Number Of Consensus Iterations Research Articles

Hybrid consensus‐based distributed pseudomeasurement information filter for small UAVs tracking in wireless sensor network

In this study, a novel state estimation method is proposed for small unmanned aerial vehicles (UAVs) tracking in passive sensors (PSs) wireless sensor network (WSN), where the measurement equations are highly non-linear. Different from many non-linear function approximation methods, the non-linear measurement functions are recast into a linear form with respect to UAV states. The centralised 3D pseudomeasurement information filter (PMIF) is derived by embedding the linear form measurements into conventional information filtering schemes. For distributed state estimation in a not fully connected WSN, a hybrid consensus-based distributed PMF (DHCPMIF) is developed in which each PS independently calculates local contribution by using its own pseudomeasurement. To guarantee bounded estimation error as close as possible to that obtained by the optimal centralised estimator, the hybrid consensus method is developed by combining consensus on measurement and consensus on information method, which retains the advantages of both approaches, thus providing estimation accuracy improvement when the number of consensus iterations is moderate. The effectiveness of the proposed distributed filter is verified via two simulation examples involving tracking a UAV using four and eight PSs, respectively.

Distributed Kalman Filtering With Finite-Time Max-Consensus Protocol

This paper investigates the distributed state estimation problem for an unstable dynamic plant in a sparsely strongly connected sensors network. The dynamics of the plant are collectively observable for all sensors, but not necessarily locally observable for each sensor. We propose a finite-time consensus-based distributed estimator to cope with the local unobservability. This algorithm is based on the max-consensus technique, and the number of consensus iterations is precisely provided. We prove that this estimator is stable and the mean-squared error is equal to that obtained by the centralized estimator. Furthermore, we extend this finite-time consensus Kalman filtering algorithm to networks with nonuniform time-varying communication delays. By introducing the virtual nodes, which act as the relay nodes, we prove the stability of the algorithm. Finally, the effectiveness of the proposed distributed finite-time consensus filters is evaluated by simulation experiments.

Square-Root Sigma-Point Information Consensus Filters for Distributed Nonlinear Estimation.

This paper focuses on the convergence rate and numerical characteristics of the nonlinear information consensus filter for object tracking using a distributed sensor network. To avoid the Jacobian calculation, improve the numerical characteristic and achieve more accurate estimation results for nonlinear distributed estimation, we introduce square-root extensions of derivative-free information weighted consensus filters (IWCFs), which employ square-root versions of unscented transform, Stirling’s interpolation and cubature rules to linearize nonlinear models, respectively. In addition, to improve the convergence rate, we introduce the square-root dynamic hybrid consensus filters (DHCFs), which use an estimated factor to weight the information contributions and shows a faster convergence rate when the number of consensus iterations is limited. Finally, compared to the state of the art, the simulation shows that the proposed methods can improve the estimation results in the scenario of distributed camera networks.

Trust-Aware Consensus-Inspired Distributed Cooperative Spectrum Sensing for Cognitive Radio Ad Hoc Networks

Cooperation among cognitive radios for spectrum sensing is deemed essential for environments with deep shadows. In this paper, we study cooperative spectrum sensing for cognitive radio ad hoc networks where there is no fusion center to aggregate the information from various secondary users. We propose a novel consensus-inspired cooperative sensing scheme based on linear iterations that is fully distributed and low-cost. In addition, the tradeoffs on the number of consensus iterations are explored for scenarios with different shadow fading characteristics. Furthermore, we model insistent spectrum sensing data falsification (ISSDF) attack aimed at consensus-based iterative schemes and show its destructive effect on the cooperation performance which accordingly results in reduced spectrum efficiency and increased interference with primary users. We propose a trust management scheme to mitigate these attacks and evaluate the performance improvement through extensive Monte Carlo simulations for large-scale cognitive radio ad hoc networks in TV white space. Our proposed trust management reduces the harm of a set of collusive ISSDF attackers up to two orders of magnitude in terms of missed-detection and false alarm error rates. Moreover, in a hostile environment, integration of trust management into cooperative schemes considerably relaxes the sensitivity requirements on the cognitive radio devices.

Improved Distributed Cooperative Spectrum Sensing (DCSS) For Cognitive Radio Adhoc Network

Cooperation among cognitive radios for spectrum sensing is deemed essential for environments with deep shadows. In this paper, we study cooperative spectrum sensing for cognitive radio ad hoc networks where there is no fusion center to aggregate the information from various secondary users. We propose a novel consensus-inspired cooperative sensing scheme based on linear iterations that is fully distributed and low-cost. In addition, the trade-offs on the number of consensus iterations are explored for scenarios with different shadow fading characteristics. Furthermore, we model Insistent Spectrum Sensing Data Falsification (ISSDF) attack aimed at consensus-based iterative schemes and show its destructive effect on the cooperation performance which accordingly results in reduced spectrum efficiency and increased interference with primary users. We propose a trust management scheme to mitigate these attacks and evaluate the performance improvement through extensive Monte Carlo simulations for large-scale cognitive radio ad hoc networks in TV white space. Our proposed trust management reduces the harm of a set of collusive ISSDF attackers up to two orders of magnitude in terms of missed-detection and false alarm error rates. Moreover, in a hostile environment, integration of trust management into cooperative schemes considerably relaxes the sensitivity requirements on the cognitive radio devices.

Distributed H∞ Filtering with Consensus Strategies in Sensor Networks: Considering Consensus Tracking Error

AbstractThe existing distributed filtering with consensus strategies consists of two steps: the consensus step through locally communicating with neighboring sensor nodes and the local filtering step. In this paper, the influence of consensus tracking error on the local estimation error is analyzed, and a distributed H∞ filtering algorithm considering the consensus tracking error is proposed. When the number of consensus iterations per sampling period is limited, the proposed method can suppress the effect of consensus tracking error on local estimation error; when the number of consensus iterations per sampling period goes to infinity, i.e., the consensus tracking error converges to zero, the local filtering in the distributed algorithm reduces to the centralized filtering. Simulation shows the effectiveness of the proposed method.