Vehicle dynamics prediction via adaptive robust unscented particle filter

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...

Unscented Kalman Filter for Combined Longitudinal and Lateral Vehicle Dynamics

Advanced driver assistance system development is a rapidly growing field in modern automotive engineering. These systems ensure increased safety and comfort for passenger vehicles by actively influencing the vehicle dynamics. Most of these systems rely on the current driving stability, described by internal states. Since many of these states cannot be measured directly or only under high costs,...

Robust Vehicle Tracking Multi-feature Particle Filter

Object detection and tracking have been studied separately in most cases. Particle filtering has proven very successful for non-linear and nonGaussian estimation problems. This paper presents a new method for tracking moving vehicles with temporal disappearance. The proposed method can continue tracking after disappearance. Color distribution of objects is integrated into particle filtering alg...

Tracking Moving Object via Unscented Particle Filter in Sensor Network

Moving object tracking is one of the typical applications in wireless sensor network (WSN). As a result, a lot of important solutions have been proposed in the last decade, toward addressing different aspects of object tracking in WSN settings. This work describes an Unscented particle filter (UPF) based Moving Object Tracking algorithm, UMOT, in WSN settings, where the sensor nodes are cluster...