30 research outputs found
Consistent distributed state estimation with global observability over sensor network
This paper studies the distributed state estimation problem for a class of
discrete time-varying systems over sensor networks. Firstly, it is shown that a
networked Kalman filter with optimal gain parameter is actually a centralized
filter, since it requires each sensor to have global information which is
usually forbidden in large networks. Then, a sub-optimal distributed Kalman
filter (DKF) is proposed by employing the covariance intersection (CI) fusion
strategy. It is proven that the proposed DKF is of consistency, that is, the
upper bound of error covariance matrix can be provided by the filter in real
time. The consistency also enables the design of adaptive CI weights for better
filter precision. Furthermore, the boundedness of covariance matrix and the
convergence of the proposed filter are proven based on the strong connectivity
of directed network topology and the global observability which permits the
sub-system with local sensor's measurements to be unobservable. Meanwhile, to
keep the covariance of the estimation error bounded, the proposed DKF does not
require the system matrix to be nonsingular at each moment, which seems to be a
necessary condition in the main DKF designs under global observability.
Finally, simulation results of two examples show the effectiveness of the
algorithm in the considered scenarios.Comment: 11 pages, 9 figure
Distributed Filtering for Uncertain Systems Under Switching Sensor Networks and Quantized Communications
This paper considers the distributed filtering problem for a class of
stochastic uncertain systems under quantized data flowing over switching sensor
networks. Employing the biased noisy observations of the local sensor and
interval-quantized messages from neighboring sensors successively, an extended
state based distributed Kalman filter (DKF) is proposed for simultaneously
estimating both system state and uncertain dynamics. To alleviate the effect of
observation biases, an event-triggered update based DKF is presented with a
tighter mean square error bound than that of the time-driven one by designing a
proper threshold. Both the two DKFs are shown to provide the upper bounds of
mean square errors online for each sensor. Under mild conditions on systems and
networks, the mean square error boundedness and asymptotic unbiasedness for the
proposed two DKFs are proved. Finally, the numerical simulations demonstrate
the effectiveness of the developed filters
Distributed Parameter Estimation Under Event-triggered Communications
In this paper, we study a distributed parameter estimation problem with an
asynchronous communication protocol over multi-agent systems. Different from
traditional time-driven communication schemes, in this work, data can be
transmitted between agents intermittently rather than in a steady stream.
First, we propose a recursive distributed estimator based on an event-triggered
communication scheme, through which each agent can decide whether the current
estimate is sent out to its neighbors or not. With this scheme, considerable
communications between agents can be effectively reduced. Then, under mild
conditions including a collective observability, we provide a design principle
of triggering thresholds to guarantee the asymptotic unbiasedness and strong
consistency. Furthermore, under certain conditions, we prove that, with
probability one, for every agent the time interval between two successive
triggered instants goes to infinity as time goes to infinity. Finally, we
provide a numerical simulation to validate the theoretical results of this
paper
Distributed Kalman Filters with State Equality Constraints: Time-based and Event-triggered Communications
In this paper, we investigate a distributed estimation problem for
multi-agent systems with state equality constraints (SEC). First, under a
time-based consensus communication protocol, applying a modified projection
operator and the covariance intersection fusion method, we propose a
distributed Kalman filter with guaranteed consistency and satisfied SEC.
Furthermore, we establish the relationship between consensus step, SEC and
estimation error covariance in dynamic and steady processes, respectively.
Employing a space decomposition method, we show the error covariance in the
constraint set can be arbitrarily small by setting a sufficiently large
consensus step. Besides, we propose an extended collective observability (ECO)
condition based on SEC, which is milder than existing observability conditions.
Under the ECO condition, through utilizing a technique of matrix approximation,
we prove the boundedness of error covariance and the exponentially asymptotic
unbiasedness of state estimate, respectively. Moreover, under the ECO condition
for linear time-invariant systems with SEC, we provide a novel event-triggered
communication protocol by employing the consistency, and give an offline design
principle of triggering thresholds with guaranteed boundedness of error
covariance. More importantly, we quantify and analyze the communication rate
for the proposed event-triggered distributed Kalman filter, and provide
optimization based methods to obtain the minimal (maximal) successive
non-triggering (triggering) times. Two simulations are provided to demonstrate
the developed theoretical results and the effectiveness of the filters.Comment: 16 pages, 11 figure
Distributed control under compromised measurements:Resilient estimation, attack detection, and vehicle platooning
We study how to design a secure observer-based distributed controller such
that a group of vehicles can achieve accurate state estimates and formation
control even if the measurements of a subset of vehicle sensors are compromised
by a malicious attacker. We propose an architecture consisting of a resilient
observer, an attack detector, and an observer-based distributed controller. The
distributed detector is able to update three sets of vehicle sensors: the ones
surely under attack, surely attack-free, and suspected to be under attack. The
adaptive observer saturates the measurement innovation through a preset static
or time-varying threshold, such that the potentially compromised measurements
have limited influence on the estimation. Essential properties of the proposed
architecture include: 1) The detector is fault-free, and the attacked and
attack-free vehicle sensors can be identified in finite time; 2) The observer
guarantees both real-time error bounds and asymptotic error bounds, with
tighter bounds when more attacked or attack-free vehicle sensors are identified
by the detector; 3) The distributed controller ensures closed-loop stability.
