Abstract
The airborne bearing-only passive target localization performance is relevant with the specific filter and maneuver model. This paper presents a combination of Unscented Kalman Filtering (UKF) with control inputs and optimal route planning algorithm to improve the performance. Taking the minimum trace of unbiased UKF estimation covariance matrix as criteria, single-step heading traverse method is applied to reach every optimal solution. A single flight path simulation and Monte Carlo analysis validate that the optimal strategy can improve both the convergence and localization accuracy.
Highlights
Benefitting its flexibility and concealment, airborne single-observer localization is applied in interference investigation, radio rescue and military reconnaissance, etc
Noises are inevitably introduced into the bearing measurements, which are propagated to final location estimation
Since no linearization is required, Unscented Kalman Filtering (UKF) is more suitable for states estimation in nonlinear systems, and attracts more attentions [8]
Summary
Benefitting its flexibility and concealment, airborne single-observer localization is applied in interference investigation, radio rescue and military reconnaissance, etc. Since no linearization is required, UKF is more suitable for states estimation in nonlinear systems, and attracts more attentions [8] Another issue to be considered is that better observability should be maintained to ensure accurate bearing angles as possible. The combination of an UKF filter and an optimal maneuver strategy is presented to improve convergence and accuracy of the airborne single-observer localization algorithm. In this strategy, taking the trace of covariance as the control criterion, UKF determines the optimal maneuvering path for better observability, resulting improved emitter localization
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