State Estimation In Sensor Networks Research Articles

Encoding–decoding-based distributed state estimation over sensor networks with limited sensing range under DoS attacks

This paper is concerned with encoding–decoding-based distributed state estimation over sensor networks under DoS attacks. Different from most of the existing research results on distributed state estimation for sensor networks, where all sensors are assumed to have sufficiently wide sensing ranges, this paper considers sensors with limited sensing ranges. Therefore, the problem studied has more practical significance. To save the limited bandwidth resources of sensor networks, a two-channel encoding–decoding scheme (EDS) based on probability is proposed for each node to compress the transmitted data to an acceptable range, where independent DoS attacks are launched randomly on the communication channels between nodes. Then, a distributed state estimator with limited sensing ranges under DoS attacks in the presence of both the sensor-estimator channel EDS and the node–node channel EDS is constructed under the criterion of minimum mean-square error. Furthermore, considering the real-time changes of the communication topology resulting from independent DoS attacks and the uncertainty introduced by the node–node channel EDS, the upper bound of the expected estimation error covariance is derived and the boundedness of the upper bound is analyzed under given assumption conditions. Finally, a numerical example is exhibited to illustrate the effectiveness of the designed algorithm.

Optimal Distributed Finite-Time Fusion Method for Multi-Sensor Networks under Dynamic Communication Weight.

Aiming at the problem of distributed state estimation in sensor networks, a novel optimal distributed finite-time fusion filtering method based on dynamic communication weights has been developed. To tackle the fusion errors caused by incomplete node information in distributed sensor networks, the concept of limited iterations of global information aggregation was introduced, namely, fast finite-time convergence techniques. Firstly, a local filtering algorithm architecture was constructed to achieve fusion error convergence within a limited number of iterations. The maximum number of iterations was derived to be the diameter of the communication topology graph in the sensor network. Based on this, the matrix weight fusion was used to combine the local filtering results, thereby achieving optimal estimation in terms of minimum variance. Next, by introducing the generalized information quality (GIQ) calculation method and associating it with the local fusion result bias, the relative communication weights were obtained and embedded in the fusion algorithm. Finally, the effectiveness and feasibility of the proposed algorithm were validated through numerical simulations and experimental tests.

Event-Triggered Consensus for Continuous-Time Distributed Estimation

Distributed sensor networks have gained interest thanks to the developments in processing power and communications. Event-triggering mechanisms can be useful in reducing communication between the nodes of the network, while still ensuring an adequate behaviour of the system. However, very little attention has been given to continuous-time systems in this context. In this work, we propose a strategy for distributed state estimation in sensor networks, based on average dynamic consensus of the continuous measurements. While communication between nodes is discrete and heavily reduced due to the event-triggering mechanism, our method ensures that the nodes are still able to produce a continuous estimate of the global average measurement and the state of the plant, within some tuneable error bounds.

Distributed Robust Kalman Filters under Model Uncertainty and Multiplicative Disturbance

This paper considers the problem of distributed robust state estimation for sensor networks in the presence of model uncertainty and multiplicative noise. More precisely, we assume that the modeling uncertainty, i.e., the actual state-space model belongs to an ambiguity set or a set of convex polytopic uncertain parameters. Several robust Kalman filters are proposed based on projection theorem, variance-constrained optimization, and robust mean square error estimation with different types of ambiguity sets. Stability analysis and simulation example verify the presented distributed robust filters.

Distributed moving horizon state estimation for sensor networks with low computation capabilities

This paper focuses on distributed state estimation for sensor network observing a discrete-time linear system. The provided solution is based on a Distributed Moving Horizon Estimation (DMHE) algorithm considering a pre-estimating Luenberger observer in the formulation of the local problem solved by each sensor. This leads to reduce the computation load, while preserving the accuracy of the estimation. Moreover, observability properties of local sensors are used for tuning the weights related to consensus information fusion built on a rank-based condition, in order to improve the convergence of the estimation error. Results obtained by Monte Carlo simulations are provided to compare the performance with existing approaches, in terms of accuracy of the estimations and computation time.

Distributed High-Degree Cubature Information Filter With Embedded Hybrid Consensus

To settle the problem of distributed nonlinear state estimation in sensor networks with naive nodes, a novel distributed high-degree cubature information filter with embedded hybrid consensus (DHCIF) is proposed. The multi-dimensional Gaussian weighted integrals involved in the filtering process are approximated by the fifth-degree cubature rule. A novel scheme, with consideration for the predicted measurement errors, is applied to compute the information contribution. With parallel consensus performed on both prior and measurement information, the proposed DHCIF is derived. The stability analysis with regard to consistency and boundedness of estimation errors for the proposed DHCIF is also developed. Finally, the effectiveness and advantage of the proposed DHCIF is validated by a typical maneuvering target tracing scenario. The experimental results indicate that the proposed DHCIF outperforms the existing algorithms in the aspects of estimation accuracy, consistency and consensus at the cost of a little more extra computation burden.

Hybrid Consensus-Based Cubature Kalman Filtering for Distributed State Estimation in Sensor Networks

In this paper, the high-dimensional distributed state estimation problem is investigated for a class of sensor networks within the cubature Kalman filtering (CKF) framework. The network consists of two types of nodes, i.e., communication ones and sensor ones. First, a hybrid consensus-based cubature Kalman filtering (HCCKF) is developed by blending the two existing approaches, namely, consensus on measurements (CM) and consensus on information (CI). As a result, the proposed filtering algorithm has complementary features of CM and CI, which turns out to be a better solution to the distributed state estimation problem. Secondly, estimation errors in HCCKF are proved to be exponentially bounded in mean square. Finally, a target tracking case-study in an example sensor network is given to demonstrate the effectiveness of the proposed HCCKF.

