Consensus Filtering Algorithm Research Articles

Freshness constraints of an age of information based event-triggered Kalman consensus filter algorithm over a wireless sensor network

This paper presents the design of a new event-triggered Kalman consensus filter (ET-KCF) algorithm for use over a wireless sensor network (WSN). This algorithm is based on information freshness, which is calculated as the age of information (AoI) of the sampled data. The proposed algorithm integrates the traditional event-triggered mechanism, information freshness calculation method, and Kalman consensus filter (KCF) algorithm to estimate the concentrations of pollutants in the aircraft more efficiently. The proposed method also considers the influence of data packet loss and the aircraft’s loss of communication path over the WSN, and presents an AoI-freshness-based threshold selection method for the ET-KCF algorithm, which compares the packet AoI to the minimum average AoI of the system. This method can obviously reduce the energy consumption because the transmission of expired information is reduced. Finally, the convergence of the algorithm is proved using the Lyapunov stability theory and matrix theory. Simulation results show that this algorithm has better fault tolerance compared to the existing KCF and lower power consumption than other ET-KCFs.

Distributed H∞-Consensus Filtering for Attitude Tracking Using Ground-Based Radars.

This paper is concerned with the distributed H ∞ -consensus filtering problem on attitude tracking over a radar filter network subject to switching topology and random packet dropouts occurring in the data transmission from both the Sun sensor and the filters. Since ground-based radars cannot directly measure the satellite attitude, a Sun sensor is deployed at the satellite side and its measurements are transmitted to radar filters through different network communication channels while suffering from random packet dropouts with different probabilities. In the radar filter network, each radar filter receives data not only from the Sun sensor but also from its local neighboring radar filters in accordance with a switching network topology. A delicate distributed H ∞ -consensus filtering algorithm, which incorporates the effects of switching network topology and random packet dropouts, is adopted to estimate attitude and attitude-rate. The algorithm guarantees H ∞ -consensus attenuation performance for the estimation deviations among radar filters, and the robustness against the switching network topology and packet dropouts for the radar filter network. The illustrative examples are given to verify the effectiveness of the proposed distributed H ∞ -consensus filtering algorithm.

A Distributed Probabilistic Modeling Algorithm for the Aggregated Power Forecast Error of Multiple Newly Built Wind Farms

The extensive penetration of wind farms (WFs) presents challenges to the operation of distribution networks (DNs). Building a probability distribution of the aggregated wind power forecast error is of great value for decision making. However, as a result of recent government incentives, many WFs are being newly built with little historical data for training distribution models. Moreover, WFs with different stakeholders may refuse to submit the raw data to a data center for model training. To address these problems, a Gaussian mixture model (GMM) is applied to build the distribution of the aggregated wind power forecast error; then, the maximum a posteriori (MAP) estimation method is adopted to overcome the limited training data problem in GMM parameter estimation. Next, a distributed MAP estimation method is developed based on the average consensus filter algorithm to address the data privacy issue. The distribution control center is introduced into the distributed estimation process to acquire more precise estimation results and better adapt to the DN control architecture. The effectiveness of the proposed algorithm is empirically verified using historical data.

Average-Consensus Filter of First-Order Multi-Agent Systems With Disturbances

Average-consensus filter problem of assigned constant inputs is investigated for the first-order multi-agent systems with heterogeneous disturbances, and a proportional-integral consensus filter algorithm accompanied with disturbances’ observers is designed. By using matrix theory and frequency-domain analysis, sufficient and necessary convergence conditions are obtained for our proposed algorithm without and with identical communication delay, respectively. Numerical simulations illustrate the correctness of theoretical results.

Cost-sensitive elimination of mislabeled training data

Accurately labeling training data plays a critical role in various supervised learning tasks. Since labeling in practical applications might be erroneous due to various reasons, a wide range of algorithms have been developed to eliminate mislabeled data. These algorithms may make the following two types of errors: identifying a noise-free data as mislabeled, or identifying a mislabeled data as noise free. The effects of these errors may generate different costs, depending on the training datasets and applications. However, the cost variations are usually ignored thus existing works are not optimal regarding costs. In this work, the novel problem of cost-sensitive mislabeled data filtering is studied. By wrapping a cost-minimizing procedure, we propose the prototype cost-sensitive ensemble learning based mislabeled data filtering algorithm, named CSENF. Based on CSENF, we further propose two novel algorithms: the cost-sensitive repeated majority filtering algorithm CSRMF and cost-sensitive repeated consensus filtering algorithm CSRCF. Compared to CSENF, these two algorithms could estimate the mislabeling probability of each training data more confidently. Therefore, they produce less cost compared to CSENF and cost-blind mislabeling filters. Empirical and theoretical evaluations on a set of benchmark datasets illustrate the superior performance of the proposed methods.

