Finite precision effect on performance and complexity of particle filters for bearing-only tracking

The complexity reduction of particle filters and their performance due to infinite precision effects are investigated. A method for dynamic range scaling of importance weights is proposed as well as ways for its integration with computation of the weights. Functions are approximated in a way to mitigate dynamic range scaling problems. To reduce complexity, redundant operations of particle filters are correspondingly modified. The sample importance resampling (SIR) method of particle filtering applied to the bearings-only tracking problem is also considered.