation-based cyber-attack mechanism. We show that the attack mechanism can be implemented in an online manner and the convergence is theoretically guaranteed. Besides, we present a new cyber-threat model in communication networks, called network congestion attack, to illustrate the applicability of our approach.

We propose a novel mmWave radar-based system capable of accurately estimating six human motions (Sit down, stand up, snap, swing hands, get up, and lie down) commonly observed in smart home scenarios with cost-effectiveness and real-time processing speed. We are the first to propose the “Doppler-Height-SNR-Time” feature maps as system input for human motion estimation. We propose a novel approach that combines lightweight CNN and LSTM architectures. Our approach leverages the strengths of both models to enhance system accuracy by effectively capturing spatial and doppler velocity features using CNN while extracting temporal features related to motion continuity using LSTM. Our model achieves an impressive offline testing accuracy of 96.7% and demonstrates a 73.33% recognition rate for predefined actions in real-time scenarios, along with robustness against non-target motions in experiments.

In recent years, over-the-air aggregation has been widely considered in large-scale distributed learning, optimization, and sensing. In this paper, we propose an over-the-air federated policy gradient algorithm, where all agents simulta-neously broadcast an analog signal carrying local information to a common wireless channel, and a central controller uses the received aggregated waveform to update the policy parameters. We investigate the effect of noise and channel distortion on the convergence of the proposed algorithm, and establish the complexities of communication and sampling for finding an E-approximate stationary point. Finally, we present some simulation results to show the effectiveness of the algorithm.

This paper investigates the robustness and optimality of the multi-kernel correntropy (MKC) on linear regression. We first derive an upper error bound for a scalar regression problem in the presence of arbitrarily large outliers. Then, we find that the proposed MKC is related to a specific heavy-tail distribution, where its head shape is consistent with the Gaussian distribution while its tail shape is heavy-tailed and the extent of heavy-tail is controlled by the kernel bandwidth. Interestingly, when the bandwidth is infinite, the MKC-induced distribution becomes a Gaussian distribution, enabling the MKC to address both Gaussian and non-Gaussian problems by appropriately selecting correntropy parameters. To automatically tune these parameters, an expectation-maximization-like (EM) algorithm is developed to estimate the parameter vectors and the correntropy parameters in an alternating manner. The results show that our algorithm can achieve equivalent performance compared with the traditional linear regression under Gaussian noise, and significantly outperforms the conventional method under heavy-tailed noise. Both numerical simulations and experiments on a magnetometer calibration application verify the effectiveness of the proposed method.Note to Practitioners—The goal of this paper is to enhance the accuracy of conventional linear regression in handling outliers while maintaining its optimality under Gaussian situations. Our algorithm is formulated under the maximum likelihood estimation (MLE) framework, assuming the regression residuals follow a type of heavy-tailed noise distribution with an extreme case of Gaussian. The degree of the heavy tail is explored alternatingly using an Expectation-Maximization (EM) algorithm which converges very quickly. The robustness and optimality of the proposed approach are investigated and compared with the traditional approaches. Both theoretical analysis and experiments on magnetometer calibration demonstrate the superiority of the proposed method over the conventional methods. In the future, we will extend the proposed method to more general cases (such as nonlinear regression and classification) and derive new algorithms to accommodate more complex applications (such as with equality or inequality constraints or with prior knowledge of parameter vectors).

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.

We consider signal source localization from range-difference measurements. First, we give some readily-checked conditions on measurement noises and sensor deployment to guarantee the asymptotic identifiability of the model and show the consistency and asymptotic normality of the maximum likelihood (ML) estimator. Then, we devise an estimator that owns the same asymptotic property as the ML one. Specifically, we prove that the negative log-likelihood function converges to a function, which has a unique minimum and positive definite Hessian at the true source’s position. Hence, it is promising to execute local iterations, e.g., the Gauss-Newton (GN) algorithm, following a consistent estimate. The main issue involved is obtaining a preliminary consistent estimate. To this aim, we construct a linear least-squares problem via algebraic operation and constraint relaxation and obtain a closed-form solution. We then focus on deriving and eliminating the bias of the linear least-squares estimator, which yields an asymptotically unbiased and further consistent estimate. Noting that the bias is a function of the noise variance, we further devise a consistent noise variance estimator that involves a 3-order polynomial rooting. Based on the preliminary consistent location estimate, a one-step GN iteration suffices to achieve the same asymptotic property as the ML estimator. Simulation results demonstrate the superiority of our proposed algorithm in the large sample case.

