Table 2 Overview of the evaluated algorithms

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.

EGA

This work

Sections 3.1.3 and 3.1.4

Extension of GA to a complete second-order approx. for both additive and multiplicative noise.

MCA

This work

Section 4

MC-based likelihood approx.