Abstract:
The sample impoverishment problem in particle
filters is investigated from the perspective of genetic algorithms.
The contribution of this paper is in the proposal of a hybrid
technique to mitigate sample impoverishment such that
the number of particles required and hence the computation
complexity are reduced. Studies are conducted through the use
of Chebyshev inequality for the number of particles required.
The relationship between the number of particles and the
time for impoverishment is examined by considering the takeover
phenomena as found in genetic algorithms. It is revealed
that the sample impoverishment problem is caused by the resampling
scheme in implementing the particle filter with a
finite number of particles. The use of uniform or roulette-wheel
sampling also contributes to the problem. Crossover operators
from genetic algorithms are adopted to tackle the finite particle
problem by re-defining or re-supplying impoverished particles
during filter iterations. Effectiveness of the proposed approach
is demonstrated by simulations for a monobot simultaneous
localization and mapping application.
