RF Emitter Geolocation using Amplitude Comparison with Auto-Calibrated Relative Antenna Gains

We consider an RF emitter localization problem that consists of a stationary RF emitter as a target and a moving RF receiver as an observer. An antenna system with unknown bias is deployed in the RF receiver to find the target direction of arrival (DOA) or bearing. This problem is conventionally solved by two separate stages, namely, to estimate target bearings through a relative amplitude direction finding (DF) algorithm, and to find the emitter location from the estimated bearings. The drawback of such a structure is that it is difficult to identify antenna bias through the DF algorithm alone, and the bearing error caused by antenna bias can significantly decrease the localization accuracy, especially when a target is located in longer range. To overcome this drawback, we merge the two stages together to formulate the localization problem as a discrete dynamic estimation problem. Thus, the target location and the antenna bias can be estimated simultaneously. Two algorithms are developed to cope with the merged system. The first algorithm, which is named as A-UKF, uses an unscented Kalman filter (UKF) to estimate the target location and the antenna bias in an augmented state. The second algorithm called MM-UKF further improves the A-UKF by introducing a multiple model (MM) approach to overcome the problem caused by an inaccurate initial state. The inaccurate initial state can seriously affect the performance of subsequent nonlinear estimation. The results show that both proposed algorithms are obviously superior to the conventional two-stage localization algorithm, and the MM-UKF algorithm outperforms the A-UKF.