Abstract
A new positioning algorithm based on RSS measurement is proposed. The algorithm adopts maximum likelihood estimation and semi-definite programming. The received signal strength model is transformed to a non-convex estimator for the positioning of the target using the maximum likelihood estimation. The non-convex estimator is then transformed into a convex estimator by semi-definite programming, and the global minimum of the target location estimation is obtained. This algorithm aims at the known problem and then extends its application to the case of unknown. The simulations and experimental results show that the proposed algorithm has better accuracy than the existing positioning algorithms.
Highlights
With the development of wireless sensor networks (WSNs) [1,2,3,4,5,6,7,8,9,10,11,12,13], the Internet of things has become common
This paper proposes a new positioning algorithm based on maximum likelihood estimation and semi-definite programming (“MLE-SDP”), which takes into account the variation of noise standard deviation
A new algorithm based on maximum likelihood criterion for unknown point location using semi-definite programming estimation is studied
Summary
With the development of wireless sensor networks (WSNs) [1,2,3,4,5,6,7,8,9,10,11,12,13], the Internet of things has become common. This paper proposes a new positioning algorithm based on maximum likelihood estimation and semi-definite programming (“MLE-SDP”), which takes into account the variation of noise standard deviation. The basic steps of this algorithm is to transform the path loss model of the received signal into a relatively simple expression without a logarithm and expand the term with noise by Taylor series to obtain a new noise term. The estimator of the target can be obtained by solving the convex problem. The RSS model is transformed into a pseudo-linear system with new noise; Based on LS criterion, a new non-convex objective function is derived to solve the target positioning problem; The non-convex objective function is transformed into a convex objective function by semi-definite programming.
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