Proceedings of the 2013 IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems May 26-29, 2013, Nanjing, China
Federated Kalman Consensus Filter in Distributed Track Fusion Jiahong Li
Jie Chen, Senior Member, IEEE
Chen Chen, Fang Deng
School of Automation Beijing Institute of Technology Beijing 100081, China Email:
[email protected]
School of Automation Beijing Institute of Technology Beijing 100081, China Email:
[email protected]
School of Automation Beijing Institute of Technology
[email protected],
[email protected]
Abstract—Multi-sensor tracking fusion plays a fundamental role in networked information system, especially in the field of fire control systems. According to the diversity, networked and flexible recombined characteristics of the modern information system, a bottom-up architecture of networked information system and the method of track fusion are investigated. Distributed track fusion problem under limited communication is discussed, and federated Kalman consensus filtering(FKCF) algorithm is proposed. Compared to conventional federated filter, FKCF algorithm considers the mobile sensor model, applies Kalman consensus filter to design the sub-filter and designs information-driven method to improve information allocation. The algorithm not only achieves auto recombination and improves survivability, but increases fused tracking accuracy of mobile sensor network with limited communication capability. The experimental results show that FKCF algorithm is better than conventional federated filtering algorithm in track fusion with limited communication.
I.
I NTRODUCTION
Renovation of networked campaign modality leads to many problems in current fire control systems[1]. Diversity, information sharing and flexible recombination are three main characteristics in networked information system. Networked information system combines a varieties of information units connected with communication network in established structure. It forms network which has flocking structure, and in which accurate target tracking data and commanding order is transmitted. So the information units can be connected and recombined flexibly. Because target observations and commanding order of each information unit in networked information system can be transmitted in time, effective track fusion plays an important role. Recently, Chen[2] proposed networked track fusion based on federated Kalman filter, the filter is suitable to flexible structure, and sub-filter can solve data dropout and time delay according to network transfer state. Wen[3] proposed an optimal and general recursive estimator in Linear Minimum Mean Square Error(LMMSE) for asynchronous fusion problem. However, the above papers regard the information unit as a fixed sensor node, and lack consideration of flexible recombination. To ensure this, peer to peer communication among information units need to be established, and the information unit should be mobile.Aiming at this problem, Spanos, Olfati Saber and Murray[4] proposed scalable sensor fusion strategy that requires fusion of estimation combined
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with local Kalman filter. The key approach is to develop a distributed method to ensure that nodes of a sensor network track the average all measurements. Olfati Saber[5] developed a distributed low-pass filter that solves this tracking problem by reaching an average-consensus, but this algorithm was only applicable to sensors with identical observation matrices.To solve the problem, Olfati Saber[6] designed modified distributed Kalman filtering algorithm that uses two identical consensus filters for fusion of sensor data and covariance, and designed[7] group strategy for free group and constrained group. In order to track the target more accurately, Olfati Saber[8] proposed information induced mobile sensor strategy, and utilized Kalman Consensus Filtering algorithm. Olfati Saber[9] found the optimal decentralized Kalman consensus filter, introduced a scalable suboptimal Kalman consensus filter and provided a formal stability and performance analysis of it. Olfati Saber and P. Jalalkamali provided a formal stability analysis of continuous Kalman consensus filtering algorithm for tracking m targets using n mobile sensors for one case of m n[10] and the other case of m n[11]. Zongyao Wang and Dongbing Gu[12] proposed to use a novel distributed Kalman Filter to estimate the target position and a distributed flocking algorithm to communicate with other robots. Our main result is to establish the architecture of networked information system by multi-agent theory, and propose a distributed federated filtering algorithm which combines Kalman consensus filter with federated filter to solve the problem of track fusion. The algorithm not only achieves auto recombination by information-driven method and improves survivability, but improves fused tracking accuracy of mobile sensor network with limited communication capability. The outline of the paper is as follows. The architecture of networked information system in Section II. Our main theoretical results on distributed target tracking fusion algorithms are provided in Section III. Our experimental results are presented in Section IV. Finally, concluding remarks are made in Section V. II.
A RCHITECTURE OF NETWORKED INFORMATION SYSTEM
A. Basic concepts and composition of system Networked information system is the system that links information units within a certain region together and forms
real time high speed information network. In the network, distributed information processing technology is used to let every information units collaborate efficiently and get accurate data of situations. Considering network survivability, a distributed bottom-up architecture is established. The architecture is very suitable for solving tracking fusion problem, the main idea is as follows. Regard each information unit as a mobile sensor, and each sensor can only communicate with other eligible sensors for limited network capability. Using multi-agent flocking control method, sensors are organized in a group, and each information group cannot communicate but can be connected with information center, receiving and dispatching data and order, as in Fig. 1(IU is represented as information unit).
III.
A. Data processing principle One target can be observed by multiple sensors, and each sensor can only be connected with sensors eligible to exchange information for limited communication and observation capability. Information group network is established via flocking control method, and distributed target estimations which transmit from the network are fused in the information center, as in Fig.2. /hĐĞŶƚĞƌ
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