Networked Multi-sensor Fusion Systems Research Articles

Distributed Event-Triggered Nonlinear Fusion Estimation under Resource Constraints

This paper studies the event-triggered distributed fusion estimation problems for a class of nonlinear networked multi-sensor fusion systems without noise statistical properties. In practice, the sensor-to-remote estimator channel and the smart sensor-to-fusion center channel of the communication network will be faced with some resource constraint problems. To meet the finite communication resources during the information transmission, an event-triggered strategy and a dimensionality reduction strategy are introduced in a unified network framework to reduce communication traffic. Since the reduction of communication information will inevitably degenerate the estimation performance, two kinds of compensation strategies in terms of a unified model are proposed to restructure the untransmitted information. Then, the local/fusion estimators are designed based on the compensation information. Moreover, the linearization errors caused by the Taylor expansion are modeled by the state-dependent matrices with uncertain parameters when establishing estimation error systems, and then different robust recursive optimization problems are established to determine the estimator gains and the fusion criteria. Meanwhile, the stability conditions are also presented such that the square errors of the designed nonlinear estimators are asymptotically bounded. Finally, a vehicle localization system is employed to demonstrate the effectiveness and advantages of the proposed methods.

Event-Triggered Distributed Fusion Estimation of Networked Multisensor Systems With Limited Information

In this paper, the event-triggered distributed Kalman filtering problem is considered for a class of networked multisensor fusion systems (NMFSs) under sensor energy and network bandwidth constraint. A general event-triggered scheme is employed for the NMFSs to reduce the energy consumption and communication burden between the sensor nodes and fusion center (FC) under the communication bandwidth constraints. Local estimation information is allowed to transmit partial components to FC over the network with limited bandwidth. A group of binary variables are introduced to describe the component transmitting process when the triggering condition is violated. Furthermore, the untransmitted local estimation signals are compensated by the previous transmitted one, and a recursively event-triggered distributed fusion Kalman filter in the linear minimum mean square error sense is designed from the restructured local unbiased estimators. At each time instant, a set of binary variables are determined by an optimal judgement criterion. Finally, a simulation example is provided to illustrate the effectiveness and advantages of the proposed methods.

Networked Fusion Estimation under Denial-of-Service Attacks

This paper studies the fusion estimation problem for a class of networked multi-sensor fusion systems (NMFSs) under bandwidth constraints and denial-of-service (DoS) attacks. The dimensionality reduction strategy is used to satisfy the finite bandwidth, while the prediction compensation strategy is used to reduce the information loss caused by attacks. Based on dimensionality reduction and DoS attack model, a recursive distributed Kalman fusion estimator (DKFE) is designed for the addressed NMFSs. An explicit form of the dimensionality reduction is given against DoS attacks, while an effective attack strategy is proposed for the attacker. An illustrative example is given to show the effectiveness of the proposed methods.

Distributed Mixed H2/H Fusion Estimation With Limited Communication Capacity

This technical note studies the problem of distributed fusion estimation for a class of networked multi-sensor fusion systems (NMFSs) with limited communication capacity and affected by sensor noises and disturbances. A novel data compression strategy is proposed to reduce communication cost, which leads to a stochastic model describing the communication capacity. To obtain an optimal weighting fusion criterion, the distributed mixed $H_{2}/H_{\infty}$ fusion estimation problem is converted into a convex optimization problem by using Lyapunov theory and matrix analysis approach, which can be easily solved by standard software packages. The obtained fusion estimator gains are time-invariant, which will not increase computation cost of the fusion center. An illustrative example is given to show the effectiveness of the proposed methods.

Networked fusion kalman filtering with multiple uncertainties

This paper investigates the problem of fusion filtering for a class of networked multisensor fusion systems with multiple uncertainties, including sensor failures, stochastic parameter uncertainties, random observation delays, and packet dropouts. A novel model is proposed to describe the random observation delays and packet dropouts, and a robust optimal fusion filter for the addressed networked multisensor fusion systems is designed using the innovation analysis method. The dimension of the designed filter is the same as that of the original system, which helps to reduce computation cost compared with the augmentation method. Moreover, robust reduced-dimension observation-fusion Kalman filters are proposed to further reduce the computation burden. Note that the designed fusion filter gain matrices can be computed off-line, as they depend only on the upper bounds of random delays and on the occurrence probabilities of delays and sensor failures. Some sufficient conditions are presented for stability and optimality of the designed fusion filters, and a steady-state fusion filter is also given for the networked multisensor fusion systems. Simulations show the effectiveness of the proposed fusion filters.

