Sensor Fusion and Optimal Platform Trajectory Planning for Ground Target Localization with Terrain Uncertainty and Measurement Biases

A ground target can be localized using an airborne angle-only sensor. However, possible measurement bias causes a delay in error convergence. Adding measurements from a range-only sensor can improve localization by attaining faster convergence. Estimation accuracy can be improved further by optimizing the trajectories of the platforms containing the angle-only and range-only sensors. To ensure observability in 3-D localization, in most of the recent works, the height of the ground target from the sea level is assumed to be known perfectly. However, in most practical applications, target height is obtained from a Digital Terrain Elevation Database (DTED), having multiple levels of resolution. As a result, in addition to the bias uncertainty, the terrain uncertainty is required to be handled. In this work, Cramer Rao Lower Bound (CRLB) is derived for the localization problem considering the terrain and measurement bias uncertainties. A CRLB based optimization algorithm is proposed for optimal platform trajectory planning. We propose two localization approaches to handle the biases. The first approach involves bias compensation using a prior whereas the target and bias states are estimated jointly in the second approach. In both approaches, range sensor fusion is proposed to improve localization accuracy. The effectiveness of our algorithms is verified using Monte Carlo simulations.