174 research outputs found
Distributed fusion filter over lossy wireless sensor networks with the presence of non-Gaussian noise
The information transmission between nodes in a wireless sensor networks
(WSNs) often causes packet loss due to denial-of-service (DoS) attack, energy
limitations, and environmental factors, and the information that is
successfully transmitted can also be contaminated by non-Gaussian noise. The
presence of these two factors poses a challenge for distributed state
estimation (DSE) over WSNs. In this paper, a generalized packet drop model is
proposed to describe the packet loss phenomenon caused by DoS attacks and other
factors. Moreover, a modified maximum correntropy Kalman filter is given, and
it is extended to distributed form (DM-MCKF). In addition, a distributed
modified maximum correntropy Kalman filter incorporating the generalized data
packet drop (DM-MCKF-DPD) algorithm is provided to implement DSE with the
presence of both non-Gaussian noise pollution and packet drop. A sufficient
condition to ensure the convergence of the fixed-point iterative process of the
DM-MCKF-DPD algorithm is presented and the computational complexity of the
DM-MCKF-DPD algorithm is analyzed. Finally, the effectiveness and feasibility
of the proposed algorithms are verified by simulations
Consensus-Based Distributed State Estimation of Biofilm in Reverse Osmosis Membranes by WSNs
The appearance of biofilm has become a serious problem in many reverse osmosis based systems such as the ones found in water treatment and desalination plants. In these systems, the use of traditional techniques such as pretreatment or dozing biocides are not effective when the biofilm reaches an irreversible attachment phase. In this work, we present a framework for the use of a WSN as an estimator of the biofilm evolution in a reverse osmosis membrane so that effective solutions can be applied before the irreversible phase is attained. This design is addressed in a complete distributed and decentralized fashion, and subject to realistic constraints where cooperation between nodes is performed under unreliable links.acceptedVersionnivĂĄ
Distributed Target Tracking with Fading Channels over Underwater Wireless Sensor Networks
This paper investigates the problem of distributed target tracking via
underwater wireless sensor networks (UWSNs) with fading channels. The
degradation of signal quality due to wireless channel fading can significantly
impact network reliability and subsequently reduce the tracking accuracy. To
address this issue, we propose a modified distributed unscented Kalman filter
(DUKF) named DUKF-Fc, which takes into account the effects of measurement
fluctuation and transmission failure induced by channel fading. The channel
estimation error is also considered when designing the estimator and a
sufficient condition is established to ensure the stochastic boundedness of the
estimation error. The proposed filtering scheme is versatile and possesses wide
applicability to numerous real-world scenarios, e.g., tracking a maneuvering
underwater target with acoustic sensors. Simulation results demonstrate the
effectiveness of the proposed filtering algorithm. In addition, considering the
constraints of network energy resources, the issue of investigating a trade-off
between tracking performance and energy consumption is discussed accordingly.Comment: 12 pages, 6 figures, 6 table
Distributed kalman filtering over sensor networks with unknown random link failures
In this letter we consider the distributed consensus-based filtering problem for linear time-invariant systems over sensor networks subject to random link failures when the failure sequence is not known at the receiving side. We assume that the information exchanged, traveling along the channel, is corrupted by a noise and hence, it is no more possible to discriminate with certainty if a link failure has occurred. Therefore, in order to process the only significant information, we endow each sensor with detectors which decide on the presence of link failures. At each sensor the proposed approach consists of three steps: 1) failure detection; 2) local data aggregation; and 3) Kalman consensus filtering. Numerical examples show the effectiveness of this method
Distributed estimation over a low-cost sensor network: a review of state-of-the-art
Proliferation of low-cost, lightweight, and power efficient sensors and advances in networked systems enable the employment of multiple sensors. Distributed estimation provides a scalable and fault-robust fusion framework with a peer-to-peer communication architecture. For this reason, there seems to be a real need for a critical review of existing and, more importantly, recent advances in the domain of distributed estimation over a low-cost sensor network. This paper presents a comprehensive review of the state-of-the-art solutions in this research area, exploring their characteristics, advantages, and challenging issues. Additionally, several open problems and future avenues of research are highlighted
Bibliographic Review on Distributed Kalman Filtering
In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud
The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area
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