Distributed Fusion Estimation With Communication Bandwidth Constraints

This technical note is concerned with the distributed Kalman filtering problem for a class of networked multi-sensor fusion systems (NMFSs) with communication bandwidth constraints. To satisfy finite communication bandwidth, only partial components of the local vector estimation signals are transmitted to the fusion center (FC) at each time step, where multiple binary variables are introduced to model this component transmitting process. A novel compensation strategy is proposed to restructure the untransmitted components of each local estimate at the FC end, and a recursive distributed fusion kalman filter (DFKF) is designed in the linear minimum variance sense. Moreover, a simple suboptimal judgement criterion is proposed to determine a group of binary variables such that the mean square error of the designed DFKF is minimal at each time step. An illustrative example is given to show the effectiveness of the proposed methods.

Distributed Fusion Estimation With Missing Measurements, Random Transmission Delays and Packet Dropouts

This technical note is concerned with the distributed Kalman filtering problem for a class of networked multi-sensor fusion systems (NMFSs) with missing sensor measurements, random transmission delays and packet dropouts. A novel stochastic model is proposed to describe the transmission delays and packet dropouts, and an optimal distributed fusion Kalman filter (DFKF) is designed based on the optimal fusion criterion weighted by matrices. Some sufficient conditions are derived such that the MSE of the designed DFKF is bounded or convergent. Moreover, steady-state DFKF is also presented for the NMFSs. An illustrative example is given to demonstrate the effectiveness of the proposed results.

Distributed Finite-Horizon Fusion Kalman Filtering for Bandwidth and Energy Constrained Wireless Sensor Networks

This paper is concerned with the distributed finite-horizon fusion Kalman filtering problem for a class of networked multi-sensor fusion systems (NMFSs) in a bandwidth and energy constrained wireless sensor network. To satisfy the finite communication bandwidth, only partial components of each local vector estimate are allowed to be transmitted to the fusion center (FC) at a particular time, while each sensor intermittently sends information to the FC for reducing energy consumptions. At the FC end, a novel compensation strategy is proposed to compensate the untransmitted components of each local estimates, then a recursively distributed fusion Kalman filter (DFKF) is derived in the linear minimum variance sense. Notice that the designed DFKF update does not need to know the transmitting situation of each component at a particular time, which means that the proposed fusion estimation algorithm is easily implemented for the addressed NMFSs. Since the performance of the designed DFKF is dependent on the selecting probability of each component, some criteria for the choice of probabilities are derived such that the mean squared errors (MSEs) of the designed DFKF are bounded or convergent. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed method.

Distributed H∞ fusion filtering with communication bandwidth constraints

This paper is concerned with the distributed H∞ fusion filtering problem (DHFFP) for a class of networked multi-sensor fusion systems with communication bandwidth constraints. Due to the limited bandwidth, only finite-level quantized sensor messages are sent to the fusion center, and multiple finite-level logarithmic quantizers are introduced to describe the above quantization strategy. In this sense, the DHFFP is inherent the co-design of the fusion parameters and quantization parameters. With the aid of the discrete-time bounded real lemma, the co-design problem is converted into a convex optimization problem over all the aforementioned parameters, which can be easily solved by standard software packages. It turns out that the performance of the designed distributed fusion filter is superior to that of each local quantized estimate. Finally, a numerical example is given to show the effectiveness of the proposed method.

Multi-rate stochastic [formula omitted] filtering for networked multi-sensor fusion

This paper presents a multi-rate filtering problem for a class of networked multi-sensor fusion systems with packet dropouts (PDs): the state evolves according to a linear discrete-time model with norm-bounded unknown inputs (UIs), and its underlying period is h ; the p sensors are distributively deployed with different sampling periods n 1 h , … , n p h ; multi-rate sensor measurements, corrupted by UIs, are subject to stochastic PDs in the transmission to a fusion center for state estimation; the estimation is updated at the period m h . Different from the single-rate estimator design with PDs which are treated as stochastic parameters, a UI observer is proposed where PDs are represented as zero-mean white input noises of the linear time-variant estimation error system. The results on the existence of a stable observer are proposed. Due to insufficient design freedom for absolute error decoupling, we turn to designing an observer-based stochastic H ∞ filter. A numerical example of distributive multi-sensor target tracking is given to illustrate the proposed filter.