64 research outputs found
Sensor fault-tolerant state estimation by networks of distributed observers
We propose a state estimation methodology using a network of distributed observers. We consider a scenario in which the local measurement at each node may not guarantee the system’s observability. In contrast, the ensemble of all the measurements does ensure that the observability property holds. As a result, we design a network of observers such that the estimated state vector computed by each observer converges to the system’s state vector by using the local measurement and the communicated estimates of a subset of observers in its neighborhood. The proposed estimation scheme exploits sensor redundancy to provide robustness against faults in the sensors. Under suitable conditions on the redundant sensors, we show that it is possible to mitigate the effects of a class of sensor faults on the state estimation. Simulation trials demonstrate the effectiveness of the proposed distributed estimation scheme
On Kalman Filtering over Fading Wireless Channels with Controlled Transmission Powers
We study stochastic stability of centralized Kalman filtering for linear time-varying systems equipped with wireless sensors. Transmission is over fading channels where variable channel gains are counteracted by power control to alleviate the effects of packet drops. We establish sufficient conditions for the expected value of the Kalman filter covariance matrix to be exponentially bounded in norm. The conditions obtained are then used to formulate stabilizing power control policies which minimize the total sensor power budget. In deriving the optimal power control laws, both statistical channel information and full channel information are considered. The effect of system instability on the power budget is also investigated for both these cases
Towards remote fault detection by analyzing communication priorities
The ability to detect faults is an important safety feature for event-based
multi-agent systems. In most existing algorithms, each agent tries to detect
faults by checking its own behavior. But what if one agent becomes unable to
recognize misbehavior, for example due to failure in its onboard fault
detection? To improve resilience and avoid propagation of individual errors to
the multi-agent system, agents should check each other remotely for malfunction
or misbehavior. In this paper, we build upon a recently proposed predictive
triggering architecture that involves communication priorities shared
throughout the network to manage limited bandwidth. We propose a fault
detection method that uses these priorities to detect errors in other agents.
The resulting algorithms is not only able to detect faults, but can also run on
a low-power microcontroller in real-time, as we demonstrate in hardware
experiments
Data compression using adaptive transform coding. Appendix 1: Item 1
Adaptive low-rate source coders are described in this dissertation. These coders adapt by adjusting the complexity of the coder to match the local coding difficulty of the image. This is accomplished by using a threshold driven maximum distortion criterion to select the specific coder used. The different coders are built using variable blocksized transform techniques, and the threshold criterion selects small transform blocks to code the more difficult regions and larger blocks to code the less complex regions. A theoretical framework is constructed from which the study of these coders can be explored. An algorithm for selecting the optimal bit allocation for the quantization of transform coefficients is developed. The bit allocation algorithm is more fully developed, and can be used to achieve more accurate bit assignments than the algorithms currently used in the literature. Some upper and lower bounds for the bit-allocation distortion-rate function are developed. An obtainable distortion-rate function is developed for a particular scalar quantizer mixing method that can be used to code transform coefficients at any rate
Distributed State Estimation for Linear Systems
This paper studies a distributed state estimation problem for both
continuous- and discrete-time linear systems. A simply structured distributed
estimator is first described for estimating the state of a continuous-time,
jointly observable, input free, multi-channel linear system whose sensed
outputs are distributed across a fixed multi-agent network. The estimator is
then extended to non-stationary networks whose graphs switch according to a
switching signal with a fixed dwell time or a variable but with fixed average
dwell time, or switch arbitrarily under appropriate assumptions. The estimator
is guaranteed to solve the problem, provided a network-widely shared gain is
sufficiently large. As an alternative to sharing a common gain across the
network, a fully distributed version of the estimator is thus studied in which
each agent adaptively adjusts a local gain though the practicality of this
approach is subject to a robustness issue common to adaptive control. A
discrete-time version of the distributed state estimation problem is also
studied, and a corresponding estimator is proposed for time-varying networks.
For each scenario, it is explained how to construct the estimator so that its
state estimation errors all converge to zero exponentially fast at a fixed but
arbitrarily chosen rate, provided the network's graph is strongly connected for
all time. This is accomplished by appealing to the ``split-spectrum'' approach
and exploiting several well-known properties of invariant subspace. The
proposed estimators are inherently resilient to abrupt changes in the number of
agents and communication links in the inter-agent communication graph upon
which the algorithms depend, provided the network is redundantly strongly
connected and redundantly jointly observable.Comment: 17 pages, 8 figures. arXiv admin note: substantial text overlap with
arXiv:1903.0548
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