Restarting Particle Filters: An Approach to Improve the Performance of Dynamic Indoor Localization

Particle filters have been found to be effective in tracking mobile targets in indoor environments. One frequently encountered problem in these settings occurs when the target's movement pattern changes unexpectedly; such as when the target turns around, enters a room from a corridor or turns left or right at an intersection. If the particle filter makes an incorrect prediction, it might not be able to recover using the normal techniques of prediction, weight update and resampling. We propose an approach to automatically restart the particle filter by sampling the latest trusted observation when the particle cloud diverges too much from the observations. The restart decision is based on Kullback-Leibler divergence between the probability surfaces associated with the current observation and the particle cloud. Through an experimental study we show that the restart algorithm allows the successful early recovery of stranded particle filters, in our scenarios providing a 36% average improvement in localization accuracy.