Distributed $H_{\infty }$-Consensus Estimation for Random Parameter Systems Over Binary Sensor Networks: A Local Performance Analysis Method

We present a method to extract topology information from detection events of mobile entities moving through a network of binary sensors. We extract the topological structure of possible paths in the network by analyzing the time correlation of events at different sensors. The histograms of time delays between any two sensors contain the necessary information to reconstruct the network topology. This data is heavily corrupted by noise due to multiple agents in the network. We therefore use a mixture model of multiple Gaussian and a uniform distribution to explicitly isolate the noise. Our algorithm yields a graph representing the topology of our sensor network along with average travel time between nodes.

Target localization and tracking are two of the critical tasks of sensor networks in many applications. Conventional localization and tracking techniques developed for wireless systems that rely on direction-of-arrival or time-of-arrival information, are not suitable for low-power sensors with limited computation and communication capabilities. In this paper, we propose a low complexity and energy efficient localization and tracking method for binary sensor networks in noisy environments, where the sensors can only perform binary detection, and the physical links are characterized by additive white Gaussian noise channels. The proposed method is based on known spatial topology. An efficient wake-up strategy is used to activate a particular group of sensors for cooperative localization and tracking. We analyze the localization error probability and tracking miss probability in the presence of prediction errors. Simulation results validate the theoretic analysis and demonstrate the effectiveness of the localization and tracking mechanism.

This paper is concerned with the distributed filtering problem for a class of discrete time-varying stochastic parameter systems with error variance constraints over a sensor network where the sensor outputs are subject to successive missing measurements. The phenomenon of the successive missing measurements for each sensor is modeled via a sequence of mutually independent random variables obeying the Bernoulli binary distribution law. To reduce the frequency of unnecessary data transmission and alleviate the communication burden, an event-triggered mechanism is introduced for the sensor node such that only some vitally important data is transmitted to its neighboring sensors when specific events occur. The objective of the problem addressed is to design a time-varying filter such that both the requirements and the variance constraints are guaranteed over a given finite-horizon against the random parameter matrices, successive missing measurements, and stochastic noises. By recurring to stochastic analysis techniques, sufficient conditions are established to ensure the existence of the time-varying filters whose gain matrices are then explicitly characterized in term of the solutions to a series of recursive matrix inequalities. A numerical simulation example is provided to illustrate the effectiveness of the developed event-triggered distributed filter design strategy.

Distributed estimation is a major feature in wireless sensor networks (WSNs). Recently, hard quantized observations based on sign of innovation (SOI) were used to perform optimal distributed filtering involving thus the SOI Kalman filter (KF)/extended KF (EKF) [1]. In this paper, a SOI-particle filter (SOIPF) is derived to enhance the performance of the distributed estimation procedure. On one hand, the use of the particle filter avoids the imperative linearization in the EKF and on the other hand it guarantees a part of optimality for nonlinear/non Gaussian state models. The SOIPF proposed in this paper is applied in the target tracking context. The experimental results obtained for different simulations demonstrate the good tracking ability of the SOIPF compared to the SOIEKF as well as the consistency of the so given trajectory estimate.

Nonlinear analyses of structures under dynamic excitation are becoming increasingly important in structural design and performance evaluation, but large computational effort is the main factor that limits their application. Because nonlinearity is commonly confined within small regions, many studies have been devoted to improving the efficiency of solving such local nonlinear problems by maintaining the structural stiffness elasticity and simulating the effects of local nonlinearity through fictitious nonlinear forces or local modification of the elastic structural response. For dynamic analysis, the nature of the stiffness matrix determines the formulation of the widely used Rayleigh damping; as a result, these local nonlinear analysis methods often use elastic stiffness-based Rayleigh damping models. However, such a damping model can generate unexpected artificial damping forces for inelastic systems and consequently produce inaccurate or even invalid results. Although a tangent stiffness–based Rayleigh damping model has been proven to be a reasonable basis for performing highly accurate dynamic analyses, this type of damping model has difficulty achieving direct compatibility with local nonlinear analysis methods. The present research focuses on the implementation of a tangent stiffness–based Rayleigh damping model for use in efficient local nonlinear analysis methods developed based on the Woodbury formula, which can calculate the structural inelastic behavior by updating the elastic solution rather than by updating the stiffness. By representing tangent stiffness–based Rayleigh damping as a low-rank perturbation to the elastic stiffness-based damping matrix, this study derives a modified dynamic Woodbury formula in which an additional influence coefficient is introduced to reflect the effects of local nonlinearity on the structural damping properties. Moreover, to overcome the potential solution difficulty caused by abrupt changes in the damping forces in certain steps and further improve the efficiency of the proposed method, a variable time step solution scheme that can meet the computational requirements of the Woodbury formula is presented. Verifications demonstrate the validity and high efficiency of the proposed method.

We propose a method for trackingmultiple pedestrians using a binary sensor network. In ourproposed method, sensor nodes are composed of pairs ofbinary sensors and placed at specific points, referred to asgates, where pedestrians temporarily change their movementcharacteristics, such as doors, stairs, and elevators,to detect pedestrian arrival and departure events. Trackingpedestrians in each subregion divided by gates, referredto as microcells, is conducted by matching the pedestriangate arrival and gate departure events using a Bayesianestimation-based method. To improve accuracy of pedestriantracking, estimated pedestrian velocity and its reliability in amicrocell are used for trajectory estimation in the succeedingmicrocell. Through simulation experiments, we show that theaccuracy of pedestrian tracking using our proposed methodis improved by up to 35% compared to the conventionalmethod.

