In this study, an enhanced approach for automotive radar systems is proposed to solve the detection, tracking, and track management problem in the presence of clutter with high accuracy and low computational cost. The...
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In this study, an enhanced approach for automotive radar systems is proposed to solve the detection, tracking, and track management problem in the presence of clutter with high accuracy and low computational cost. The unscented Kalman filter (UKF) with a constant turn rate and acceleration (CTRA) dynamic model is employed for target tracking, and the tracking accuracy is enhanced by incorporating the linear regression (LR) algorithm into the UKF-CTRA algorithm. We investigate, for the first time, the jointprobabilisticdataassociation (JPDA) algorithm for dataassociation, and the composite M/N tests for track management. The capability of the proposed approach (CTRA-UKF-LR-JPDA-composite-M/N-tests) is demonstrated by comparing it with various algorithms for different single and multi-target tracking scenarios and for various sets of parameter regimes. The results show the superior performance of the proposed method over other existing techniques in automotive radar systems. This reveals the effectiveness of the proposed algorithm as a promising technique in automotive applications.
algorithms are presented for managing sensor information to reduce the effects of bias when tracking interacting targets. When targets are close enough together that their measurement validation gates overlap, the mea...
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algorithms are presented for managing sensor information to reduce the effects of bias when tracking interacting targets. When targets are close enough together that their measurement validation gates overlap, the measurement from one target can be confused with another. dataassociationalgorithms such as the jointprobabilisticdataassociation (JPDA) algorithm can effectively continue to track targets under these conditions, but the target estimates may become biased. A modification of the covariance control approach for sensor management can reduce this effect. Sensors are chosen based on their ability to reduce the extent of measurement gate overlap as judged by a set of heuristic parameters derived in this work. Monte Carlo simulation results show that these are effective methods of reducing target estimate bias in the JPDA algorithm when targets are close together. An analysis of the computational demands of these algorithms shows that while they are computationally demanding, they are not prohibitively so.
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