Distributed Kalman Filtering Fusion Research Articles

Distributed Robust Dimensionality Reduction Fusion Estimation Under DoS Attacks and Uncertain Covariances

This paper is concerned with the networked distributed fusion estimation problem under denial-of-service (DoS) attacks, where the noise covariances are unknown but bounded, and the distribution information of DoS attacks is not required to be known. Based on the dimensionality reduction and compensation model, the local Kalman filter (LKF) with unknown covariances is designed by the maximum and minimum robust estimation criterion, while the distributed fusion Kalman filter (DFKF) is derived from the optimal weighted fusion criterion. Moreover, the robustness of the developed DFKF is also analyzed in the presence of DoS attacks. Finally, an illustrative example is exploited to demonstrate the effectiveness 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.

Distributed fusion Kalman filtering under binary sensors

SummaryBinary sensors are special sensors that only transmit one‐bit information at each time and have been widely applied to environmental awareness and medical monitoring. This paper is concerned with the distributed fusion Kalman filtering problem for a class of binary sensor systems. A novel uncertainty approach is proposed to better extract valid information from binary sensors at switching instant. By minimizing a local estimation error covariance, the local robust Kalman estimates are firstly obtained. Then, the distributed fusion Kalman filter is designed by resorting to the covariance intersection fusion criterion. Finally, a blood oxygen content model is employed to show the effectiveness of the proposed methods.

Optimal distributed Kalman filtering fusion for multirate multisensor dynamic systems with correlated noise and unreliable measurements

An optimal distributed fusion estimation problem is concerned in this study for a kind of linear dynamic multirate sensors systems with correlated noise and stochastic unreliable measurements. The system is formulated at the finest scale with multiple sensors at different scales observing a common target independently with different sampling rates. The noise between different sensors is relevant, moreover, is also correlated with the system noise. The authors derive the local state estimation algorithms under the circumstance of total reliable measurements and stochastic unreliable measurements occur occasions, and the optimal distributed Kalman filter fusion algorithm, respectively. The authors provide a simulation example to illustrate the effectiveness and feasibility of the proposed algorithm.

Distributed state estimation for stochastic non‐linear systems with random delays and packet dropouts

A novel distributed fusion Kalman filter is proposed for a class of stochastic non-linear systems with multi-step transmission delays and packet dropouts. The stochastic non-linearities described by statistical means enter into both state equation and measurement equation, and some Bernoulli distributed random variables are introduced to model the delayed measurements. Using the measurement reorganisation approach instead of state augmentation, the addressed system is transformed into a delay-free one. For each subsystem, the optimal local estimators are designed via the innovation analysis method. The filtering error cross-covariance matrices between any two local filters are then obtained. On this basis, a distributed fusion filter is derived by means of matrix-weighted fusion estimation criterion. The effects of stochastic non-linearities, random delays and packet dropouts on the performance of the filter are all considered in the proposed algorithms. Moreover, some sufficient conditions that guarantee the convergence of the estimation error covariance matrices are established. Finally, a numerical example is given to illustrate the effectiveness of the developed algorithms.

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.

Fusion Kalman filtration for distributed multisensor systems

Abstract In the paper, fusion state hierarchical filtration for a multisensor system is considered. An optimal global Kalman filter is realized by a central node in the information form. The state estimate depends on local information that should be sent by local nodes. Two information structures are considered in the paper. In the first case local estimates are based on the local measurement information. It leads to distributed Kalman filter fusion that is well known in a literature. In the second case local node has additionally global information of the system with one step delay. A synthesis of local filters is presented. An advantage of this structure is discussed.

Local node selection for target tracking based on underwater wireless sensor networks

Traditional sonar-array-based target tracking algorithms may be unsuitable for on-demand tracking missions, since they assume that the sonar arrays should be towed or mounted by a submarine or a ship. Alternatively, underwater wireless sensor networks can offer a promising solution approach. First, each underwater node is battery-powered, so saving energy expenditure is a critical issue. Instead of keeping all sensor nodes active, this paper provides a local node selection (LNS) scheme which increases energy efficiency by waking up only a small part of nodes at each time. Second, considering node's limited computing ability and the real-time requirement for the tracking algorithm, instead of employing the centralised fusion structure, we utilise the distributed Kalman filtering fusion with feedback in this paper. Finally, instead of assuming one sensor node can uniquely determine target's location, a more practical range-only measurement model is proposed. Then the LNS scheme and distributed fusion with feedback are extended to our range-only measurement model. The simulation results demonstrate the efficiency of our scheme.

