State estimation and data fusion for multirate sensor networks

This article is concerned with state estimation and data fusion of a linear dynamic system observed by multirate sensors in the environment of wireless sensor networks. The sampling, estimation, and transmission rates in the wireless sensor networks are different. We derive the optimal linear estimation in the centralized, sequential, and distributed forms. A numerical example is given to show the feasibility and effectiveness of the proposed algorithms. Copyright © 2015 John Wiley & Sons, Ltd.     

Bounded synchronization of complex dynamical networks with prescribed performance via event‐based transmission strategy

This paper addresses a novel prescribed performance synchronization of complex dynamical networks by means of event‐triggered communication protocols. With the designed controller and proposed even‐triggered communication strategy, this paper have advantages of guaranteeing the transient and steady‐state performance of complex dynamic networks and avoiding the continuous communication of networks for reducing the number of communications and saving the network resources. In addition, the Zeno behavior is avoided in the networks. At last, the effectiveness of the theoretic results obtained is verified via the application in the complex dynamical network with Chua's circuit.     

Average information‐weighted consensus filter for target tracking in distributed sensor networks with naivety issues

In the consensus‐based state estimation, multiple neighboring nodes iteratively exchange their local information with each other and the goal is to get more accurate and more convergent state estimation on each node. In order to improve network scalability and fault tolerance, the distributed sensor networks are desirable because the requirements of the fusion node are eliminated. However, the state estimation becomes challenging in the case of limited sensing regions and/or distinct measurement‐noise covariances. A novel distributed average information‐weighted consensus filter (AICF) is proposed, which does not require the knowledge of the total number of sensor nodes. Based on the weighted average consensus, AICF effectively addresses the naivety issues caused by unequal measurement‐noise covariances. Theoretical analysis and experimental verification show that AICF can approach the optimal centralized state estimation.     

Distributed adaptive state estimation and tracking by using active-passive sensor networks

Heterogeneous sensor networks (HSN) find a wide range of applications in the field of military and civilian environments, where sensor nodes are utilized to estimate the position of a target with both dynamics and control input being unknown for the purposes of tracking. In the HSN, nodes are considered active depending upon their ability to sense the target output while the others are taken passive. Accurate estimation requires local information exchange among the spatially located sensor nodes, so that the active nodes as well as the passive nodes converge simultaneously to the same value. The local information exchange among the nodes is dictated by a connected graph. By using the criterion of collective observability, a novel distributed adaptive estimation scheme is introduced via adaptive observer where the nodes are allowed to have different sensor modalities. Using the estimated information, a subset of active and passive nodes, referred to as mobile nodes, can track the moving target. By using a constant state feedback controller at each mobile node, the state and parameter estimation as well as the tracking errors are shown to be uniformly ultimately bounded. Simulation results verify theoretical claims.     

Asynchronous distributed state estimation based on a continuous‐time stochastic model

In this paper, the problem of state estimation in an asynchronous distributed multi‐sensor estimation (ADE) system is considered. In such an ADE system, the state of a plant of interest is estimated by a group of local estimators. Each local estimator based, for example, on a Kalman filter, performs fusion of data from its local sensor and other (remote) processors to compute possibly best state estimates. In performing data fusion, however, two important issues need to be addressed, namely, the problem of asynchronism of local processors and the one of unknown correlation between asynchronous data in local processors. Consequently, there are two main contributions proposed in this paper. The first is a method to deal with asynchronous discrete‐time data based on a continuous‐time stochastic plant model. The second contribution is an asynchronous distributed data‐fusion algorithm. Simulated experiments illustrate the effectiveness of the proposed ADE approach. Copyright © 2011 John Wiley & Sons, Ltd.     

Distributed Kalman filters with adaptive strategy for linear time‐varying interconnected systems

We consider the problem of distributed state estimation over a sensor network in which a set of nodes collaboratively estimates the state of a continuous‐time linear time‐varying system. In particular, our work focuses on the benefits of weight adaptation of the interconnection gains in distributed Kalman filters. To this end, an adaptation strategy is proposed with the adaptive laws derived via a Lyapunov‐redesign approach. The justification for the gain adaptation stems from a desire to adapt the pairwise difference of state estimates as a function of their agreement, thereby enforcing an interconnection‐dependent gain. In the proposed scheme, an adaptive gain for each pairwise difference of the interconnection terms is used in order to address edge‐dependent differences in the state estimates. Accounting for node‐specific differences, a special case of the scheme is also presented, where it uses a single adaptive gain in each node estimate and which uniformly penalizes all pairwise differences of state estimates in the interconnection term. The filter gains can be designed either by standard Kalman filter or Luenberger observer to construct the adaptive distributed Kalman filter or adaptive distributed Luenberger observer. Stability of the schemes has been shown, and it is not restricted by the graph topology and therefore the schemes are applicable to both directed and undirected graphs. The proposed algorithms offer a significant reduction in communication costs associated with information flow by the nodes. Finally, numerical studies are presented to illustrate the performance and effectiveness of the proposed adaptive distributed Kalman filters. Copyright © 2015 John Wiley & Sons, Ltd.     

