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Table 2 Overview of the evaluated algorithms

From: Multiple particle filtering for tracking wireless agents via Monte Carlo likelihood approximation

Abbr.Proposed inDiscussed inComment
PE[8]Eliminates the dependency of the likelihood on the other agent’s state by means of the point estimate \(\boldsymbol {\hat {x}}_{\mathbbm {j},k} = \sum _{\ell =1}^{l} w_{\mathbbm {j},k-1}^{\ell } \boldsymbol {x}_{\mathbbm {j},k.}^{\ell }\)
GA[14] (basic form)Section 3.1 (generalized form)Approximates the likelihood as a Gaussian density using first- and second-order terms for the variance and mean, respectively. Treats \(\boldsymbol {x}_{\mathbbm {j},k}\) as a random variable in the likelihood.
EGAThis workSections 3.1.3 and 3.1.4Extension of GA to a complete second-order approx. for both additive and multiplicative noise.
MCAThis workSection 4MC-based likelihood approx.