We study the denial-of-service (DoS) attack power allocation optimization in a multiprocess cyber–physical system (CPS), where sensors observe different dynamic processes and send the local estimated states to a remote estimator through wireless channels, while a DoS attacker allocates its attack power on different channels as interference to reduce the wireless transmission rates, and thus degrading the estimation accuracy of the remote estimator. We consider two attack optimization problems. One is to maximize the average estimation error of different processes, and the other is to maximize the minimal one. We formulate these problems as Markov decision processes (MDPs). Unlike the majority of existing works where the attacker is assumed to have complete knowledge of the CPS, we consider an attacker with no prior knowledge of the wireless channel model and the sensor information. To address this uncertainty issue and the curse of dimensionality, we provide a learning-based attack power allocation algorithm stemming from the double deep Q-network (DDQN) method. First, with a defined partial order, the maximal elements of the action space are determined. By investigating the characteristic of the MDP, we prove that the optimal attack allocations of both problems belong to the set of these elements. This property reduces the entire action space to a smaller subset and speeds up the learning algorithm. In addition, to further improve the data efficiency and learning performance, we propose two enhanced attack power allocation algorithms which add two auxiliary tasks of MDP transition estimation inspired by model-based reinforcement learning, i.e., the next state prediction and the current action estimation. Experimental results demonstrate the versatility and efficiency of the proposed algorithms in different system settings compared with other algorithms, such as the conventional value iteration, double Q-learning, and deep Q-network.

In this paper, we propose a Bi-layer Prediction-based Reduction Branch (BP-RB) framework to speed up the process of finding a high-quality feasible solution for Mixed Integer Programming (MIP) problems. A graph convolutional network (GCN) is employed to predict binary variables' values. After that, a subset of binary variables is fixed to the predicted value by a greedy method conditioned on the predicted probabilities. By exploring the logical consequences, a learning-based problem reduction method is proposed, significantly reducing the variable and constraint sizes. With the reductive sub- MIP problem, the second layer GCN framework is employed to update the prediction for the remaining binary variables' values and to determine the selection of variables which are then used for branching to generate the Branch and Bound (B&B) tree. Numerical examples show that our BP-RB framework speeds up the primal heuristic and finds the feasible solution with high quality.

The REINFORCE algorithm \cite{williams1992simple} is popular in policy gradient (PG) for solving reinforcement learning (RL) problems. Meanwhile, the theoretical form of PG is from \cite{sutton1999policy}. Although both formulae prescribe PG, their precise connections are not yet illustrated. Recently, \citeauthor{nota2020policy} (\citeyear{nota2020policy}) have found that the ambiguity causes implementation errors. Motivated by the ambiguity and implementation incorrectness, we study PG from a perturbation perspective. In particular, we derive PG in a unified framework, precisely clarify the relation between PG implementation and theory, and echos back the findings by \citeauthor{nota2020policy}. Diving into factors contributing to empirical successes of the existing erroneous implementations, we find that small approximation error and the experience replay mechanism play critical roles.

In this paper we focus on the dynamic state estimation which harnesses a vast amount of sensing data harvested by multiple parties and recognize that in many applications, to improve collaborations between parties, the estimation procedure must be designed with the awareness of protecting participants' data and model privacy, where the latter refers to the privacy of key parameters of observation models. We develop a state estimation paradigm for the scenario where multiple parties with data and model privacy concerns are involved. Multiple parties monitor a physical dynamic process by deploying their own sensor networks and update the state estimate according to the average state estimate of all the parties calculated by a cloud server and security module. The paradigm taps additively homomorphic encryption which enables the cloud server and security module to jointly fuse parties' data while preserving the data privacy. Meanwhile, all the parties collaboratively develop a stable (or optimal) fusion rule without divulging sensitive model information. For the proposed filtering paradigm, we analyze the stabilization and the optimality. First, to stabilize the multi-party state estimator while preserving observation model privacy, two stabilization design methods are proposed. For special scenarios, the parties directly design their estimator gains by the matrix norm relaxation. For general scenarios, after transforming the original design problem into a convex semi-definite programming problem, the parties collaboratively derive suitable estimator gains based on the alternating direction method of multipliers (ADMM). Second, an optimal collaborative gain design method with model privacy guarantees is provided, which results in the asymptotic minimum mean square error (MMSE) state estimation. Finally, numerical examples are presented to illustrate our design and theoretical findings.

