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Karishma, S. K. B.
- Application of Particle Filter using TA Bearing Measurements
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Authors
Affiliations
1 School of Electrical Sciences, KL University, Vaddeswaram, Guntur – 522502, Andra Pradesh, IN
1 School of Electrical Sciences, KL University, Vaddeswaram, Guntur – 522502, Andra Pradesh, IN
Source
Indian Journal of Science and Technology, Vol 9, No 7 (2016), Pagination:Abstract
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.Keywords
Algorithm, Estimation, Gaussianity, Kalman Filter, Linearity, Simulation, Towed Array- Passive Target Tracking using Intercept Sonar Measurements
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Authors
Affiliations
1 KarishmaSchool of Electrical Sciences, KL University, Vaddeswaram – 522502, Andhra Pradesh, IN
1 KarishmaSchool of Electrical Sciences, KL University, Vaddeswaram – 522502, Andhra Pradesh, IN
Source
Indian Journal of Science and Technology, Vol 9, No 12 (2016), Pagination:Abstract
Intercept sonar of ownship is used to track a target, which is assumed to be doing active transmission for detecting a target in underwater. The ownship intercepts the active transmissions and generates bearing measurements of the target. The measurement interval between generated bearings in intercept mode is not constant and so closed loop estimators like Kalman filter is not useful to find out target motion parameters. So, sub-optimal estimator like Pseudo Linear Estimator (PLE) is used. Recursive PLE developed by S. K. Rao is modified to suit this application. The algorithm is tested in Monte-Carlo simulation and its results are presented for two typical scenarios.