Automotive radar target tracking by Kalman filtering
Automotive radars have been introduced successfully into the consumer market with sensors in the 24- and 77-GHz domain [1]. In order to increase traffic safety, an application for detecting and tracking Vulnerable Road Users (VRU) and vehicles in front of the car is currently under development. By using a single 24-GHz radar sensor, target parameters like range, radial velocity and azimuth angle are measured even in multiple target scenarios. This paper describes the current implementation progress of a signal processing scheme for target tracking by Kalman filtering and presents tracking results using real measurement data. The work is developed under the FP7 EC funded project ARTRAC [2].
A Method for Improving Target Tracking Accuracy of Vehicle Radar
Compared with other vehicle sensors, the vehicle radar has strong adaptability and it can detect targets in the harsh environment, which make the vehicle radar technology become popular in the field of vehicle sensors. However, there will be a big error in target tracking while detecting targets on the road through vehicle radar. In order to solve this problem, this paper has present a method which uses Elman neural network to study historical trajectory of the target and then predicts the next coordinates of this target. At last the improved nearest neighbor method is used to remove false alarms by combining information of the predicted coordinates and the measurement coordinates, so the target tracking can be finished. The Matlab simulation results prove that the method can improve the target tracking accuracy of vehicle radar.
Creating the Virtual Universe
Simulation software is important to our understanding of the universe. The intrinsic multiphysics aspects are spiced with a range of temporal scales and spatial scales, both of which cover more digits than are available in the standard hardware. This, together with the intrinsic chaotic nature of many physical processes, poses quite a challenge. To meet this challenge, researchers developed the Astronomical Multipurpose Environment (AMUSE). Instead of writing a suite of multiphysics solvers from scratch, AMUSE's developers coupled existing solvers for each physical ingredient. The result is a highly inhomogeneous collection of dedicated solvers with a homogeneous protocol that scales to supercomputers.