Robust Distributed Kalman Filter for Wireless Sensor Networks with Uncertain Communication Channels (original) (raw)

Problem Statement and Kalman-Consensus Filter 2 . 1 . Problem Statement

2014

We address a state estimation problem over a large-scale sensor network with uncertain communication channel. Consensus protocol is usually used to adapt a large-scale sensor network. However, when certain parts of communication channels are broken down, the accuracy performance is seriously degraded. Specifically, outliers in the channel or temporal disconnection are avoided via proposed method for the practical implementation of the distributed estimation over large-scale sensor networks. We handle this practical challenge by using adaptive channel status estimator and robust L1-norm Kalman filter in design of the processor of the individual sensor node. Then, they are incorporated into the consensus algorithm in order to achieve the robust distributed state estimation. The robust property of the proposed algorithm enables the sensor network to selectively weight sensors of normal conditions so that the filter can be practically useful.

Distributed Kalman filtering based on consensus strategies

IEEE Journal on Selected Areas in Communications, 2000

In this paper, we consider the problem of estimating the state of a dynamical system from distributed noisy measurements. Each agent constructs a local estimate based on its own measurements and estimates from its neighbors. Estimation is performed via a two stage strategy, the first being a Kalman-like measurement update which does not require communication, and the second being an estimate fusion using a consensus matrix. In particular we study the interaction between the consensus matrix, the number of messages exchanged per sampling time, and the Kalman gain. We prove that optimizing the consensus matrix for fastest convergence and using the centralized optimal gain is not necessarily the optimal strategy if the number of exchanged messages per sampling time is small. Moreover, we showed that although the joint optimization of the consensus matrix and the Kalman gain is in general a non-convex problem, it is possible to compute them under some important scenarios. We also provide some numerical examples to clarify some of the analytical results and compare them with alternative estimation strategies.

Distributed Kalman filtering using consensus strategies

2007 46th IEEE Conference on Decision and Control, 2007

In this paper, we consider the problem of estimating the state of a dynamical system from distributed noisy measurements. Each agent constructs a local estimate based on its own measurements and estimates from its neighbors. Estimation is performed via a two stage strategy, the first being a Kalman-like measurement update which does not require communication, and the second being an estimate fusion using a consensus matrix. In particular we study the interaction between the consensus matrix, the number of messages exchanged per sampling time, and the Kalman gain. We prove that optimizing the consensus matrix for fastest convergence and using the centralized optimal gain is not necessarily the optimal strategy if the number of message exchange per sampling time is small. Moreover, we prove that under certain conditions the optimal consensus matrix should be doubly stochastic. We also provide some numerical examples to clarify some of the analytical results.

Distributed Kalman filter with embedded consensus filters

Decision and Control, 2005 and 2005 …, 2005

The problem of distributed Kalman filtering (DKF) for sensor networks is one of the most fundamental distributed estimation problems for scalable sensor fusion. This paper addresses the DKF problem by reducing it to two separate dynamic consensus problems in terms of weighted measurements and inverse-covariance matrices. These to data fusion problems are solved is a distributed way using lowpass and band-pass consensus filters. Consensus filters are distributed algorithms that allow calculation of average-consensus of time-varying signals. The stability properties of consensus filters is discussed in a companion CDC '05 paper . We show that a central Kalman filter for sensor networks can be decomposed into n micro-Kalman filters with inputs that are provided by two types of consensus filters. This network of micro-Kalman filters collectively are capable to provide an estimate of the state of the process (under observation) that is identical to the estimate obtained by a central Kalman filter given that all nodes agree on two central sums. Later, we demonstrate that our consensus filters can approximate these sums and that gives an approximate distributed Kalman filtering algorithm. A detailed account of the computational and communication architecture of the algorithm is provided. Simulation results are presented for a sensor network with 200 nodes and more than 1000 links.

