Therefore, we propose a TDoA positioning algorithm based on factor graphs.
In this paper, we describe the development of a generic method based on factor graphs to model robot kinematics.
Specifically, in[11], a distributed localization method is presented which is based on factor graphs and relies on cooperation and message-passing between nodes.
Of course, the proposed framework is not unique, so we also refer the reader to an alternative version based on factor graphs [23].
In the first part of the paper, a unified framework based on factor graphs is presented for complexity reduction without loss of optimality.
Specifically, our contribution is twofold: on one hand, we design an iterative receiver that jointly performs LDGM decoding and data fusion based on factor graphs and the Sum Product Algorithm.
In [9], the authors show that an MMSE decoder is unfeasible for large-scale sensor networks, due to its computational complexity, and propose a distributed detection strategy based on factor graphs and the sum product algorithm.
For these reasons, some location-estimation algorithms are based on factor graphs (FGs) [26 30], the errors of these algorithm are expressed in the form of a Gaussian probability density function (PDF).
In this paper, we take a step towards addressing this issue, by proposing a novel approach to biclustering based on factor graphs, which yields high quality solutions and scales more favorably than previous methods.
Based on factor graphs, a variant of the sum-product algorithm [5], namely the min-sum algorithm [12], can then be applied in order for all nodes, through iterative message passing with their respective neighbor nodes, to decide upon the best set of local parameters that can collectively maximize a global performance metric across the network.
