some minor changes and added smoothing transition section

tex/chapters/relatedwork.tex

@@ -6,6 +6,8 @@
 Sequential MC filter, like aforementioned particle filter, use all observations $\mObsVec_{1:t}$ until the current time $t$ for computing an estimation of the state $\mStateVec_t$. 
 In a Bayesian setting, this can be formalized as the computation of the posterior distribution $p(\mStateVec_t \mid \mObsVec_{1:t})$ using a sample of $N$ independent random variables, $\vec{X}^i_{t} \sim (\mStateVec_t \mid \mObsVec_{1:t})$ for $i = 1,...,N$ for approximation. 
 Due to importance sampling, a weight $W^i_t$ is assigned to each sample $\vec{X}^i_{t}$.
+In context of particle filtering $\{\vec{X}^i_{1:t}, W^i_{1:t} \}_{i=1}^N$ is a weighted set of samples, also called particles.
+Therefore a particle is a representation of one possible system state $\mStateVec$.
 By considering a situation given all observations $\vec{o}_{1:T}$ until a time step $T$, where $t \ll T$, standard filtering methods are not able to make use of this additional data for computing $p(\mStateVec_t \mid \mObsVec_{1:T})$.
 This problem can be solved with a smoothing algorithm.