Passive location of the nonlinear systems with fuzzy uncertainty

With the merits such as hiding receiving, easy-deploying and high security, passive location has attracted more and more attention and plays an important role in fields as diverse as navigation, location, and tracking, etc. However, the current filters models for passive location methods are most under the framework of the probability theory, thus they can not estimate the state in some passive location with fuzzy uncertainty accurately. Although the fuzzy extended Kalman filter (FEKF) can deal with the fuzzy uncertainty, it unavoidably introduces truncation error. In this paper, based on the FEKF and the iterated extended Kalman filter (IEKF) principle, a new fuzzy passive location model is built, and moreover, an iterated fuzzy extended Kalman filter (IFEKF) is proposed for estimating the target state. Compared to the FEKF and the IEKF, the proposed algorithm can not only reduce the truncation error, but also deal with fuzzy uncertainty. Moreover, it is proved that the IFEKF update is an application of the Gauss–Newton method. Then, a fuzzy passive location algorithm is proposed. Simulation results demonstrate that the proposed approach has better estimation precision than the traditional fuzzy extended Kalman filter.