Abstract
Abstract 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.
Highlights
With the rapid advance in technologies and infrastructures, wireless communication systems have been getting great momentum due to the convenience that they can provide
This article presents a forward scheme for location estimation and a one-step backward scheme for speed estimation with message-passing algorithm to implement the Bayesian approach
According to the proposed algorithm, is a new adaptive weighted scheme used to reduce the computational complexity of traditional tracking schemes, and the idea of decoupling X and Y dimensions is used to simplify the implementation for location estimation
Summary
With the rapid advance in technologies and infrastructures, wireless communication systems have been getting great momentum due to the convenience that they can provide. To reduce the computational load of the KF tracking algorithm and to get more flexible tracking schemes than some fixed coefficient or degenerate form algorithms are worth developing low-complexity techniques for location-estimation and tracking systems. For these reasons, some location-estimation algorithms are based on factor graphs (FGs) [26,27,28,29,30], the errors of these algorithm are expressed in the form of a Gaussian probability density function (PDF).
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