Recently, the lower limb exoskeletons which provide mobility for paraplegic patients to support their daily life have drawn much attention. However, the pilots are required to apply excessive force through a pair of crutches to maintain balance during walking. This paper proposes a novel gait trajectory generation algorithm for exoskeleton locomotion on flat ground and stair which aims to minimize the force applied by the pilot without increasing the degree of freedom (DoF) of the system. First, the system is modelled as a five-link mechanism dynamically for torque computing. Then, an optimization approach is used to generate the trajectory minimizing the ankle torque which is correlated to the supporting force. Finally, experiment is conducted to compare the different gait generation algorithms through measurement of ground reaction force (GRF) applied on the crutches.

Variable impedance control is advantageous for physical human-robot interaction to improve safety, adaptability and many other aspects. This paper presents a gain-scheduled variable stiffness control approach under strict frequency-domain constraints. Firstly, to reduce conservativeness, we characterize and constrain the impedance rendering, actuator saturation, disturbance/noise rejection and passivity requirements into their specific frequency bands. This relaxation makes sense because of the restricted frequency properties of the interactive robots. Secondly, a gain-scheduled method is taken to regulate the controller gains with respect to the desired stiffness. Thirdly, the scheduling function is parameterized via a nonsmooth optimization method. Finally, the proposed approach is validated by simulations, experiments and comparisons with a gain-fixed passivity-based PID method.

Privacy-preserving data aggregation (DA) in network systems, e.g., Internet of Things (IoT), is a challenging problem, considering the dynamic network topology, limited computing capacity, energy supply of IoT devices, etc. The difficulty is exaggerated when there exist dishonest nodes, and how to ensure privacy, accuracy, and robustness of the DA process against dishonest nodes remains an open issue. Different from the widely investigated cryptographic approaches, in this paper, we address this challenging problem by exploiting the distributed consensus technique. To mitigate the pollution from dishonest nodes, we propose an enhanced secure consensus-based DA (E-SCDA) algorithm that allows neighbors to detect dishonest nodes, and derive the error bound when there are undetectable dishonest nodes. We prove the convergence of the E-SCDA and show that the algorithm can preserve the privacy associated to nodes' initial states. Extensive simulations have shown that the proposed algorithm has a high convergence accuracy and low complexity, even when there exist dishonest nodes in the network.

Queue overflow of a dynamic queue system gives rise to the information loss (or packet loss) in the communication buffer or the decrease of throughput in the transportation network. This paper investigates a stochastic optimal control problem for dynamic queue systems when imposing probability constraints on queue overflows. We reformulate this problem as a Markov decision process (MDP) with safety constraints. We prove that both finite-horizon and infinite-horizon stochastic optimal control for MDP with such constraints can be transformed as a linear program (LP), respectively. Feasibility conditions are provided for the finite-horizon constrained control problem. Two implementation algorithms are designed under the assumption that only the state (not the state distribution) can be observed at each time instant. Simulation results compare optimal cost and state distribution among different scenarios, and show the probability constraint satisfaction by the proposed algorithms.

We consider the problem of warehouse multi-robot automation system in discrete-time and discrete-space configuration with focus on the task allocation and conflict-free path planning. We present a system design where a centralized server handles the task allocation and each robot performs local path planning distributively. A genetic-based task allocation algorithm is firstly presented, with modification to enable heuristic learning. A semi-complete potential field based local path planning algorithm is then proposed, named the recursive excitation/relaxation artificial potential field (RERAPF). A mathematical proof is also presented to show the semi-completeness of the RERAPF algorithm. The main contribution of this paper is the modification of conventional artificial potential field (APF) to be semi-complete while computationally efficient, resolving the traditional issue of incompleteness. Simulation results are also presented for performance evaluation of the proposed path planning algorithm and the overall system.

