Discrete Dynamics in Nature and Society

Volume 2018, Article ID 7954263, 11 pages

https://doi.org/10.1155/2018/7954263

## Distributed Robust Kalman Filtering with Unknown and Noisy Parameters in Sensor Networks

^{1}School of Architecture Engineering, Nanjing Institute of Technology, Nanjing 211167, China^{2}School of Automation, Southeast University, Nanjing 210096, China^{3}Institute of Automation, Jiangnan University, Wuxi 214122, China

Correspondence should be addressed to Ya Zhang; nc.ude.ues@gnahzay

Received 8 August 2018; Revised 9 October 2018; Accepted 18 October 2018; Published 2 December 2018

Academic Editor: Dorota Mozyrska

Copyright © 2018 Donghua Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

This paper investigates the distributed filtering for discrete-time-invariant systems in sensor networks where each sensor’s measuring system may not be observable, and each sensor can just obtain partial system parameters with unknown coefficients which are modeled by Gaussian white noises. A fully distributed robust Kalman filtering algorithm consisting of two parts is proposed. One is a consensus Kalman filter to estimate the system parameters. It is proved that the mean square estimation errors for the system parameters converge to zero if and only if, for any one system parameter, its accessible node subset is globally reachable. The other is a consensus robust Kalman filter to estimate the system state based on the system matrix estimations and covariances. It is proved that the mean square estimation error of each sensor is upper-bounded by the trace of its covariance. An explicit sufficient stability condition of the algorithm is further provided. A numerical simulation is given to illustrate the results.

#### 1. Introduction

Distributed estimation is a fundamental problem in sensor networks and has attracted broad attention of the researchers. Earlier works on distributed estimation often assume that the system model is precisely known to all sensors. However, in most practical applications, an exact model of the system may not be available to sensors and the robust performance of the distributed filter should be investigated.

Consensus is a simple and feasible strategy for distributed estimation over networks. There have been many consensus estimation algorithms. Olfati-Saber ([1]) proposed consensus Kalman filtering algorithms based on the consensus protocol and standard Kalman filter. Stanković et al. ([2]) gave an algorithm which was composed of decentralized overlapping estimators and a consensus scheme for sensor networks where each sensor had prior knowledge of submatrices of the system matrices. Li et al. ([3]) developed optimal and suboptimal filters and gave sufficient conditions for the stochastic stability of the suboptimal filter. Cattivelli et al. ([4]) studied the networks in which each sensor had observable aggregated measurement matrix and proposed diffusion Kalman filtering algorithms. Yang et al. ([5]) studied sensor networks where each sensor could measure the target and was activated with a certain probability. Ugrinovskii ([6, 7]) proposed a suboptimal consensus-based estimation algorithm for continuous-time system with deterministic disturbance. The above-mentioned algorithms are called one time-scale method; i.e., the consensus iteration time interval is equal to the estimation/filtering one. References [8–13] designed consensus Kalman filtering algorithms with two time-scale strategy; i.e., during one estimation/filtering interval, multiple consensus iterations were processed to fuse the prior and novel information. The two time-scale algorithms are fully distributed and their stability just requires the collective detectability of the network. However, in this algorithm vast communication costs are required. In most existing works on the one time-scale algorithm, to design the observer gains or provide stability conditions, global knowledge of the network topology is needed. References [2–7, 14–18] gave stability conditions based on LMIs or the spectral radius of the whole system matrix, which requires global topology information and large computations.

Earlier works on distributed estimation in sensor networks often suppose that an exact model of the target system is known. In most practical problems, it is difficult for all sensors to exactly know the model of the system, and each sensor may just have access to partial model information. The robust performance of the filter with respect to the prior unknown or noisy system parameters is an important issue. Although there have been many works solving the robust state estimation problem ([19–21]), there have been few works designing the consensus-based robust estimation algorithm and investigating the robust performance for sensor networks. Recently, Zhang et al. ([22]) proposed a distributed Kalman/ filter for sensor networks with partial unknown system parameters. Han et al. ([23]) proposed a distributed consensus filtering problem for a class of discrete-time-varying systems with stochastic nonlinearities and gave sufficient stability conditions based on the recursive linear matrix inequalities. However, the linear matrix inequalities of sensors are correlated and their verification requires global knowledge of topology and large computations.

