Abstract
A nonlinear target-maneuver detection and estimation algorithm, developed using concepts borrowed from failure detection theory, is presented. The algorithm is based on the assumption that target maneuvers are a piecewise constant process where transitions from one acceleration level to another occur relatively infrequently in time. The development is a two-step procedure using both separated-bias and non-Gaussian random transition theory developed by B. Friedland (1969, 1979). A nonlinear maneuver-detection algorithm is designed to signal when a target maneuver has actually occurred, and a linear separate-bias filter, designed under the assumption that the time of the target maneuver is known, is used to estimate the target acceleration vector. Simulation results are presented to demonstrate the efficacy of the procedure. Results indicate that the proposed technique is a viable approach to reduce terminal miss distance in air-to-air intercept scenarios against highly maneuverable targets.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 851-855 |
| Number of pages | 5 |
| Journal | Proceedings of the IEEE Conference on Decision and Control |
| State | Published - Dec 1988 |
| Externally published | Yes |
| Event | Proceedings of the 27th IEEE Conference on Decision and Control - Austin, TX, USA Duration: Dec 7 1988 → Dec 9 1988 |
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Modeling and Simulation
- Control and Optimization