Most existing works investigate consensus with noise following a certain distribution, e.g., Gaussian distribution, with fixed expectation and variance, which may not be satisfied in practical applications. This paper investigates the discrete system consensus under bounded noise, which is important and practical problem. We first provide necessary and sufficient conditions for the convergence of consensus under bounded noise. To be more general, we derive an analytical bound to show the max-min difference between the nodes' states when the general consensus algorithm converges to a stable state. Then, a novel consensus algorithm, fast consensus under bounded noise (FCBN), is proposed to eliminate the accumulative error caused by the bounded noise. It is proved that FCBN has a faster convergence speed and a higher consensus accuracy than general consensus algorithms. Extensive simulations demonstrate the effectiveness of the proposed algorithm.

In this paper, the problem of state estimation over multiple parallel communication channels with multiplicative noise is investigated. The main novelty of this work lies in the introduction of the channel/estimator co-design framework which provides the estimator designer an additional freedom to allocate the channel capacities among different output channels. Under this co-design framework, we first study the state observation problem by reaching a conclusion that the minimum total channel capacity required for the output channels such that the estimation error remains bounded is given in terms of the topological entropy of the plant. We then look into the case of the optimal state estimation over channels with multiplicative noise. By a sequential design under the channel/estimator co-design framework, we show that the estimation error covariance of the optimal estimator is bounded under channel resource allocation if the total channel capacity is greater than the topological entropy.

In this paper, we are concerned with the consensus problem for multi-agent systems with time delay. The consensus problem is taken as a root finding problem in stochastic approximation to deal with. Via choosing an appropriate regression function, the multi-agent system with a finite delay achieves asymptotic consensus. Furthermore, we establish a relationship between the convergence rate and the exponent of the step size of the algorithm. It is worth mentioning that the convergence rate only depends on the step size rather than the delay.

This paper considers decentralized state estimation subject to communication constraints. A group of agents measure the state of a process and obtain their state estimates by exchanging data with one another. Due to the communication constraint, only a few communication channels are available. The main objective of this paper is to allocate these channels among the agents so as to minimize their average estimation errors. Assuming the agents have the same sensing capability, we provide the optimal allocation strategy for both directed and un-directed communication channels.

In this paper, we consider the problem of sensor scheduling with limited resources. Two sensors are used to measure the state of a discrete-time linear process. We assume that each sensor has a maximum duty cycle and at most one sensor can communicate with a remote estimator at each time step due to the limited communication bandwidth. When a sensor is scheduled to send data, it sends the most recent D measurement data to the estimator. Upon receiving the measurement data from the sensors, the estimator computes the optimal estimate of the state of the process. We first present a necessary condition for a sensor scheduling scheme to be optimal. Based on this necessary condition, we construct an optimal scheduling scheme that minimizes the estimation error at the estimator and at the same time satisfies the energy and communication bandwidth constraints. We also provide a sufficient condition on the minimum D such that an optimal scheduling scheme can be constructed. Examples are provided throughout the paper to demonstrate the results developed.

This paper is concerned with estimation problem for discrete-time systems with packet dropping. A new optimal filter is derived by minimizing the mean squared estimation error. An optimal smoother is also derived in a similar way. Both estimators are designed by solving one deterministic Riccati equation. Both the convergence of the estimation error covariance and mean square stability of the estimator are proved under standard assumption. It is shown that the new estimator has smaller error covariance and has wider applications as compared with the MMSE estimator. One of the key techniques adopted in this paper is the introduction of the innovation sequence for the multiplicative noise systems.

We consider the problem of state estimation of a discrete time process over a packet dropping network. Previous pioneering work on Kalman filtering with intermittent observations is concerned with the asymptotic behavior of E[Pk], i.e., the expected value of the error covariance, for a given packet arrival rate. We consider a different performance metric, Pr[Pkles M], i.e., the probability that Pkis bounded by a given M, and we derive lower and upper bounds on Pr[Pkles M]. We are also able to recover the results in the literature when using Pr[Pkles M] as a metric for scalar systems. Examples are provided to illustrate the theory developed in the paper.