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Multi-Target tracking is a central aspect of modeling the surrounding environment of autonomous vehicles. Automotive millimeter-wave radar is a necessary component in the autonomous driving system. One of the biggest advantages of radar is it measures the velocity directly. Another big advantage is that the radar is less influenced by environmental conditions. It can work day and night, in rainy or snowy conditions. In the expressway scenario, the forward-looking radar can generate multiple objects, to properly track the leading vehicle or neighbor-lane vehicle, a multi-target tracking algorithm is required. How to associate the track and the measurement or data association is an important question in a multi-target tracking system. This paper applies the nearest-neighbor method to solve the data association problem and uses an extended Kalman filter to update the state of the track.

Based on the vector space decomposition (VSD) transformation, the phase currents of the asymmetric dual three phase permanent magnet synchronous motor (ADT-PMSM) can be mapped into three orthogonal subspaces, i.e., α-β subspace, x-y subspace and O1-O2 subspace. The mechanical energy conversion takes place in the α-β subspace, while in the x-y and O1-O2 subspaces only losses are produced. With neutral points being isolated, O1-O2 subspace can be omitted. So the vector control algorithm can control the α-β and x-y subspaces separately to realize the four dimensional current control. In the α-β subspace, deviation decoupling control method is employed to realize the mechanical energy conversion, which is robust to the motor parameters.