A fast resampling scheme for particle filters
Tiancheng Li, Dedong Tang, Tariq Sattar · 2013
An unbiased resampling method is proposed for particle filters which is computing fast for implementation. There are two differences of our approach from other methods. First, the number of the particles is not fixed but varies around a reference. Second, it is a deterministic sampling procedure since there is no random numbers used. The core idea is simply replicating each particle as many times as the rounding result on the product of the reference number and weight of the particle. As an extension, the application of random numbers in resampling is discussed. Simulations show that our approach obtains comparable estimation accuracy with traditional resampling methods but be faster. (4 pages)