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Continuous-Discrete Filtering using EKF, UKF, and PF
| Content Provider | CiteSeerX |
|---|---|
| Author | Mallick, Mahendra Mihaylova, Lyudmila Morel, Mark |
| Abstract | Abstract—Continuous-discrete filtering (CDF) arises in many real-world problems such as ballistic projectile tracking, ballistic missile tracking, bearing-only tracking in 2D, angle-only tracking in 3D, and satellite orbit determination. We develop CDF algorithms using the extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter (PF) with applications to the angle-only tracking in 3D. The modified spherical coordinates are used to represent the target state. Monte Carlo simulations are performed to compare the performance and computational complexity of the proposed filtering algorithms. Our results show that the CDF algorithms based on the EKF and UKF have the best state estimation accuracy and nearly the same computational cost. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Ballistic Projectile Tracking Particle Filter Extended Kalman Filter State Estimation Accuracy Computational Cost Bearing-only Tracking Ballistic Missile Tracking Cdf Algorithm Angle-only Tracking Monte Carlo Simulation Abstract Continuous-discrete Filtering Filtering Algorithm Many Real-world Problem Modified Spherical Coordinate Unscented Kalman Filter Target State Continuous-discrete Filtering Satellite Orbit Determination Computational Complexity |
| Content Type | Text |
