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

Abbr. | Proposed in | Discussed in | Comment |
---|---|---|---|

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. |