Track-Before-Detect of Multiple Slowly Moving Targets

We describe a new approach for combining range and Doppler data from multiple radar platforms to perform multi-target detection and tracking. Increasing the number of sensors can cause data association by conventional means to become impractical due combinatorial complexity, i.e., an exponential increase in the number of target to signature mappings. If the azimuthal resolution is coarse, this problem will be exacerbated by the resulting overlap between signatures from multiple targets and clutter. Our approach avoids combinatorial complexity during data association by performing an efficient optimization in the space of all target tracks and mappings between tracks and data. The reduced computational complexity of our approach scales only linearly with increasing numbers of targets and sensors. As a proof-of-concept, a simplified (single-sensor, range-only) version of the algorithm is tested on experimental radar data acquired with a stretch receiver. These results are promising, and demonstrate a surprising degree of robustness in the presence of nonhomogeneous clutter. Also the full, multi-sensor, version of the algorithm is tested on synthetic data. These results demonstrate that very accurate tracks can be estimated by exploiting spatial diversity in the sensor locations. The algorithm appears to be robust in the presence of clutter and uncertain knowledge regarding the number of targets present.