Application of Particle Filter using TA Bearing Measurements

Background/Objectives: An Algorithm, the Particle filter, is proposed for implementing the bearings only Torpedo Motion Analysis (TMA). The required density of the state vector is represented as a set of random samples, which are updated and propagated by the algorithm. The method is not restricted by assumptions of linearity or Gaussian noise. Methods/Statistical analysis: The particle filter is combined with Modified Gain Bearings Only Extended Kalman Filter and the results are compared with that of Extended Kalman Filter or Unscented Kalman Filters. Findings: Almost similar performance is obtained. The algorithm is applied to track a torpedo using measurements available from towed array. Application/Improvements: The results in simulation mode and with sea trial data are presented.