Robust Interacting Multiple Model Unscented Particle Filter for Navigation

Robust Adaptive Unscented Particle Filter

The Unscented Particle Filter

In this paper, we propose a new particle filter based on sequential importance sampling. The algorithm uses a bank of unscented filters to obtain the importance proposal distribution. This proposal has two very "nice" properties. Firstly, it makes efficient use of the latest available information and, secondly, it can have heavy tails. As a result, we find that the algorithm outperforms standar...

Interacting Multiple Model Adaptive Unscented Kalman Filters for Navigation Sensor Fusion

The unscented Kalman filter (UKF) is adopted in the interacting multiple model (IMM) framework to deal with the system nonlinearity in navigation applications. The adaptive tuning system (ATS) is employed for assisting the unscented Kalman filter in the IMM framework, resulting in an interacting multiple model adaptive unscented Kalman filter (IMM-AUKF). Two models, a standard UKF and an adapti...

An MCMC-Based Particle Filter for Tracking Multiple Interacting Targets

We describe a Markov chain Monte Carlo based particle filter that effectively deals with interacting targets, i.e., targets that are influenced by the proximity and/or behavior of other targets. Such interactions cause problems for traditional approaches to the data association problem. In response, we developed a joint tracker that includes a more sophisticated motion model to maintain the ide...

An Adaptive Particle Filter Method for Tracking Multiple Interacting Targets

In this paper, we present a new adaptive particle filter method for tracking multiple interacting targets. We introduce a sampling strategy to consecutively sample particles and adapt their spreading according to current measurements. For multiple targets, we develop a new concept of guiding the particles by current measurements of adjacent targets. Our method does not increase the computationa...