In this paper, we mainly study the distributed filtering for discrete-time-invariant system whose system matrix parameters are previously unknown or not precisely known to the sensors in the network. Each sensor may not be able to obtain the measurement. Even if one sensor can obtain the measurement, its measurement may not be observable. We design a distributed robust Kalman filtering algorithm to estimate the system parameters and system state. To estimate the system parameters, due to noises in the available parameter information, we apply a consensus Kalman filtering algorithm. We prove that the estimation errors of the system parameters converge to zero in mean square sense, if and only if for any one of the system parameters its corresponding node subset in which each node can obtain this parameter information is globally reachable. To estimate the system state, due to parameter estimation errors, we apply a consensus robust Kalman filtering algorithm where the consensus weights are designed based on the covariances of the sensors. We provide a sufficient condition guaranteeing the boundedness of the mean square estimation errors of the sensors. The contributions of this paper include the following: (1) to propose a fully distributed robust Kalman filtering algorithm to deal with the noises in available system parameters; (2) to provide a sufficient stability condition based on some uncorrelated LMIs, whose computation is small and does not require global topology information.

*Notation. *In this paper, denotes a unit matrix of size . , , and represent the trace, the trace norm, and the 2-norm of a matrix, respectively. denotes the 2-norm of vector . denotes a block-diagonal matrix with diagonal blocks . Matrix is positively definite and is written as . denotes the expectation of . For a finite node set , denotes the number of nodes in this set.

#### 2. Problem Formulation

Consider a target system with discrete-time linear dynamicswhere denotes the state of the system and is a system Gaussian noise with zero mean and covariance matrix . It is supposed that is nonsingular.

Suppose target (1) is monitored by a network of sensors with the following measuring system:where, for sensor , is the measurement vector at sensor , is the measuring matrix, and is the Gaussian measurement white noise with zero mean and covariance matrix . The measurement noises of different sensors and system noise are uncorrelated. Here may not be observable.

In the network, some sensors may not be able to obtain the measurements (2) due to limited sensing range and energy saving. Let denote whether the measurement is available. If sensor can obtain its measurement (2), then ; otherwise . Let () represent the sensing topology.

In applications, it is a really strong assumption that all sensors in the network precisely know the system parameters of the target. In many cases, if one sensor can just obtain the measurement outputs with respect to partial system state, it is likely to obtain the corresponding partial parameters in system matrix . This paper discusses the case in which the parameters may not be previously known to some sensors and sensors’ available system information contains noises. For (), we apply to denote whether the rank, column element of matrix is known to sensor . If the information is unknown to , ; otherwise, and sensor ’s corresponding available system information iswhere and is a zero mean Gaussian white noise with variance , which is independent of , , and for any one different , , or ().

In this network, even if one sensor can obtain the measurement of the target, it does not completely know the target’s system matrix and its measuring system may be also not observable, which makes it infeasible to estimate the state of the target without cooperation. The objective is to construct an estimation algorithm on the basis of the local system parameter information, local measurements (when available), and data received from all adjacent neighbors to estimate the state of the target.

#### 3. Distributed Filtering Algorithm

To explain the principle of graph theory in distributed estimation, preliminaries about graph theory are firstly introduced.

In the cooperative estimation in the sensor network, each sensor is treated as a node and the nodes communicate according to the communication graph. For a communication digraph , denotes the node set, denotes the edge set, and denotes the adjacency matrix. If node can receive information from node , then there is a corresponding edge in ; i.e., . The adjacency elements associated with the edges of the graph are defined as , . Since each node can always get its own information, for all , . Neighbor set of sensor is denoted as .

In , a simple path of length from to is such that there exists a sequence of nodes with subsequent edges . For node and a node subset , there exists at least one path from to the node set if there exists at least one node such that there is a path from node to node . A node subset is said to be globally reachable in the communication topology if, for any node in , there exists at least one path from to the node set .

Consensus protocol is a simple and effective strategy in cooperation of sensors. In this distributed filtering algorithm, two consensus processes, aimed at estimating the system parameters and system state, respectively, are included. Let us assume that, at time , each node in the sensor network can transmit its system matrix information, including parameter estimation error covariances and parameter estimations , and its system state estimation information, including measurement , measurement matrix , measurement noise covariance (when available), state estimation error covariance , state covariance , and estimated state , to its neighbors. Then the consensus-based distributed robust Kalman filtering algorithm is summarized by Algorithm 1.