An enhanced particle filtering method for GMTI radar tracking

On Tracking-driven Road Map Extraction from Gmti Radar Data

For analyzing dynamic scenarios characterized by many ground moving vehicles, airborne GMTI radar is a well-suited sensor due to its wide-area, all-weather, day/night, and real time capabilities (GMTI: Ground Moving Target Indicator). The generation of GMTI tracks from these data is the backbone for producing a “recognized ground picture” as well as for analyzing traffic flows. In this paper we...

Enhanced Importance Sampling: Unscented Auxiliary Particle Filtering for Visual Tracking

The particle filter has attracted considerable attention in visual tracking due to its relaxation of the linear and Gaussian restrictions in the state space model. It is thus more flexible than the Kalman filter. However, the conventional particle filter uses system transition as the proposal distribution, leading to poor sampling efficiency and poor performance in visual tracking. It is not a ...

Airborne MIMO GMTI Radar

Particle Filtering For Target Tracking

Particle filtering is a sequential Monte Carlo technique that recursively computes the posterior probability density function using the concept of “Importance Sampling”. This paper considers the application of particle filtering technique to a target tracking application, in which a radar sends a signal towards a target and estimates the state (position and velocity) of the target using the obs...

Joint target tracking and classification with particle filtering and mixture Kalman filtering using kinematic radar information

This paper considers the problem of joint maneuvering target tracking and classification. Based on recently proposed Monte Carlo techniques, a multiple model (MM) particle filter and a mixture Kalman filter (MKF) are designed for two-class identification of air targets: commercial and military aircraft. The classification task is carried out by processing radar measurements only, no class (feat...