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Particle filter

Particle filters, also known as Sequential Monte Carlo methods (SMC), are sophisticated model estimation techniques based on simulation.

They are usually used to estimate Bayesian models and are the sequential ('on-line') analogue of Markov chain Monte Carlo (MCMC) batch methods and are often similar to importance sampling methods. If well designed, particle filters can be much faster than MCMC. They are often an alternative to the Extended Kalman filter (EKF) or Unscented Kalman filter (UKF) with the advantage that, with sufficient samples, they approach the Bayesian optimal estimate, so they can be made more accurate than the EKF or UKF. The approaches can also be combined by using a version of the Kalman filter as a proposal distribution for the particle filter.

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1 Goal
2 Model
3 Monte Carlo approximation
4 Sampling Importance Resampling (SIR)
5 Sequential Importance Sampling (SIS)
6 "Direct version" algorithm
7 Other Particle Filters
8 See also
9 References

Goal

The particle filter aims to estimate the sequence of hidden parameters, $x k$ for $k=0,1,2,3, \ldots$ , based only on the observed data $y k$ for $k=0,1,2,3, \ldots$ . All Bayesian estimates of $x k$ follow from the posterior distribution $p(x_k|y_0,y_1,\ldots,y_k)$ . In contrast, the MCMC or importance sampling approach would model the full posterior $p(x_0,x_1,\ldots,x_k|y_0,y_1,\ldots,y_k)$ .

Model

Particle methods assume $x k$ and the observations $y k$ can be modeled in this form:

$x_0, x_1, \ldots$ is a first order Markov process such that
$x_k|x_{k-1} \sim p_{x_k|x_{k-1}}(x|x_{k-1})$

and with an initial distribution $p (x 0)$ .

The observations $y_0, y_1, \ldots$ are conditionally independent provided that $x_0, x_1, \ldots$ are known

In other words, each

y k

only depends on

x k

y k | x k ˜ p y | x (y | x k)

One example form of this scenario is

$x_k = f(x_{k-1}) + v_k \,$

$y_k = h(x_k) + w_k \,$

where both $v k$ and $w k$ are mutually independent and identically distributed sequences with known probability density functions and $f(\cdot)$ and $h(\cdot)$ are known functions. These two equations can be viewed as state space equations and look similar to the state space equations for the Kalman filter. If the functions $f(\cdot)$ and $h(\cdot)$ were linear, and if both $v k$ and $w k$ were Gaussian, the Kalman filter finds the exact Bayesian filtering distribution. If not, Kalman filter based methods are a first-order approximation. Particle filters are also an approximation, but with enough particles can be much more accurate.

Monte Carlo approximation

Particle methods, like all sampling-based approaches (e.g., MCMC), generate a set of samples that approximate the filtering distribution $p(x_k|y_0,\dots,y_k)$ . So, with $P$ samples, expectations with respect to the filtering distribution are approximated by

$\int f(x_k)p(x_k|y_0,\dots,y_k)dx_k\approx\frac1P\sum_{L=1}^Pf(x_k^{(L)})$

and $f(\cdot)$ , in the usual way for Monte Carlo, can give all the moments etc. of the distribution up to some degree of approximation.

Generally, the algorithm is repeated iteratively for a specific number of $k$ values (call this $N$ ). Initializing $x k = 0 | k = 0$ for all particles provides a starting place to generate $x 1$ , which can then be used to generate $x 2$ , which can be used to generate $x 3$ and so on up to $k = N$ . When done, the mean of $x k$ over all the particles (or $\frac{1}{P}\sum_{L=1}^P x_k^{(L)}$ ) is approximately the actual value of $x k$ .

Sampling Importance Resampling (SIR)

Sampling importance resampling (SIR) is a very commonly used particle filtering algorithm, which approximates the filtering distribution $p(x_k|y_0,\ldots,y_k)$ by a weighted set of particles

$\{(w^{(L)}_k,x^{(L)}_k)~:~L=1,\ldots,P\}$ .

The importance weights $w^{(L)}_k$ are approximations to the relative posterior probabilities (or densities) of the particles such that $\sum_{L=1}^P w^{(L)}_k = 1$ .

SIR is a sequential (i.e., recursive) version of importance sampling. As in importance sampling, the expectation of a function $f(\cdot)$ can be approximated as a weighted average

$\int f(x_k) p(x_k|y_0,\dots,y_k) dx_k \approx \sum_{L=1}^P w^{(L)} f(x_k^{(L)}).$

For a finite set of particles, the algorithm performance is dependent on the choice of the proposal distribution

$\pi(x_k|x_{0:k-1},y_{0:k})\,$ .

The optimal proposal distribution is given as the target distribution

$\pi(x_k|x_{0:k-1},y_{0:k}) = p(x_k|x_{k-1},y_{k}). \,$

However, the transition prior is often used as importance function, since it is easier to draw particles (or samples) and perform subsequent importance weight calculations:

$\pi(x_k|x_{0:k-1},y_{0:k}) = p(x_k|x_{k-1}). \,$

Sampling Importance Resampling (SIR) filters with transition prior as importance function are commonly known as bootstrap filter and condensation algorithm.

Resampling is used to avoid the problem of degeneracy of the algorithm, that is, avoiding the situation that all but one of the importance weights are close to zero. The performance of the algorithm can be also affected by proper choice of resampling method. The stratified resampling proposed by Kitagawa (1996) is optimal in terms of variance.

