Particle filters approximate the posterior by a finite number of parameters. The posterior \(\text{bel}(x_t)\) is represented by a set of random state samples drawn from this posterior. This representation can represent a much broader space of distributions, but is approximate.

The samples of a posterior distribution are called particles, denoted by:

\begin{equation} X_t := x_t^{[1]}, x_t^{[2]}, \dots \end{equation}

Each particle \(x_t^{[m]}\) is a concrete instantiation of the state at time \(t\): a hypothesis as to what the true world state may be at time \(t\). \(M\) denotes the number of particles in the particle set \(X_t\). The number of particles is often large, and sometimes a function of \(t\) or other quantities related to the belief.

Key Idea

The likelihood for a state hypothesis \(x_t\) to be included in the particle set \(X_t\) should be proportional to its Bayes filter posterior \(\text{bel}(x_t)\):

\begin{equation} x_t^{[m]} \sim p(x_t : z_{1:t}, u_{1:t}) \end{equation}

Hence, the denser the subregion of the state space populated by samples, the more likely it is that the true state falls into this region. This property holds asymptotically for \(m \rightarrow \infty\).


The algorithm first constructs a temporary particle set \(\overline{X}\) which is reminiscent to the belief \(\overline{\text{bel}}(x_t)\). It does this by systematically processing each particle \(x_{t-1}^{[m]}\) in the input particle set \(X_{t-1}\).

\begin{algorithm} \caption{Particle Filter} \label{particle_filter} \begin{algorithmic}[1] \Procedure{ParticleFilter}{$X_{t-1}, u_t, z_t$} \State $\overline{X}_t = X_t = \phi$ \For {$m = 1 \text{ to } M$} \State sample $x_t^{[m]} \sim p(x_t | u_t, x_{t-1}^{[m]})$ \State $w_t^{[m]} = p(z_t | x_t^{[m]})$ \State $\overline{X}_t = \overline{X}_t + \langle x_t^{[m]} , w_t^{[m]} \rangle$ \EndFor \For {$m = 1 \text{ to } M$} \State draw $i$ with probability $\proportional w_t^{[i]}$ \State add $x_t^{[i]} to X_t$ \EndFor \State \Return $X_t$ \EndProcedure \end{algorithmic} \end{algorithm}

\(w_t^{[m]}\) is the importance factor for the particle \(x_t^{[m]}\): the probability of measurement \(z_t\) under the particle \(x_t^{[m]}\).

The second for-loop implements importance re-sampling. The algorithm draws with replacement \(M\) particles from \(\overline{X}_t\). Whereas \(\overline{X}_t\) is distributed according to \(\overline{\text{bel}}(x_t)\), the resampling causes them to be distributed according to the posterior \(\text{bel}(x_t) = \eta p(z_t | x_t^{[m]})\overline{\text{bel}}(x_t)\).


There are four complimentary sources of approximation error:

  1. There are finitely many particles. Non-normalized values \(w_t^{m}\) are drawn from an M-dimensional space, but after normalization they reside in a space of dimension \(M-1\). The effect of loss of dimensionality becomes less pronounced with larger \(M\).
  2. The resampling process induces a loss of diversity in the particle population, manifesting as an approximation error. This is the variance of the estimator. This is countered with several variance reduction techniques:
    1. Reducing the frequency of resampling
    2. low variance sampling
  3. Divergence of proposal and target distribution. Particles are generated from a proposal distribution that does not consider the measurement. If at one extreme, the sensors of the robot are highly inaccurate, but its motion is accurate, the target distribution will be similar to the proposal distribution and the particle filter will be efficient. However, the opposite configuration can cause the distributions to diverge substantially.
  4. Particle deprivation problem: in high dimensional spaces, there may be no particles in the vicinity to the correct state. That is, the number of particles might be too small to cover all relevant regions of high likelihood.