Unscented summation information‐weighted consensus filter for distributed sensor networks with incomplete information

In order to improve network scalability and fault tolerance, the distributed sensor networks are desirable. However, the distributed state estimation becomes challenging when some sensors have insufficient information due to restricted observability, and/or have imparity information due to unequal measurement‐noise covariances. Centralized summation information‐fusion (CSI) model is presented which performs weighted least‐squares estimation for all measurement information to achieve the optimal centralized state estimation. The CSI model revises the initialization and covariance propagation in the original information‐weighted consensus filter (ICF). Since centralized information fusion is a summation mode and is approached by the average consensus protocol, all the covariances involved in the CSI model contain the information regarding the total number of nodes. The artificially preset initial values are considered as measurement information and fused in accordance with the CSI model. By combining the CSI model with unscented transform, distributed unscented summation information‐weighted consensus filter (USICF) is proposed. USICF realizes the nonlinear estimation in the context of highly incomplete information. Theoretical analysis and experimental verification showed that USICF achieves better performance than UICF that is based on ICF.     

A novel distributed algorithm for media access control address assignment in wireless sensor networks

This paper presents a novel distributed media access control (MAC) address assignment algorithm, namely virtual grid spatial reusing (VGSR), for wireless sensor networks, which reduces the size of the MAC address efficiently on the basis of both the spatial reuse of MAC address and the mapping of geographical position. By adjusting the communication range of sensor nodes, VGSR algorithm can minimize the size of MAC address and meanwhile guarantee the connectivity of the sensor network. Theoretical analysis and experimental results show that VGSR algorithm is not only of low energy cost, but also scales well with the network size, with its performance superior to that of other existing algorithms. __________ Translated from Journal of Xidian University, 2006, 33(5): 716–720 [译自: 西安电子科技大学学报 (自然科学版)]     

Design considerations for low power time‐mode SAR ADC

This paper describes circuit design considerations for realization of low power dissipation successive approximation register (SAR) analog‐to‐digital converter (ADC) with a time‐mode comparator. A number of design issues related to time‐mode SAR ADC are discussed. Also, noise and offset models describing the impact of the noise and offset on the timing error of time‐domain comparator are presented. The results are verified by comparison to simulations. The design considerations mentioned in this paper are useful for the initial design and the improvements of time‐mode SAR ADC. Then, a number of practical design aspects are illustrated with discussion of an experimental 12‐bit SAR ADC that incorporates a highly dynamic voltage‐to‐time converter and a symmetrical input time‐to‐digital converter. Prototyped in a 0.18‐µm six‐metal one‐polysilicon Complementary Metal‐Oxide‐Semiconductor (CMOS) process, the ADC, at 12 bit, 500 kS/s, achieves a Nyquist signal‐to‐noise‐and‐distortion ratio of 53.24 dB (8.55 effective number of bits) and a spurious‐free dynamic range of 70.73 dB, while dissipating 27.17 μW from a 1.3‐V supply, giving a figure of merit of 145 fJ/conversion‐step. Copyright © 2013 John Wiley & Sons, Ltd.     

A 0.15‐μm CMOS baseband LSI employing sleep mode with clock‐offset compensation for M2M wireless sensor networks

This paper describes a wireless baseband large‐scale integration (LSI) that contains a sleep management circuit. The sleep manager performs the sleep‐clock offset compensation and enables a wireless terminal (WT) with a typical crystal oscillator (XO) to remain in sleep mode for a long period while maintaining synchronization with the access point. Lab experiments show that the sleep period reaches 512 s and that, with intermittent operation, the WT maintains synchronization with the access point for 10 days. The LSI's average current consumption is as low as 11 μA for a 128‐s sleep period. A wakeup detection circuit is also implemented in the LSI. This circuit performs paging control instead of a microprocessor unit (MPU) and this helps to reduce current consumption in the MPU and the flash read only memory (ROM). The single‐chip baseband LSI is fabricated using 0.15‐μm CMOS technology. It is 4.6 mm × 4.2 mm in area and consumes 4.0 μA for sleep operation. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

