javaslam.filter
Interface NonlinearFilter

All Known Implementing Classes:

LinearizedFilter

public interface NonlinearFilter

A filter for nonlinear systems that maintains an approximate Gaussian belief state.

Method Summary

void	measurement(ListSet vars, NoisyVectorFunction g, double[] y) Performs an approximate nonlinear measurement update.
void	time(ListSet vars, NoisyVectorFunction f) Performs an approximate nonlinear time update.

Method Detail

measurement

public void measurement(ListSet vars,
                        NoisyVectorFunction g,
                        double[] y)

Performs an approximate nonlinear measurement update. The parameters vars, g, and q define the measurement equation as follows:

y=g(x[vars], v)

where v is an independent white noise vector. Given the actual measurement y, this method updates the belief state.

Parameters:

vars - an ordered set of the variables with sum dimension n in the belief state that causally influenced this measurement.

g - the noisy measurement function with input dimension n + m and output dimension k; the input is formed from x[vars] stacked on top of the noise vector v

y - the measurement k-vector

Throws:

IllegalArgumentException - if there are any dimension mismatches

time

public void time(ListSet vars,
                 NoisyVectorFunction f)

Performs an approximate nonlinear time update. The parameters vars, f, and q define the state evolution equation as follows:

x_{t + 1}[vars]= f(x_t[vars], w)

where w is an independent white noise vector. All variables not in vars are assumed stationary.

Parameters:

vars - an ordered set of the variables with sum dimension n in the belief state that evolve.

f - the state evolution function with input dimension n + m and output dimension n

Throws:

IllegalArgumentException - if there are any dimension mismatches