Joint track-to-track association and sensor registration at the track level
A joint approach is developed in this paper to simultaneously deal with the problem of track-to-track association and sensor registration at the track level. In previous research, it is usually supposed that sensor biases are directly imposed on local estimates, and only relative biases of sensors can be estimated. However, for some practical sensors such as the radar sensor, the measurement process is implemented in the local polar coordinate system. Thus, sensor biases are imposed on sensor measurements and included implicitly in local estimates represented in the global Cartesian coordinate system. In our previous work, a pseudo-measurement equation based on the first-order Taylor series expansion was derived revealing the relationship explicitly between local estimates and sensor biases. In this paper, by assuming that sensor biases are imposed on the original sensor measurements, we construct a novel mixed integer nonlinear programming (MINLP) model in the maximum likelihood rule. The model serves to determine the correspondence between local tracks and provide an access to estimate the absolute sensor biases in a recursive way. Several heuristic solution methods, including Single-start, Gaussian Multi-start, K-best are implemented to handle the resulting MINLP model. Performance comparisons and analyses are made to illustrate the efficiency of the proposed approach.
track-to-track association sensor registration sensor biases mixed integer nonlinear programming (minlp)