The effectiveness of the proposed methods is evaluated through simulations by
an application to vehicle platooning
Navigating A Mobile Robot Using Switching Distributed Sensor Networks
This paper proposes a method to navigate a mobile robot by estimating its
state over a number of distributed sensor networks (DSNs) such that it can
successively accomplish a sequence of tasks, i.e., its state enters each
targeted set and stays inside no less than the desired time, under a
resource-aware, time-efficient, and computation- and communication-constrained
setting.We propose a new robot state estimation and navigation architecture,
which integrates an event-triggered task-switching feedback controller for the
robot and a two-time-scale distributed state estimator for each sensor. The
architecture has three major advantages over existing approaches: First, in
each task only one DSN is active for sensing and estimating the robot state,
and for different tasks the robot can switch the active DSN by taking resource
saving and system performance into account; Second, the robot only needs to
communicate with one active sensor at each time to obtain its state information
from the active DSN; Third, no online optimization is required. With the
controller, the robot is able to accomplish a task by following a reference
trajectory and switch to the next task when an event-triggered condition is
fulfilled. With the estimator, each active sensor is able to estimate the robot
state. Under proper conditions, we prove that the state estimation error and
the trajectory tracking deviation are upper bounded by two time-varying
sequences respectively, which play an essential role in the event-triggered
condition. Furthermore, we find a sufficient condition for accomplishing a task
and provide an upper bound of running time for the task. Numerical simulations
of an indoor robot's localization and navigation are provided to validate the
proposed architecture
Distributed Kalman Filter for A Class of Nonlinear Uncertain Systems: An Extended State Method
This paper studies the distributed state estimation problem for a class of
discrete-time stochastic systems with nonlinear uncertain dynamics over
time-varying topologies of sensor networks. An extended state vector consisting
of the original state and the nonlinear dynamics is constructed. By analyzing
the extended system, we provide a design method for the filtering gain and
fusion matrices, leading to the extended state distributed Kalman filter. It is
shown that the proposed filter can provide the upper bound of estimation
covariance in real time, which means the estimation accuracy can be evaluated
online.It is proven that the estimation covariance of the filter is bounded
under rather mild assumptions, i.e., collective observability of the system and
jointly strong connectedness of network topologies. Numerical simulation shows
the effectiveness of the proposed filter
Community Detection for Gossip Dynamics with Stubborn Agents
We consider a community detection problem for gossip dynamics with stubborn
agents in this paper. It is assumed that the communication probability matrix
for agent pairs has a block structure. More specifically, we assume that the
network can be divided into two communities, and the communication probability
of two agents depends on whether they are in the same community. Stability of
the model is investigated, and expectation of stationary distribution is
characterized, indicating under the block assumption, the stationary behaviors
of agents in the same community are similar. It is also shown that agents in
different communities display distinct behaviors if and only if state averages
of stubborn agents in different communities are not identical. A community
detection algorithm is then proposed to recover community structure and to
estimate communication probability parameters. It is verified that the
community detection part converges in finite time, and the parameter estimation
part converges almost surely. Simulations are given to illustrate algorithm
performance
Secure distributed filtering for unstable dynamics under compromised observations
In this paper, we consider a secure distributed filtering problem for linear
time-invariant systems with bounded noises and unstable dynamics under
compromised observations. A malicious attacker is able to compromise a subset
of the agents and manipulate the observations arbitrarily. We first propose a
recursive distributed filter consisting of two parts at each time. The first
part employs a saturation-like scheme, which gives a small gain if the
innovation is too large. The second part is a consensus operation of state
estimates among neighboring agents. A sufficient condition is then established
for the boundedness of estimation error, which is with respect to network
topology, system structure, and the maximal compromised agent subset. We
further provide an equivalent statement, which connects to 2s-sparse
observability in the centralized framework in certain scenarios, such that the
sufficient condition is feasible. Numerical simulations are finally provided to
illustrate the developed results
Recursive Network Estimation From Binary-Valued Observation Data
This paper studies the problem of recursively estimating the weighted
adjacency matrix of a network out of a temporal sequence of binary-valued
observations. The observation sequence is generated from nonlinear networked
dynamics in which agents exchange and display binary outputs. Sufficient
conditions are given to ensure stability of the observation sequence and
identifiability of the system parameters. It is shown that stability and
identifiability can be guaranteed under the assumption of independent standard
Gaussian disturbances. Via a maximum likelihood approach, the estimation
problem is transformed into an optimization problem, and it is verified that
its solution is the true parameter vector under the independent standard
Gaussian assumption. A recursive algorithm for the estimation problem is then
proposed based on stochastic approximation techniques. Its strong consistency
is established and convergence rate analyzed. Finally, numerical simulations
are conducted to illustrate the results and to show that the proposed algorithm
is insensitive to small unmodeled factors