Optimal linear estimation fusion. I. Unified fusion rules

Data Fusion Performance Evaluation for Dissimilar Sensors: Application to Road Obstacle Tracking

“…Thus, the aim of the fusion center is to estimate Cartesian target state and , its associated covariance, with respect to every , using all available information . Estimation fusion can be classified into three categories, depending on which information is available at the fusion module (Li et al, 2003) hybrid fusion if available information at the fusion center includes both unprocessed data from one sensor and processed data from the other one. In this chapter, we focus on centralized and decentralized fusion.…”

“…It uses a laser based 3D-sensor (Laser Mirror Scanner LMS-Z210-60 from Riegl) which measures range and angles, combined with a radar sensor which delivers range and range rate. For the laser sensor, since target motion is best described in Cartesian coordinates but measurements are available in sensor coordinates, a commonly used method is to convert measurements from sensor to Cartesian coordinates (Li & Jilkov, 2001). Thus, we combine Cartesian target coordinates and with radar range and radar range rate target measurements.…”

Sensor Data Fusion Using Mutual Information Algorithm

“…First hybrid algorithms of multiple models which combined few models in form of linear combination return to late 1970s, worked separately by Chong and Spyer on optimal expanded kalman filters (Chong, Spyer, 1979) and later changed to a unique model (Li, 2003). Then in late 1980s, hybrid algorithms expanded upon nonlinear systems, too (Xu, 2003, Torra, 2007.…”

Distributed Kalman filtering

“…Other related publications cited in the Table 2.7 are [338]- [339], [337], [332], [333,334], [37], [46], [47], [48], [48], [60], [59,58], [57], [56], [55], [54], [53], [52]. Other related publications cited in the Table 2.8 are [67], [68], [91], [93,94], [95], [115], [116], [117], [124], [125], [127] and [128].…”