Multi-target tracking for merged measurements of automotive narrow-band radar sensors

In this paper, the conventional JPDAM (joint probabilistic data association for merged measurements) algorithm for modelling merged observations, is applied to the automotive environment. As every sensor has limited resolution, the JPDAM should be used instead of the JPDA algorithm, which does not model the effects of measurement merging. As the JPDAM algorithm is more complicated, these effect are normally neglected for most sensors with good resolution capability. In this paper a new automotive prototypical multi-beam monopulse narrow-band FMCW radar sensor is used. While this sensor has good detection accuracy, it has only limited resolution capability. For this sensor, measurement merging leads to wrong target estimations when not modelled correctly in the multi-target tracking process. While the JPDAM algorithm has already been developed for this case, it can not be applied directly, as it does not model the diminished measurement accuracy when more than one measurement is in the same resolution cell. When using a gating procedure with standard measurement accuracy, the merged measurements will not be associated to the corresponding targets. This motivates the usage of a modified gating method which will be presented in this paper. To reduce the computational demands of the algorithm, a clustering technique for the modified gating procedure is shown. The modified algorithm is tested with real sensor data. The JPDA algorithm and the JPDAM algorithm are both applied to a challenging measurement merging scenario and the results of these algorithms are compared.