Yinya Li

Distributed Asynchronous Fusion Algorithm for Sensor Networks with Packet Losses

This paper is concerned with the problem of distributed estimation fusion over peer-to-peer asynchronous sensor networks with random packet dropouts. A distributed asynchronous fusion algorithm is proposed via the covariance intersection method. First, local estimator is developed in an optimal batch fashion by constructing augmented measurement equations. Then the fusion estimator is designed to fuse local estimates in the neighborhood. Both local estimator and fusion estimator are developed by taking into account the random packet losses. The presented estimation method improves local estimates and reduces the estimate disagreement. Simulation results validate the effectiveness of the proposed distributed asynchronous fusion algorithm.

An order insensitive sequential fast covariance intersection fusion algorithm

The proposed SFCI fusion algorithm can handle the problem of unknown cross-correlation in local estimation errors.The fusion accuracy of the proposed algorithm is not relevant to the fusion orders, i.e., all fusion nodes can acquire identical result.The fusion coefficients are straightforward to calculate, owing to not require optimizing nonlinear cost function.The proposed algorithm is computationally efficient and is therefore applicable for use in real-time fusion systems. In this article, data cross-correlation in distributed sensor system is investigated via an order insensitive sequential fast covariance intersection fusion algorithm. Among the existing approaches, the common drawbacks are that the fusion results are sensitive to fusion orders and the computational burden is tremendous due to the optimization of multi-dimensional nonlinear cost function. In order to overcome these drawbacks, a sequential fast covariance intersection (SFCI) algorithm is presented. The new fusion coefficients can be calculated straightforward by taking the reciprocal of the trace of the inverse variances as local fusion coefficients and using an iterative process for fusion step to revise the coefficient weight. Note that the proposed fusion algorithm is consistent, and its accuracy is unrelated to the fusion order of the distributed system. Finally, real radar data and simulation examples are provided to verify the effectiveness of the proposed algorithm.

Extended target tracking filter with intermittent observations

This study addresses the problem of tracking extended target with intermittent observations. Based on practical applications, two Bernoulli distributed random variables are employed to describe the intermittent phenomenon of the positional measurements and the measurements of target extent, respectively. First, a machine vision algorithm is developed to solve the target shape parameters. Then, four sub-filters are designed according to the received observations and the achieved target shape parameters. The output of the proposed tracking filer can be obtained by the weighted-confidence fusion of the sub-filters. Finally, the machine vision algorithm is evaluated by the virtual target images created in OpenGL (Open Graphics Library) and the real images of a moving ship. The performance of the designed tracking filter is compared with the traditional tracking filter. The experiment results show the effectiveness of the machine vision approach; also the Monte-Carlo runs demonstrate that the provided tracking filter outperforms the traditional one with respect to accuracy.

Adaptive Kalman Estimation in Target Tracking Mixed with Random One-Step Delays, Stochastic-Bias Measurements, and Missing Measurements

The objective of this paper is concerned with the estimation problem for linear discrete-time stochastic systems with mixed uncertainties involving random one-step sensor delay, stochastic-bias measurements, and missing measurements. Three Bernoulli distributed random variables are employed to describe the uncertainties. All the three uncertainties in the measurement have certain probability of occurrence in the target tracking system. And then, an adaptive Kalman estimation is proposed to deal with this problem. The adaptive filter gains can be obtained in terms of solutions to a set of recursive discrete-time Riccati equations. Examples in three scenarios of target tracking are exploited to show the effectiveness of the proposed design approach.

Cooperative Target Localization and Tracking with Incomplete Measurements

This study investigates a problem on target localization and tracking for two cases where either the slant range information of dual stations is lost or the slant range information of one station and the pitch angle information of the other one are missing. The models of cooperative localization with incomplete measurements are presented and the Kalman filtering algorithm is applied for target tracking. For improving tracking precision, a strategy of observers path planning based on the gradient of circular error probability (CEP) is integrated into the Kalman filtering algorithm. Several numerical examples are used to illustrate the tracking performance of the proposed algorithm with the corresponding root mean square error (RMSE) and Cramer-Rao lower bound (CRLB). The Monte Carlo simulation results validate the effectiveness of the presented algorithm.

A distributed fusion algorithm over asynchronous sensor networks

This paper presents a distributed asynchronous fusion algorithm over peer-to-peer asynchronous sensor networks. We propose two major steps to make an asynchronous fusion algorithm to work in a distributed way. First, we develop a local estimator in an optimal batch fashion. The second step is to present the fusion estimator which fuses local estimates in the neighborhood based on covariance intersection algorithm. The proposed algorithm improves local estimates and reduces the estimate disagreement. The effectiveness of the proposed distributed asynchronous fusion algorithm is validated by simulation results.

Quantized steady-state kalman filter in a wireless sensor network

This paper addresses the problem of state estimation in the wireless sensor network (WSN). Firstly, the quantized Kalman filter based on the quantized observations is presented. Focuses are on tradeoff between the communication energy and the estimation accuracy. A closed-form solution to the optimization problem for minimizing the energy consumption is given, where the total energy consumption is minimized subject to a constraint on the stead state error covariance. An illustrative numerical example is provided to demonstrate the usefulness and flexibility of the proposed approach.

Diffusion Kalman filtering with multi-channel decoupled event-triggered strategy and its application to the optic-electric sensor network

Abstract Recently the distributed estimation problem with communication constraints has been widely studied for sensor network application. Our work focus on the diffusion Kalman filter with communication constraints. To satisfy finite communication resources constraints, this paper presents a multi-channel decoupled event-triggered strategy which improves the utilization of the network communication resources. With this strategy, only some entries of sensors’ measurements are transmitted if their triggering criteria are satisfied. We apply this strategy to the step 1 of the diffusion Kalman filter and analyze its performance. The analysis shows that the multi-channel decoupled event-triggered diffusion Kalman filter is unbiased in mean sense and is convergent in mean-square sense. The theoretical steady-state mean-square deviation (MSD) and communication cost are also given in this article. Simulation results demonstrate a good match between the theory analysis and experiment. Finally this algorithm is applied to the optic-electric sensor network, and the results verify the effectiveness of the proposed strategy in terms of the communication resources utilization.

A Distributed sequential fusion algorithm over asynchronous sensor networks

A distributed sequential fusion algorithm for decentralized asynchronous sensor networks is presented in this paper. Our distributed asynchronous sequential fusion algorithm can be divided into a local estimator and a fusion estimator. The local estimator utilize the local measurements to generate a local fusion estimates. And the local fusion estimates are fused by the fusion estimator to improve the local estimation performance. The proposed algorithm is operational for sensor networks with arbitrarily sampling rates and initial sampling time instants. The effectiveness of the proposed algorithm is validated by simulation results.

Consensus-Based Distributed Fusion for Networked Antiaircraft Fire Control System

Renovation of networked campaign modality results in the development of networked antiaircraft fire control system. The traditional network antiaircraft fire control system usually needs a fusion center for target tracking and decision, which would not be suitable for system expansion and may easily suffer from packet loss. Inspired by applications of multi-agent consensus theories, we propose a novel framework of distributed air defense fire control system. Track fusion based on Consensus-Kalman filter technique is put forward, and the filter structure is designed for open architecture and flexibility. The packet loss between the sub-systems is also considered. The proposed structure can be scalable and robust for applications. The illustrative numerical example is provided to demonstrate the usefulness and flexibility of the proposed approach.