This article presents a low-complexity and high-accuracy algorithm using message-passing approach to reduce the computational load of the traditional tracking algorithm for location estimation. In the proposed tracking scheme, a state space model for the location-estimation problem can be divided into many mutual-interaction local constraints based on the inherent message-passing features of factor graphs. During each iteration cycle, the message with reliability information is passed efficiently with an adaptive weighted technique and the error propagation law, and then the message-passing approach based on prediction-correction recursion is to simplify the implementation of the Bayesian filtering approach for location-estimation and tracking systems. As compared with a traditional tracking scheme based on Kalman filtering (KF) algorithms derived from Bayesian dynamic model, the analytic result and the numerical simulations show that the proposed forward and one-step backward tracking approach not only can achieve an accurate location very close to the traditional KF tracking scheme, but also has a lower computational complexity.

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This paper presents the performance of an adaptive location-estimation technique combining Kalman filtering (KF) with vision assisting for wireless sensor networks. For improving the accuracy of a location estimator, a KF procedure is employed at a mobile terminal to filter variations of the location estimate. Furthermore, using a vision-assisted calibration technique, the proposed approach based on the normalized cross-correlation scheme is an accuracy enhancement procedure that effectively removes system errors causing uncertainty in real dynamic environments. Namely, according to the vision-assisted approach to extract the locations of the reference nodes as landmarks, a KFbased approach with the landmark information can calibrate the location estimation and reduce the corner effect of a location-estimation system. In terms of the location accuracy estimated from the proposed approach, the experimental results demonstrate that more than 60 percent of the location estimates have error distances less than 1.4 meters in a ZigBee positioning platform. As compared with the non-tracking algorithm and non-vision-assisted approach, the proposed algorithm can achieve reasonably good performance.