A Location-Estimation Experimental Platform Based on Error Propagation for Wireless Sensor Networks
This paper presents a location-estimation experimental platform based on the error propagation approach to reduce the computational load of traditional algorithms. For the experimental platform with the scalar information, the proposed technique based on the Bayesian approach is handled by a state space model; a weighted technique with the reliability of the information passing is based on the error propagation law. As compared with a traditional Kalman filtering (KF) algorithm, the proposed algorithm has much lower computational complexity with the decoupling approach. Numerical simulations and experimental results show that the proposed location-estimation algorithm can achieve the location accuracy close to that of the KF algorithm.
KeywordsBayesian filtering Error propagation Location estimation Wireless sensor networks ZigBee positioning system
This work was supported in part by the National Science Council of the Republic of China (R.O.C.) under Grants NSC 101-2218-E-033-007 and NSC 101-2221-E-130 -017.
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