Sensor data fusion is essential for environmental perception within smart traffic applications. By using multiple sensors cooperatively, the accuracy and probability of are increased, which crucial critical scenarios or under bad weather conditions. In this paper, a modular real-time capable multi-sensor framework presented tested to fuse on object list level from distributed automotive (camera...

A theoretical framework to implement multi-sensor data fusion methods for kinematic quantities is proposed. All defined through the allow combination of signals obtained from position, velocity and acceleration sensors addressing same target, improvement in observation kinematics target. Differently several alternative methods, considered ones need no dynamic and/or error models operate can be ...

In multi-sensor data fusion (or sensor fusion), sensor biases (or offsets) often affect the accuracy of the correlation and integration results of the tracking targets. Therefore, to estimate and compensate the bias, several methods are proposed. However, most methods involve bias estimation and sensor fusion simultaneously by using Kalman filter after collecting the plot data together. Hence, ...

In order to study non cooperative IFF to technology, this paper detailed illustrates the D-S theory in a multi-sensor data fusion principle and target recognition method, calculation method and practical model is established. By applying the theory of evidence (D-S method), we can solve the problem of data fusion in multi-sensor conditions. Aiming at the problem of multi-sensor target recogniti...

This chapter addresses the problem of data gathering with multi-attribute fusion over a bandwidth and energy constrained wireless sensor network (WSN). As there are strong correlations between data gathered from sensor nodes in close physical proximity, effective in-network fusion schemes involve minimizing such redundancy and hence reducing the load in wireless sensor networks. Considering a c...