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 Fusion Kalman Self-Turning Filter

This paper puts forward an optimal and distributed fusion Kalman filter based on the Riccati equation, optimal and distributed fusion The Kalman filter has fewer calculated dimensions and less calculated amount than the centralized global optimal Kalman filter. Therefore, it has greater effect in the practice. Present the distributed local self-correcting Kalman filter at the same time. The simulation examples showed its validity.

Distributed Kalman filtering fusion with packet loss or intermittent communications from local estimators to fusion center

This paper considers the distributed Kalman filtering fusion with passive packet loss or initiative intermittent communications from local estimators to a fusion center while the process noise does exist. We present a specific condition-properly short intermittent communication period/packet loss under which a proposed optimal distributed algorithm can be equivalent to the corresponding centralized Kalman filtering fusion with complete communications even if the process noise does exist. When this condition is not satisfied, using some recent results, a suboptimal distributed fusion algorithm is motivated. Numerical examples show that this algorithm still works well and significantly better than the standard distributed Kalman filtering fusion with a proper modification due to packet loss even if the condition is violated strongly.

Optimal distributed Kalman filtering fusion for a linear dynamic system with cross-correlated noises

In this article, we study the distributed Kalman filtering fusion problem for a linear dynamic system with multiple sensors and cross-correlated noises. For the assumed linear dynamic system, based on the newly constructed measurements whose measurement noises are uncorrelated, we derive a distributed Kalman filtering fusion algorithm without feedback, and prove that it is an optimal distributed Kalman filtering fusion algorithm. Then, for the same linear dynamic system, also based on the newly constructed measurements, a distributed Kalman filtering fusion algorithm with feedback is proposed. A rigorous performance analysis is dedicated to the distributed fusion algorithm with feedback, which shows that the distributed fusion algorithm with feedback is also an optimal distributed Kalman filtering fusion algorithm; the P matrices are still the estimate error covariance matrices for local filters; the feedback does reduce the estimate error covariance of each local filter. Simulation results are provided to demonstrate the validity of the newly proposed fusion algorithms and the performance analysis.

Optimal Distributed Kalman Filtering Fusion Algorithm Without Invertibility of Estimation Error and Sensor Noise Covariances

Although the globally optimal distributed Kalman filtering fusion has been proposed and studied for more than twenty years, the invertibility of estimation error and measurement noise covariances has been always a restrictive assumption to derive a globally optimal distributed Kalman filtering fusion equivalent to the centralized Kalman filtering fusion. This letter proposes an optimal distributed Kalman filtering fusion algorithm for general dynamic systems without invertibility of estimation error and measurement noise covariances. The new algorithm uses the convex combination fusion, whose fusion weights are recursively given. Computer experiments show that the performance of this fusion algorithm is very likely to be equivalent to that of the centralized Kalman filtering fusion. In practice, the new fusion algorithm can be applied to any distributed Kalman filtering fusion, such as the equality constrained distributed Kalman filtering fusion.

The Self-Tuning Distributed Information Fusion Kalman Filter for ARMA Signals

For the multisensor Autoregressive Moving Average (ARMA) signals with unknown model parameters and noise variances, using the Recursive Instrumental Variable (RIV) algorithm, the correlation method and the Gevers-Wouters algorithm with dead band, the fused estimators of unknown model parameters and noise variances can be obtained. Then substituting them into optimal fusion signal filter weighted by scalars, a self-tuning distributed fusion Kalman filter is presented. Using the dynamic error system analysis (DESA) method, it is rigorously proved that the self-tuning fused Kalman signal filter converges to the optimal fused Kalman signal filter, so that it has asymptotic optimality. A simulation example shows its effectiveness.

Globally Optimal Multisensor Distributed Random Parameter Matrices Kalman Filtering Fusion with Applications

This paper proposes a new distributed Kalman filtering fusion with random state transition and measurement matrices, i.e., random parameter matrices Kalman filtering. It is proved that under a mild condition the fused state estimate is equivalent to the centralized Kalman filtering using all sensor measurements; therefore, it achieves the best performance. More importantly, this result can be applied to Kalman filtering with uncertain observations including the measurement with a false alarm probability as a special case, as well as, randomly variant dynamic systems with multiple models. Numerical examples are given which support our analysis and show significant performance loss of ignoring the randomness of the parameter matrices.