A Low-Complexity Data-Fusion Algorithm Based on Adaptive Weighting for Location Estimation

In this paper, a tracking scheme based on adaptive weighted technique is proposed to reduce the computational load of traditional data-fusion algorithm for heterogeneous measurements. For the location-estimation technique with the data fusion of radio-based ranging measurement and speed-based sensing measurement, the proposed tracking scheme based on the Bayesian approach is handled by a state space model; a weighted technique with the reliability of message passing is based on the error propagation law. As compared with a traditional data-fusion algorithm based on a Kalman filtering approach, the proposed scheme that combines radio ranging measurement with speed sensing measurement for data fusion has much lower computational complexity with acceptable location accuracy.