Investigation of Target Motion Analysis in the Presence of Model Uncertainty
DEFENCE SCIENCE AND TECHNOLOGY ORGANISATION EDINBURGH (AUSTRALIA) MARITIME OPERATIONS DIV
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This paper presents the results of an investigation of target motion analysis algorithms that are designed to cope with model uncertainty. First, some standard recursive algorithms such as the cartesian extended Kalman filter, modified polar extended Kalman filter, and cartesian unscented Kalman filter are applied to a target motion analysis problem with model uncertainty, in order to analyse the robustness of such algorithms in these conditions. Next, some adaptive algorithms are investigated. They are the static multiple models and the dynamic multiple models estimators, namely two generalised pseudo Bayes methods and the interacting multiple model method. In this paper, the problem is restricted to a single sensor and a single non-manoeuvring target that travels at constant velocity. Both static and dynamic sensor performances are considered. For simplicity, only Gaussian measurement noise is considered. Adaptive filters are shown to have promise they can establish a useful bearing standard deviation adaptively and robustly.
- Numerical Mathematics
- Statistics and Probability
- Target Direction, Range and Position Finding