A noise‐constrained algorithm for estimation over distributed networks

Much research has been devoted recently to the development of algorithms to utilize the distributed structure of an ad hoc wireless sensor network for the estimation of a certain parameter of interest. A successful solution is the algorithm called the diffusion least mean squares algorithm. The algorithm estimates the parameter of interest by employing cooperation between neighboring sensor nodes within the network. The present work derives a new algorithm by using the noise constraint that is based on and improves the diffusion least mean squares algorithm. In this work, first the derivation of the noise constraint‐based algorithm is given. Second, detailed convergence and steady‐state analyses are carried out, including analyses for the case where there is mismatch in the noise variance estimate. Finally, extensive simulations are carried out to test the robustness of the proposed algorithm under different scenarios, especially the mismatch scenario. Moreover, the simulation results are found to corroborate the theoretical results very well. Copyright © 2012 John Wiley & Sons, Ltd.     

Control using joint torque sensor of robot arm with two‐inertia resonance: Comparison with control using state‐estimation observer

Most industrial robots are driven through reduction gears such as Harmonic Drives and RV gears. Due to the flexibility of the drive system, vibratory behavior occurs during operation. When flexibility is considered, the drive system of the robot joint can be modeled as a resonant mechanical system called a two‐inertia system. Conventionally, studies of two‐inertia system have discussed semiclosed‐loop control using only motor information and a state observer. On the other hand, joint torque sensing of robots has been studied in the harmonic drives that are widely used in robot joints. The joint torque sensor is becoming available with higher performance. In this paper, we consider the control of a robot arm having two‐inertia resonance by using the joint torque sensor. The performance of the torque sensor and that of the observer are compared. © 2006 Wiley Periodicals, Inc. Electr Eng Jpn, 156(2): 75–84, 2006; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20226     

Hierarchical fusion robust Kalman filter for clustering sensor network time‐varying systems with uncertain noise variances

For the clustering time‐varying sensor network systems with uncertain noise variances, according to the minimax robust estimation principle, based on the worst‐case conservative system with conservative upper bounds of noise variances, applying the optimal Kalman filtering, the two‐level hierarchical fusion time‐varying robust Kalman filter is presented, where the first‐level fusers consist of the local decentralized robust fusers for the clusters, and the second‐level fuser is a global decentralized robust fuser for the cluster heads. It can reduce the communication load and save energy resources of sensors. Its robustness is proved by the proposed Lyapunov equation method. The concept of robust accuracy is presented, and the robust accuracy relations of the local, decentralized, and centralized fused robust Kalman filters are proved. Specially, the corresponding steady‐state robust local and fused Kalman filters are also presented, and the convergence in a realization between the time‐varying and steady‐state robust Kalman filters is proved by the dynamic error system analysis method. A simulation example shows correctness and effectiveness. Copyright © 2013 John Wiley & Sons, Ltd.     

Self‐tuning fusion Kalman filter weighted by scalars and its convergence analysis for multi‐channel autoregressive moving average signals

For the multi‐sensor multi‐channel autoregressive (AR) moving average signals with white measurement noises and an AR‐colored measurement noise, a multi‐stage information fusion identification method is presented when model parameters and noise variances are partially unknown. The local estimators of model parameters and noise variances are obtained by the multidimensional recursive instrumental variable algorithm and correlation method, and the fused estimators are obtained by taking the average of the local estimators. They have the strong consistency. Substituting them into the optimal information fusion Kalman filter weighted by scalars, a self‐tuning fusion Kalman filter for multi‐channel AR moving average signals is presented. Applying the dynamic error system analysis method, it is proved that the proposed self‐tuning fusion Kalman filter converges to the optimal fusion Kalman filter in a realization, so that it has asymptotic optimality. A simulation example for a target tracking system with three sensors shows its effectiveness. Copyright © 2014 John Wiley & Sons, Ltd.     

An enhanced state estimation for linear parabolic PDE systems with mobile sensors

This paper studies an enhanced state estimation problem of distributed parameter processes modeled by a linear parabolic partial differential equation using mobile sensors. The proposed estimation scheme contains a state estimator and the guidance of mobile sensors, where the spatial domain is decomposed into multiple subdomains according to the number of sensors and each sensor is capable of moving within the respective subdomain. The state estimator is desired to make the state estimation error system exponentially stable while providing an performance bound. The mobile sensor guidance is used to enhance the transient performance of the error system. By the Lyapunov direct technique, an integrated design of state estimator and mobile sensor guidance laws is developed in the form of bilinear matrix inequalities (BMIs) to meet the desired design objectives. Moreover, to make the performance bound as small as possible, a suboptimal enhanced state estimation problem is formulated as a BMI optimization one, which can be solved via an iterative linear matrix inequality algorithm. Finally, numerical simulations are given to show the effectiveness of the proposed method.     