Distributed information-weighted Kalman consensus filter for sensor networks

Consensus-based algorithms for distributed Kalman filtering of the state of a dynamical target agent have attracted considerable research and attention during the past decade. In these filters, it is required for all agents to reach consensus about their estimates of the state of a target node. Distributed filtering techniques for sensor networks require less computation per sensor node and result in more robust estimation since they only use information from an agent’s neighbors in a network. However, poor local sensor node estimates caused by limited observability, network topologies that restrict allowable communications, and communication noises between sensors are challenging issues not yet fully resolved in the framework of distributed Kalman consensus filters. This paper confronts these issues by introducing a novel distributed information-weighted Kalman consensus filter (IKCF) algorithm for sensor networks in a continuous-time setting. It is formally proven using Lyapunov techniques that, using the new distributed IKCF, the estimates of all sensors reach converge to consensus values that give locally optimal estimates of the state of the target. A new measurement model is selected that only depends on local information available at each node based on the prescribed communication topology, wherein all the estimates of neighbor sensors are weighted by their inverse-covariance matrices. Locally optimal solutions are then derived for the proposed distributed IKCF considering channel noises in the consensus terms. Moreover, if the target has a nonzero control input, a method is giving of incorporating estimates of the target’s unknown input. Simulation case studies show that the proposed distributed IKCF outperforms other methods in the literature.

Consensus Filtering Algorithm with Incomplete Measurements

In order to improve the estimate performance of distributed sensor networks, we introduce an improved consensus-based distributed filtering algorithm. Firstly, sensors obtain the local estimates using their own measurements. Then through utilizing the data of neighbor nodes to update the local estimates, estimates in the networks can reach dynamic average consensus. Based on studying the value of consensus step size, we give a sufficient condition for the convergence of the algorithm and discusses the influences of the consensus step size and the detection probability on the accuracy and consensus of estimation. The numerical simulation demonstrates that the algorithm proposed in this paper can improve the accuracy and consensus of estimation, and it is more robust with incomplete measurements.

A novel square-root cubature information weighted consensus filter algorithm for multi-target tracking in distributed camera networks.

This paper deals with the problem of multi-target tracking in a distributed camera network using the square-root cubature information filter (SCIF). SCIF is an efficient and robust nonlinear filter for multi-sensor data fusion. In camera networks, multiple cameras are arranged in a dispersed manner to cover a large area, and the target may appear in the blind area due to the limited field of view (FOV). Besides, each camera might receive noisy measurements. To overcome these problems, this paper proposes a novel multi-target square-root cubature information weighted consensus filter (MTSCF), which reduces the effect of clutter or spurious measurements using joint probabilistic data association (JPDA) and proper weights on the information matrix and information vector. The simulation results show that the proposed algorithm can efficiently track multiple targets in camera networks and is obviously better in terms of accuracy and stability than conventional multi-target tracking algorithms.

Cluster-Gossip Based Distributed Kalman Consensus Filter Algorithm with Energy Efficiency

According to these constrains that wireless sensor networks are composed of many wireless nodes with limited power, a new energy efficient cluster-based distributed consensus kalman filtering algorithm is proposed in this paper. In this algorithm, each cluster contains a cluster-head and some member nodes where the cluster-head is used to fuse data which come from member nodes and consensus process between neighbor cluster-head. This clustering method divide wireless sensor networks into two classes of networks: cluster units network and cluster-heads network. In this way, numbers of information transmission among nodes are reduced efficiently and communication distances among nodes are also shortened. As a result, node’s energy in wireless sensor network can be saved greatly. Moreover, Gossip algorithm is introduced to deal with the consensus problem between cluster-heads for improving power consumption and the convergence analysis for the algorithm which is given by applying to graph theory and matrix theory. Finally, a simulation example is given to show the effectively of our method.

Second-Order Consensus Filtering Algorithm

In order to improve the accuracy of consensus filters, in this paper, we propose a second-order consensus filtering algorithm based on the pseudo two-hop distributed consensus algorithm, we analyze the performance of the second-order consensus filtering algorithm, and prove that the second-order consensus filtering algorithm is convergent. We analyze the filtering accuracy of the second-order consensus filtering algorithm, and make a comparison for filtering accuracy between the first-order consensus filtering algorithm and the second-order consensus filtering algorithm, simultaneously, simulation is proposed, the results show the second-order consensus filtering algorithm is convergent, and the filtering accuracy is higher than that of the first-order consensus algorithm.