Various Topics on Angle-Only Tracking using Particle Filters
Rickard Karlsson · 2002
Angle-only tracking estimates range and range rate from measured angle information by maneuvering the observation platform to gain observability. Traditionally, linear or linearized models are used, where the uncertainty in the sensor and motion models is typically modeled by Gaussian densities. Hence, classical sub-optimal Bayesian methods based on linearized Kalman filters can be used. The sequential Monte Carlo method, or particle filter, provides an approximative solution to the non-linear and non-Gaussian estimation problem. The particle filter approximates the optimal solution, hence it can outperform the Kalman filter in many cases, given sufficient computational resources. In an air-to-sea application it is shown how to incorporate terrain induced constraints using a terrain database. The algorithm is also successfully evaluated on experimental sonar data acquired from a torpedo system.