1 Introduction
2 System model
2.1 Signal model
2.2 Error bound
3 Information decomposition and quantization
3.1 Decomposition of FIM
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Prediction:$$\begin{array}{@{}rcl@{}} \begin{array}{l} p\left({{\boldsymbol{x}^{(\mathrm{T})}}|{\boldsymbol{z}^{(1:\mathrm{T} - 1)}}} \right)\\ = \int {p\left({{\boldsymbol{x}^{(T)}}|{\boldsymbol{x}^{(\mathrm{T} - 1)}}} \right)p\left({{\boldsymbol{x}^{(\mathrm{T} - 1)}}|{\boldsymbol{z}^{(1:\mathrm{T} - 1)}}} \right)} d{\boldsymbol{x}^{(\mathrm{T} - 1)}} \end{array} \end{array} $$(9)
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Correction:$$\begin{array}{@{}rcl@{}} \begin{array}{l} p\left({{\boldsymbol{x}^{(\mathrm{T})}}|{\boldsymbol{z}^{(1:\mathrm{T})}}} \right)\\ = \int {p\left({{\boldsymbol{x}^{(\mathrm{T})}},{\boldsymbol{x}^{(\mathrm{T} - 1)}}|{\boldsymbol{z}^{(1:\mathrm{T})}}} \right)} d{\boldsymbol{x}^{(\mathrm{T} - 1)}}\\ \propto p\left({{\boldsymbol{z}^{(\mathrm{T})}}|{\boldsymbol{x}^{(\mathrm{T})}}} \right)p\left({{\boldsymbol{x}^{(\mathrm{T})}}|{\boldsymbol{z}^{(1:\mathrm{T} - 1)}}} \right) \end{array} \end{array} $$(10)
3.2 Decomposition and presentation of EFI
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Large amount of node information means a small positioning error.
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Each node newly introduced can increase the information, meanwhile reduce the positioning error.
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Cooperation information can be represented by RII and RDM.
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There are many factors affecting the localization error including the distribution of nodes and the channel quality.