In this paper we examine the role of very simple and noisy sensors for the tracking problem. We propose a binary sensor model, where each sensor's value is converted reliably to one bit of information only: whether the object is moving toward the sensor or away from the sensor. We show that a network of binary sensors has geometric properties that can be used to develop a solution for tracking with binary sensors and present resulting algorithms and simulation experiments. We develop a particle filtering style algorithm for target tracking using such minimalist sensors. We present an analysis of fundamental tracking limitation under this sensor model, and show how this limitation can be overcome through the use of a single bit of proximity information at each sensor node. Our extensive simulations show low error that decreases with sensor density.

Automatic multi-target tracking in a binary sensor network needs to solve the simultaneous multiple sources localization problem. The virtual measurement (VM) approach provides a way to solve this problem via the integrated sensing processing (ISP). In the VM approach, a set of activated sensor detections are mapped into a set of virtual measurements as if they were observed by a large sensor. The set of VMs are then used for multi-target tracking. A drawback of this method is that some VMs may have larger variances when sensor nodes are sparsely distributed, which can yield considerably large estimation error. In this paper, we present a method to reduce the uncertainty of VMs using relative signal amplitude information at the cost of communicating more bits from activated sensors to base station. Instead of assigning a VM, we estimate the target source position using relative signal amplitude information which is also known as received signal strength (RSS) and is assumed to be available to the base station. This position is then treated as an alternative VM used by a LMIPDA tracker. Simulation results shown that an improved tracking accuracy can be obtained compared to the case where the standard VMs are used

Optimization of sensitivity analysis in best estimate plus uncertainty and the application to large break LOCA of a three-loop pressurized water reactor

This paper concerns with a distributed state estimation problem, where all sensor nodes are required to achieve a consensus estimation. The weighted least squares (WLS) estimator is a promising way to tackle this problem since it does not need the prior distribution knowledge with respect to the estimated quantity and noise terms. To this end, the equivalent relation between the information filter and the WLS estimator is explored first. Following this, an optimization problem coupled with a consensus constraint is established. Finally, the consensus-based distributed WLS problem is handled by the alternating direction method of multiplier. The convergence and consensus estimations between nodes are tested by numerical simulations and theoretical analyses.

In this paper, we propose a method for estimating the number of pedestrians walking in opposite directions, as in the case of a corridor. The proposed method utilizes a ceiling-mounted compound-eye sensor composed of two binary sensors with partially overlapping sensing regions. The output of the compound-eye sensor is sent to a monitoring server, where a number of Monte Carlo simulations are conducted. The simulation scenario with the smallest difference from the output history of the compound-eye sensor is selected as an estimate of the number of pedestrians. Simulations show that in crowded situations the relative error in the proposed method decreases by up to 45% compared to an existing method.

This paper presents an analysis framework for correct system operation (i.e. system success) of Cyber-Physical Systems (CPS) that deploy binary sensors with possible faults. We discuss potential faults in the interface part of such systems and address solutions for those faults in order to build dependable and reliable CPS applications. As a practical tool, we present a set of models in SIMULINK to help system designers extend simulations of general CPS applications that deploy binary sensor networks. We provide methodologies to add well-defined fault behaviors and offer assessment tools to measure the effects of possible faults on the overall system success. We demonstrate the feasibility of our contributions using a CPS application and explore various architectures for fault mitigation in a holistic design space exploration environment. With the ability to help system designers analyze and assess a non-trivial design space, the presented approach contributes to the design of fault-tolerant CPSs.

In this technical note, the near-optimal control problem is investigated for a class of time-varying linear discrete systems subject to additive nonlinearities and random gains. First, certain upper bound on the cost function is proposed to quantify the control performance. Subsequently, in virtue of the time-varying discrete Riccati-like recursions (DRLRs), the near-optimal control strategy is designed such that this upper bound is minimized over the finite horizon. To reflect the steady-state behavior of the dynamic system, the upper bound of the derived time-varying DRLRs is also analyzed over the infinite horizon by resorting to the semidefinite programming method. Finally, a numerical example is provided to verify the validity and advantages of the proposed methodology.

Within the context of Liénard equations, we present the FitzHugh--Nagumo model with an idealized nonlinearity. We give an analytical expression (i) for the transient regime corresponding to the emission of a finite number of action potentials (or spikes), and (ii) for the asymptotic regime corresponding to the existence of a limit cycle. We carry out a global analysis to study periodic solutions, the existence of which is linked to the solutions of a system of transcendental equations. The periodic solutions are obtained with the help of the harmonic balance method or as limit behavior of the transient regime. We show how the appearance of periodic solutions corresponds either to a fold limit cycle bifurcation or to a Hopf bifurcation at infinity. The resultsobtained are in agreement with local analysis methods, i.e., the Melnikov method and the averaging method. The generalization of the model leads us to formulate two conjectures concerning the number of limit cycles for the piecewise linear Liénard equations.

We present new methods of analyzing the dynamics of a competitive neural system with different time scales: the K-monotone system theory developed by Kamke (1932) as a global analysis techniques, and the theory of singular perturbation as a local analysis method.