Clustered multidimensional data association for limited sensor resolutions

The multidimensional data association methods were developed to establish the relation between measurements and tracks especially in dense target situations. However, even these advanced multidimensional data association methods lack in situations of unresolved measurement. Specifically, in real dense target situations being of most interest the phenomena of unresolved measurements happens quite often due to the limited sensor resolution. The new algorithm presented in this paper incorporates the unresolved measurement hypothesis into the multidimensional data association approach. An additional feature of this approach is, that also group tracking aspects are considered, which is an essential difference to proposals of other authors. Therefore, this new approach significantly increases the data association result as well as track accuracy, continuity and ensures early track initialization capabilities.