Analysis of the optimal sampling rate for state estimation in sensor networks with delays

When addressing the problem of state estimation in sensor networks, the effects of communications on estimator performance are often neglected. High accuracy requires a high sampling rate, but this leads to higher channel load and longer delays, which in turn worsens estimation performance. This paper studies the problem of determining the optimal sampling rate for state estimation in sensor networks from a theoretical perspective that takes into account traffic generation, a model of network behaviour and the effect of delays. Some theoretical results about Riccati and Lyapunov equations applied to sampled systems are derived, and a solution was obtained for the ideal case of perfect sensor information. This result is also interesting for non-ideal sensors, as in some cases it works as an upper bound of the optimisation solution.

Event-triggered robust distributed state estimation for sensor networks with state-dependent noises

This paper is concerned with the event-triggered distributed state estimation problem for a class of uncertain stochastic systems with state-dependent noises and randomly occurring uncertainties over sensor networks. An event-triggered communication scheme is proposed in order to determine whether the measurements on each sensor should be transmitted to the estimators or not. The norm-bounded uncertainty enters into the system in a random way. Through available output measurements from not only the individual sensor but also its neighbouring sensors, a sufficient condition is established for the desired distributed estimator to ensure that the estimation error dynamics are exponentially mean-square stable. These conditions are characterized in terms of the feasibility of a set of linear matrix inequalities, and then the explicit expression is given for the distributed estimator gains. Finally, a simulation example is provided to show the effectiveness of the proposed event-triggered distributed state estimation scheme.

Squared‐root cubature information consensus filter for non‐linear decentralised state estimation in sensor networks

Distributed analysis of target kinematics captured by a large network of sensors has received significant attention lately. Tracking moving targets in nonlinear systems is one of the most fundamental tasks in this regard and information-type consensus filters (ICFs) have been applied to this problem. To improve the estimate performance, a squared-root cubature information filter which can avoid numerically sensitive matrix operations such as matrix square-rooting and inversion has been developed firstly. And then, based on this filter, a decentralised information filtering algorithm is proposed in an improved consensus framework. Specifically, consensus update at each time-cycle in the modified consensus scheme is computed in two steps, first towards the predicted value and then towards the final information estimate update, which can improve average estimation accuracy and speed the average consensus. Besides the basic merits of the traditional ICFs, the resulting algorithm is more scalable and robust. Simulation results clearly show the advantage of the proposed algorithm compared with existing ICFs in the considered application scenario.

Distributed state estimation in sensor networks with event-triggered communication

This paper proposes an event-based approach to distributed state estimation for nonlinear discrete time-delay systems over sensor networks. First, an event detector is configured at each sensor node to decide when the node should send its sampled measurement output to its neighbors across the communication network. Then, sufficient conditions for the existence of an admissible event-based distributed state estimator are established. A effective algorithm for designing both the distributed state estimator design and the parameters in the event condition could be achieved by solving a set of linear matrix inequalities. Finally, two numerical examples are provided to verify the designed event-based distributed state estimator.

Distributed state estimation in sensor networks with randomly occurring nonlinearities subject to time delays

This article is concerned with a new distributed state estimation problem for a class of dynamical systems in sensor networks. The target plant is described by a set of differential equations disturbed by a Brownian motion and randomly occurring nonlinearities (RONs) subject to time delays. The RONs are investigated here to reflect network-induced randomly occurring regulation of the delayed states on the current ones. Through available measurement output transmitted from the sensors, a distributed state estimator is designed to estimate the states of the target system, where each sensor can communicate with the neighboring sensors according to the given topology by means of a directed graph. The state estimation is carried out in a distributed way and is therefore applicable to online application. By resorting to the Lyapunov functional combined with stochastic analysis techniques, several delay-dependent criteria are established that not only ensure the estimation error to be globally asymptotically stable in the mean square, but also guarantee the existence of the desired estimator gains that can then be explicitly expressed when certain matrix inequalities are solved. A numerical example is given to verify the designed distributed state estimators.

Robust distributed state estimation for sensor networks with multiple stochastic communication delays

This article is concerned with the robust distributed state estimation problem for a class of uncertain sensor networks with multiple stochastic communication delays. A sequence of mutually independent random variables obeying the Bernoulli distribution is introduced to account for the randomly occurred communication delays. Both the discrete-time target plant and the sensor model are subject to parameter uncertainties as well as stochastic disturbance. The parameter uncertainties are norm-bounded and enter both the system and the measurement matrices. The external stochastic disturbance is given in the form of a scalar Wiener process. Through available output measurements from not only each individual sensor but also its neighbouring sensors, we aim to design distributed state estimators in order to approximate the state of the target plant. By using the Kronecker product, stochastic analysis is carried out to derive a sufficient criterion ensuring the estimation error systems to be convergent in the mean square sense for all randomly occurred delays, admissible stochastic disturbance and parameter uncertainties. Then, an explicit expression of the individual estimator is given in terms of the solution to a convex optimisation problem that can be easily solved by using the semi-definite programme method. A numerical example is given at the end of this article to demonstrate the usefulness of the developed theoretical results.

Adaptive Kalman Filtering in Networked Systems With Random Sensor Delays, Multiple Packet Dropouts and Missing Measurements

In this paper, adaptive filtering schemes are proposed for state estimation in sensor networks and/or networked control systems with mixed uncertainties of random measurement delays, packet dropouts and missing measurements. That is, all three uncertainties in the measurement have certain probability of occurrence in the network. The filter gains can be derived by solving a set of recursive discrete-time Riccati equations. Examples are presented to demonstrate the applicability and performances of the proposed schemes.