Simultaneous State and Parameter Estimation with Trajectory Shape Constraints (Poster)

In some tracking scenarios, the target state is subjected to equality constraints due to external limitations or inherent properties. If the constraints are known a priori, more accurate state estimates can be produced by taking advantage of these additional information in tracking algorithms. In this paper, a new model of the trajectory shape constraint is proposed when the target trajectory is known to be a straightline. The unknown slope and intercept of the straightline are treated as states to be estimated along with the target state. Then, two pseudo-measurements are constructed and augmented into the measurement equation in the filtering process. A trajectory shape constraint augmented state filter (TSC-ASF) is developed to produce constrained state estimates and constraint parameter estimates simultaneously. The nonlinear radar measurements and pseudo-measurements are processed by the converted measurement Kalman filter (CMKF) and unscented Kalman filter (UKF), sequentially. The unscented transform (UT) is employed to initialize the filter. Monte-Carlo simulation results illustrate the effectiveness of the proposed algorithm.