International Symposium on Antennas and Propagation
Collision-Avoidance Algorithm with High Precision on Location, Velocity and Acceleration
Po-Jen Tu, Jean-Fu Kiang, Chia-Cheng Ho,
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Adaptive cruise control (ACC), parking aid, collision avoidance, and pre-crash detection require reliable calculation pending on the accurate estimation of target location, velocity and acceleration. Several methods have been proposed to trilaterate a specific target based on detecting these kinetic parameters from multiple signals. In , Klotz presents a method to trilaterate a target using multiple FMCW echoes in a least-square sense. Given the relative range measured at the sensor, a one-stage linear Kalman filter is used to estimate the relative range and relative velocity . However, the acceleration can not be estimated accurately when the target is making a turn in a short period. Furthermore, a two-stage linear Kalman filter has been built upon the one-stage linear Kalman filter, with a new bias vector to estimate the relative acceleration . Nevertheless, the convergence time will be too long if the target is making a turn in a short period. Hence, the extended Kalman filter is used to estimate all the kinetic parameters of the target using only one sensor . However, the error in some parameters can be very large when the target is making a turn. In this work, we propose a minimum-delay Kalman filter to reckon the kinetic paramaters by taking the measured FMCW (frequency-modulated continuous wave) echoes. A second Kalman filter is also used after trilateration to significantly reduce the convergence time for estimating the acceleration. Demonstration is given with a vehicle making a turn across the adjacent lane in front of the host vehicle. The trajectory of moving target with respect to the host can be calculated to predict the time for collision.