Tracking filter and multi-sensor data fusion

Multi - Sensor Data Fusion for Representing and Tracking Dynamic Objects

Speaker Tracking Using Particle Filter Sensor Fusion

in Proc. of Asian Conference on Computer Vision (ACCV), 2004 Sensor fusion for object tracking has become an active research direction during the past few years. But how to do it in a robust and principled way is still an open problem. In this paper, we propose a new fusion framework that combines both the bottom-up and top-down approaches to probabilistically fuse multiple sensing modalities. ...

Multi-Sensor Management for Data Fusion in Target Tracking

Multi-sensor management for data fusion in target tracking concerns issues of sensor assignment and scheduling by managing or coordinating the use of multiple sensor resources. Since a centralized sensor management technique has a crucial limitation in that the failure of the central node would cause whole system failure, a decentralized sensor management (DSM) scheme is increasingly important ...

Adaptive Fusion of Inertial Navigation System and Tracking Radar Data

Against the range-dependent accuracy of the tracking radar measurements including range, elevation and bearing angles, a new hybrid adaptive Kalman filter is proposed to enhance the performance of the radar aided strapdown inertial navigation system (INS/Radar). This filter involves the concept of residual-based adaptive estimation and adaptive fading Kalman filter and tunes dynamically the fil...

Sensor Data Fusion Using Kalman Filter

Autonomous Robots and Vehicles need accurate positioning and localization for their guidance, navigation and control. Often, two or more different sensors are used to obtain reliable data useful for control system. This paper presents the data fusion system for mobile robot navigation. Odometry and sonar signals are fused using Extended Kalman Filter (EKF) and Adaptive Fuzzy Logic System (AFLS)...