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].
Waveform design principles for automotive radar systems
This paper presents a high performance 77 GHz FMCW radar sensor for automotive applications. Powerful automotive radar systems are currently under development for various applications. Radar sensor based comfort systems like Adaptive Cruise Control (ACC) are already available on the market. The main objective from a radar sensor point of view is to detect all targets inside the observation area and estimate target range and relative velocity simultaneously with a high update rate. FMCW radar sensors have the advantages of very high range resolution but a serious task occurs in multiple target situations to suppress so-called ghost targets. In classical FMCW waveforms this has been solved in using multiple chirp signals with different slope. But in this case a long measurement time (approximately 50 ms) is needed which is a contradiction to a high update rate. Therefore, in this paper a new waveform design is presented which has all advantages of FMCW radars but needs an extremely low measurement time (10 ms) in a radar sensor with 3 different antenna beams.
Prediction of a representative point for rail temperature measurement by considering longitudinal deformation
Monitoring rail temperature is very important for determining the safe running speed of trains and to prevent buckling. In general, the maximum variation of the internal rail temperature can be >7 ℃ depending on the point of measurement. However, there is as yet no sufficient information about how to predict the measurement point to represent the thermal deformation due to temperature distribution. In this study, the authors report a new point, called the representative measurement point, at which the rail temperature can be measured. This point considers the average deformation of the rail through structural analysis by adopting experimental and actual rail temperature data. The authors designed and installed a measurement system similar to an actual rail environment. Using the system, various data were acquired (internal/surface rail temperature and weather data) for 10 months. On the basis of these data, an analysis was done to calculate the average deformation point through thermal analysis. Finally, the representative measurement point was proposed as the position at which the average deformation point converges regardless of weather or seasons. The authors believe that the method described herein is advantageous in that it could be used in a high-accuracy temperature-monitoring system and for predicting thermal deformation and buckling.