A method for determining pseudo‐measurement state values for topology observability of state estimation in power systems

This paper proposes a new technique for determining state values in power systems. Recently, it has been useful for carrying out state estimation with PMU (Phasor Measurement Unit) data. The authors have developed a method for determining state values with an artificial neural network (ANN) considering topology observability in power systems. The ANN has the advantage of approximating nonlinear functions with high precision. The method evaluates pseudo‐measurement state values of data which are lost in power systems. The method has been successfully applied to the IEEE 14‐bus system. © 2012 Wiley Periodicals, Inc. Electr Eng Jpn, 179(2): 27–34, 2012; Published online in Wiley Online Library ( wileyonlinelibrary.com ). DOI 10.1002/eej.21235     

Magnetic flux density bias analysis and suppression strategy for voltage‐fed single‐stage full‐bridge converter

Power transformer in the voltage‐fed single‐stage full‐bridge (SSFB) converter is easy to be saturated because of the special operating mode of this topology. First and foremost, detailed analysis about the generation mechanism of the transformer saturation in the voltage‐fed SSFB converter is presented for the first time in this paper. Second, the mathematical expression of the maximum value of magnetic flux density bias (MFDB) during every line cycle is deduced. Furthermore, a novel suppressing strategy, consisted of the digital compensating algorithm and design considerations, is proposed based on the theoretical analysis and mathematical expression. The MFDB can be eliminated and additional circuits are not needed with the proposed strategy. Finally, a 1 kW prototype with the voltage‐fed SSFB topology is built to verify the effect of the proposed approach. The voltage‐second imbalance is reduced to less than 1% using the proposed suppression strategy compared with 14% when the strategy is not used. Consequently, the system reliability and the efficiency are improved. Copyright © 2016 John Wiley & Sons, Ltd.     

Observer design for neutral‐type neural networks with discrete and distributed time‐varying delays

This paper is concerned with the problem of state estimation for a class of neural networks with discrete and distributed interval time‐varying delays. We propose a new approach of nonlinear estimator design for the class of neutral‐type neural networks. By constructing a newly augmented Lyapunov‐Krasovskii functional, we establish sufficient conditions to guarantee the estimation error dynamics to be globally exponentially stable. The obtained results are formulated in terms of linear matrix inequalities (LMIs), which can be easily verified by the MATLAB LMI control toolbox. Then, the desired estimators gain matrix is characterized in terms of the solution to these LMIs. Three numerical examples are given to show the effectiveness of the proposed design method.     

Energy‐efficient cooperative localization in mobile WSN

Design and implementation of an energy‐efficient protocol is one of the main challenges in wireless sensor networks (WSNs). In terms of localization, both energy efficiency and accuracy must be addressed to achieve the final goals of localization. In mobile sensor nodes, where battery power is the most hardware resource limitation, accurate localization needs to be extremely energy efficient. In this work, a virtual multiple‐input multiple‐output (VMIMO) technique is deployed to tackle the problems of getting more energy efficiency and higher accuracy simultaneously.In this case, the optimum selection of the number of transceiver nodes can be obtained with the lowest possible total energy consumption, localization error, and speed of nodes. In addition, VMIMO decreases the power of transmitters, and therefore will lead to the reduction of destructive effects of electromagnetic sensitivity (EMS) on the body. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

An IMM algorithm with federated information mode‐matched filters for AGV

In this paper, a tracking algorithm for autonomous navigation of automated guided vehicles (AGVs) is presented. The developed navigation algorithm is an interacting multiple‐model (IMM) algorithm used to detect other AGVs using fused information from multiple sensors. In order to detect other AGVs, two kinematic models were derived: A constant‐velocity model for linear motion, and a constant‐speed turn model for curvilinear motion. In the constant‐speed turn model, a nonlinear information filter (IF) is used in place of the extended Kalman filter (KF). Being equivalent to the KF algebraically, the IF is extended to N‐sensor distributed dynamic systems. The model‐matched filter used in multi‐sensor environments takes the form of a federated nonlinear IF. In multi‐sensor environments, the information‐based filter is easier to decentralize, initialize, and fuse than a KF‐based filter. In this paper, the structural features and information‐sharing principle of the federated IF are discussed. The performance of the suggested algorithm using a Monte Carlo simulation is evaluated under the three navigation patterns. Copyright © 2006 John Wiley & Sons, Ltd.