IEEE Transactions on Intelligent Transportation Systems
Intelligent distributed architecture (IDA) for mobile sensor data fusion
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
TOA estimation and data association for through-wall tracking of moving targets
EURASIP Journal on Wireless Communications and Networking - Special issue on radar and sonar sensor networks
VLOCI2: improving 2D location coordinates using distance measurements in GPS-equipped VANETs
Proceedings of the 14th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
LICA: robust localization using cluster analysis to improve GPS coordinates
Proceedings of the first ACM international symposium on Design and analysis of intelligent vehicular networks and applications
Finding lower bounds of localization with noisy measurements using genetic algorithms
Proceedings of the first ACM international symposium on Design and analysis of intelligent vehicular networks and applications
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Radar sensors in the 24- and 77-GHz frequency domain will be used to increase comfort and safety in many future automotive applications. In this paper, a radar network with four short-range radars is considered. Each sensor measures individually only the range information of all targets inside the observation area. The Cartesian coordinates of each target are calculated by a trilateration technique based on range measurements selected in a data-association procedure. Estimating a target position based on range measurements is called trilateration. In contrast to this, estimation of a target position based on pure angular measurements is called triangulation. In automotive applications, situations with multiple targets almost always occur. Therefore, a high-performance data association is very important to separate and to distinguish between these targets. To avoid errors in the data-association step and resulting ghost targets, this paper describes a technique that combines the procedures of data association and position estimation into a single step. This signal-processing technique shows very good results in multitarget situations and reduces the number of ghost targets drastically.