Wireless Positioning System Key Algorithm

With the advancement of science and technology, wireless location develops rapidly. Especially, passive location of emitter has been widely used in the following fields such as navigation, aerospace, and electronic warfare because of its outstanding concealing and anti-jamming ability. Thus, wireless location has attracted more and more widespread attention in many countries.Based on the configuration of the four-satellite location system, this paper focuses on the algorithms and performance of passive emitter location and tracking technology. The main contents are as follows:a) To improve the location precision of time difference of arrival (TDOA) and frequency difference of arrival (FDOA), a joint localization algorithm of TDOA/FDOA based on selection combining (SC) and maximum likelihood (ML) is proposed. Simulation results show that the proposed joint method based on SC under earth constraint (EC) can achieve the joint Cramer-Rao lower bound (CRLB) and perform better than TDOA and FDOA.b) To track the locus of a moving target more effectively, the extended Kalman filter (EKF) is applied in the passive multi-satellites location system, and the strong tracking EKF (STEKF) is deeply investigated. The simulation results show that STEKF has a great adaptive ability of tracking the maneuvering target and is much better on performance than EKF.c) To improve the tracking precision of moving targets, a tracking method combining statistic learning (SL) and ML location based on TDOA is constructed. This method uses the ML grid search based on TDOA to obtain the initial locus of the moving target, and then use SL to fit the locus. The simulation results show that this method not only provides a higher precision than EKF, but also its precision is ten times that of TDOA without SL fitting.