A Fusion Location Algorithm Based on Federated Kalman Filtering

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The paper designed a WLAN system with RFID systems joint locating algorithm based on Federated Kalman Filtering. For the problem in indoor mobile node single positioning system, such as insufficient positioning accuracy, longer positioning time and not ideal algorithm complexity, it was proposed that the federated filtering techniques was applied in the location system, and was combined with data from WLAN and RFID to realize all accuracy improved. In the system, sub-filter use unscented Kalman filter algorithm independent measurement updates and time update, and the subsystem information is fused with the main filter based on no feedback mode. It was proved from the result that the calculation improved was of feasibility, which reduced unscented Kalman filter calculation, decreased maximum deviation and minimum deviation and improved the positioning accuracy. Introduction With the increase of the number of mobile devices and improvement of the degree of intelligence, location technology [1] is highly valued by the researcher as the core support functions of various kinds of mobile applications. At present, common indoor positioning technology includes ultrasonic positioning technology, WLAN positioning technology, Zigbee positioning technology, infrared positioning technology and RFID positioning technology. One single positioning technology is difficult to achieve a satisfactory result in practice, so multi-sensor data fusion technology has become an important issue in the field of indoor mobile node localization. WLAN positioning technology has developed rapidly in recent years,which mainly includes Nibble system[2] based on acquisition and analysis of noise ratio or signal quality, WHAM! Positioning system[3] based on receiving the signal strength of AP, Ekahau positioning system[4] , Horus positioning systems and Rice systems.The RFID system,whose positioning time is shorter, the precision is higher, received much concern for its advantages of non-line and non contract ,although the positioning range is limited to the distance between the transmitter and the receiver .This paper is to effectively improve the positioning accuracy of the mobile node and real-time through information fusion of WLAN positioning system and RFID positioning system based on Federated Kalman Filtering, which has certain practical value. Theoretical basis Federated Kalman Filtering Model Traditional Kalman filtering technology for multi-sensor data fusion methods generally includes centralized Kalman filtering and the decentralized Kalman filter. Federated filter proposed by Carlson[5] achieves good effect in practical application because of its small amount of calculation, good fault-tolerant performance and flexible design. Fusion algorithm based on Federated Kalman Filtering includes algorithm both whose estimate for each sub filter is correlated and uncorrelated. Due to the premise that the various sub filters is uncorrelated can not be guaranteed in the actual use,this paper mainly study the fusion algorithm for the estimate of each sub filter,using the variance upper bound technique to transform the filtering algorithm,thus each sub filter will be estimated from the correlated state.Federated filter is a kind of two-stage filtering structure. It is assumed that the state estimation of each sub filter can be 4th International Conference on Machinery, Materials and Computing Technology (ICMMCT 2016) © 2016. The authors Published by Atlantis Press 2001 expressed as: ˆ ˆ ˆ ci i bi X X X   =       . ˆ ci X is the estimate of the common state c X for each sub filter. ˆ bi X is the proprietary state estimate of filter i . Process design of Federated Kalman filter Under normal circumstances, federated filter process mainly includes four processes as follow : information distribution, information update, measurement update of information and information fusion. Step1 Distribution of information.Determine the proportionality coefficient of information distribution,between the main filters and sub filters as shown in formula (1). 1 k Q − and ( ) 1 | g k k P − refer to system process information.In formula (1), parameter i β is called an information distribution coefficient to meet the conditions of 0 i β > and distribution principle in formula (2).