A single step of sequential importance resampling is as follows:

1) For $L=1,\ldots,P$ draw samples from the proposal distribution

$x^{(L)}_k \sim \pi(x_k|x^{(L)}_{0:k-1},y_{0:k})$

2) For $L=1,\ldots,P$ update the importance weights up to a normalizing constant:

$\hat{w}^{(L)}_k = w^{(L)}_{k-1} \frac{p(y_k|x^{(L)}_k) p(x^{(L)}_k|x^{(L)}_{k-1})} {\pi(x_k^{(L)}|x^{(L)}_{0:k-1},y_{0:k})}.$

3) For $L=1,\ldots,P$ compute the normalized importance weights:

$w^{(L)}_k = \frac{\hat{w}^{(L)}_k}{\sum_{J=1}^P \hat{w}^{(J)}_k}$

4) Compute an estimate of the effective number of particles as

$\hat{N}_\mathit{eff} = \frac{1}{\sum_{L=1}^P\left(w^{(L)}_k\right)^2}$

5) If the effective number of particles is less than a given threshold $\hat{N}_\mathit{eff} < N_{thr}$ , then perform resampling:

a) Draw

P

particles from the current particle set with probabilities proportional to their weights. Replace the current particle set with this new one.

b) For $L=1,\ldots,P$ set $w^{(L)}_k = 1/P$ .

The term Sequential Importance Resampling is also sometimes used when referring to SIR filters.

Sequential Importance Sampling (SIS)

Is the same as Sampling Importance Resampling, but without the resampling stage.

"Direct version" algorithm

The "direct version" algorithm is rather simple (compared to other particle filtering algorithms) and it uses composition and rejection. To generate a single sample $x$ at $k$ from $p_{x_k|y_{1:k}}(x|y_{1:k})$ :

1) Set p=1

2) Uniformly generate L from

{1,..., P}

3) Generate a test $\hat{x}$ from its distribution $p_{x_k|x_{k-1}}(x|x_{k-1|k-1}^{(L)})$

4) Generate the probability of $\hat{y}$ using $\hat{x}$ from $p_{y|x}(y_k|\hat{x})$ where

y k

is the measured value

5) Generate another uniform u from

[0, m k]

6) Compare u and $\hat{y}$

6a) If u is larger then repeat from step 2

6b) If u is smaller then save $\hat{x}$ as

x k | k (p)

and increment p

7) If p > P then quit

The goal is to generate P "particles" at $k$ using only the particles from $k - 1$ . This requires that a Markov equation can be written (and computed) to generate a $x k$ based only upon $x k - 1$ . This algorithm uses composition of the P particles from $k - 1$ to generate a particle at $k$ and repeats (steps 2-6) until P particles are generated at $k$ .

This can be more easily visualized if $x$ is viewed as a two-dimensional array. One dimension is $k$ and the other dimensions is the particle number. For example, $x (k, L)$ would be the L^th particle at $k$ and can also be written $x_k^{(L)}$ (as done above in the algorithm). Step 3 generates a potential $x k$ based on a randomly chosen particle ( $x_{k-1}^{(L)}$ ) at time $k - 1$ and rejects or accepts it in step 6. In other words, the $x k$ values are generated using the previously generated $x k - 1$ .

Other Particle Filters

Auxiliary particle filter
Gaussian particle filter
Unscented particle filter
Monte Carlo particle filter
Gauss-Hermite particle filter
Cost Reference particle filter

References

Sequential Monte Carlo Methods in Practice, by A Doucet, N de Freitas and N Gordon. Springer, 2001. ISBN 978-0387951461
On Sequential Monte Carlo Sampling Methods for Bayesian Filtering, by A Doucet, C Andrieu and S. Godsill, Statistics and Computing, vol. 10, no. 3, pp. 197-208, 2000 CiteSeer link
Tutorial on Particle Filters for On-line Nonlinear/Non-Gaussian Bayesian Tracking (2001); S. Arulampalam, S. Maskell, N. Gordon and T. Clapp; CiteSeer link
Particle Methods for Change Detection, System Identification, and Control, by Andrieu C., Doucet A., Singh S.S., and Tadic V.B., Proceedings of the IEEE, Vol 92, No 3, March 2004. SMC Homepage link
A Tutorial on Bayesian Estimation and Tracking Techniques Applicable to Nonlinear and Non-Gaussian Processes, A.J. Haug, The MITRE Corporation; Mitre link
Distributed Implementations of Particle Filters, Anwer Bashi, Vesselin Jilkov, Xiao Rong Li, Huimin Chen, Available from the University of New Orleans
A survey of convergence results on particle filtering methods for practitioners, Crisan, D. and Doucet, A., IEEE Transactions on Signal Processing 50(2002)736-746 doi:10.1109/78.984773
Beyond the Kalman Filter: Particle Filters for Tracking Applications, by B Ristic, S Arulampalam, N Gordon. Artech House, 2004. ISBN 1-58053-631-x
Filtering via simulation: auxiliary particle filter, by M. K. Pitt and N. Shephard, Journal of the Americian Statistical Association, 94 (1999), 590-599.

Category: Monte Carlo methods

This article is licensed under the GNU Free Documentation License. It uses material from the Wikipedia article "Particle_filter". A list of authors is available in Wikipedia.