Low-Power Distributed Kalman Filter for Wireless Sensor Networks

EURASIP Journal on Embedded Systems, 2011

Distributed estimation algorithms have attracted a lot of attention in the past few years, particularly in the framework of Wireless Sensor Network (WSN). Distributed Kalman Filter (DKF) is one of the most fundamental distributed estimation algorithms for scalable wireless sensor fusion. Most DKF methods proposed in the literature rely on consensus filters algorithm. The convergence rate of such distributed consensus algorithms typically depends on the network topology. This paper proposes a low-power DKF. The proposed DKF is based on a fast polynomial filter. The idea is to apply a polynomial filter to the network matrix that will shape its spectrum in order to increase the convergence rate by minimizing its second largest eigenvalue. Fast convergence can contribute to significant energy saving. In order to implement the DKF in WSN, more power saving is needed. Since multiplication is the atomic operation of Kalman filter, so saving power at the multiplication level can significantly impact the energy consumption of the DKF. This paper also proposes a novel light-weight and low-power multiplication algorithm. The proposed algorithm aims to decrease the number of instruction cycles, save power, and reduce the memory storage without increasing the code complexity or sacrificing accuracy.

Improving Robustness of Distributed Filtering for Sensor Networks Using FIR Filtering

—Robustness is required from an estimator to provide better performance if a wireless sensor network (WSN) operates under harsh conditions with incomplete information about noise. This paper shows that robustness of the WSN can be improved by using the distributed unbiased finite impulse response (UFIR) filter rather than the traditional distributed Kalman filter (KF), both based on the average consensus. Unlike the KF, the UFIR filter completely ignores the noise statistics and initial values which are typically not well known. As an example, we consider a vehicle travelling along a circular trajectory under unpredictable impacts and errors in the noise statistics. A case of impulsive noise generated by manufacturing process is also considered.

Fault tolerant distributed estimation in wireless sensor networks

Journal of Network and Computer Applications, 2016

In distributed wireless sensor networks (WSNs), each sensor node estimates the global parameter from the local data in distributed manner. An iterative distributed estimation algorithm is used where the diffusion cooperation scheme is incorporated. Presence of faulty sensor node in the network leads to inaccurate estimation in the conventional error squared based distributed algorithms. Therefore, a fault tolerant distributed estimation in WSNs is proposed here when faulty sensor nodes are present in the network and the network is not aware of them. For this, a robust diffusion estimation algorithms using robust function like Huber's cost function and error saturation non linearity are proposed here in order to make the network fault tolerant. Further, to make the robust estimation algorithm energy efficient, the block adaptive diffusion adaptive algorithm is addressed. The proposed algorithms are validated by simulation and the result shows that the fault tolerant distributed estimation method is robust to node failure.

Stability of Consensus Extended Kalman Filtering for Distributed State Estimation

IFAC Proceedings Volumes, 2014

The paper addresses consensus-based networked estimation of the state of a nonlinear dynamical system. The focus is on a family of distributed state estimation algorithms which relies on the extended Kalman filter linearization paradigm. Consensus is exploited in order to fuse the information, both prior and novel, available in each network node. It is shown that the considered family of distributed Extended Kalman Filters enjoys local stability properties, under minimal requirements of network connectivity and system collective observability. A simulation case-study concerning target tracking with a network of nonlinear (angle and range) position sensors is worked out in order to show the effectiveness of the considered nonlinear consensus filter.

Distributed Kalman Filter with minimum-time covariance computation

52nd IEEE Conference on Decision and Control, 2013

This paper considerably improves the well-known Distributed Kalman Filter (DKF) algorithm by by introducing a novel decentralised consensus value computation scheme, using only local observations of sensors. It has been shown that the state estimates obtained in [8] and approaches those of the Central Kalman Filter (CKF) asymptotically. However, the convergence to the CKF can sometimes be too slow. This paper proposes an algorithm that enables every node in a sensor network to compute the global average consensus matrix of measurement noise covariance in minimum time without accessing global information. Compared with the algorithm in [8], our theoretical analysis and simulation results show that the new algorithm can offer improved performance in terms of time taken for the state estimates to converge to that of the CKF.

The Hypothesizing Distributed Kalman Filter

2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), 2012

This paper deals with distributed information processing in sensor networks. We propose the Hypothesizing Distributed Kalman Filter that incorporates an assumption of the global measurement model into the distributed estimation process. The procedure is based on the Distributed Kalman Filter and inherits its optimality when the assumption about the global measurement uncertainty is met. Recursive formulas for local processing as well as for fusion are derived. We show that the proposed algorithm yields the same results, no matter whether the measurements are processed locally or globally, even when the process noise is not negligible. For further processing of the estimates, a consistent bound for the error covariance matrix is derived. All derivations and explanations are illustrated by means of a new classification scheme for estimation processes.