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].
Kalman-based tracker for multiple radar targets
Automotive radars are used for driver assistance systems such as Cruise Control, Lane Change Assistant or Vulnerable Road User (VRU) protection. In all these applications the signal processing architecture must consider target detection, data association and tracking for the proper analysis of the realistic traffic situation and for applying the correct safety measures. This paper proposes a data association and tracking algorithm based on the Hungarian association procedure and Kalman filtering. The simulations show that this scheme can be used in multiple target situations.
A real-time close-range imaging system with fixed antennas
A coherent system with two stepped frequency radar sensors separated by 1-2 m is presented in this paper for close-range imaging. Resolution is achieved via range resolution from the two different sensor positions, exploiting both monostatic and bistatic responses. A first test had been performed using a vector measurement equipment for an antenna measurement range as a radar simulator, and a mobile experimental system was built and tested, which included stationary and mobile targets.