Abstract:

Due to the nonlinear relationship between single channel receiver measurements and radiation source states, it becomes challenging to use time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements for long-distance tracking, resulting in bias in state estimation on the second-order moment. To address this issue, a measurement conversion-based algorithm is proposed. It employs second-order Taylor expansion to approximate nonlinear observation equations, transforming TDOA/FDOA measurements into the emitter state space. Simultaneously, measurement covariance conversion is achieved to the extent permitted by second-order Taylor approximation, aiming to reduce the bias incurred during the conversion process. The simulation results show that the proposed algorithm has higher radiation source tracking accuracy compared to the first-order Taylor expansion approximation measurement conversion and the unscented Kalman filter, and the computational complexity is similar to that of the unscented Kalman filter. Therefore, the proposed algorithm has broad application prospects.

Key words: time difference of arrival (TDOA), frequency difference of arrival (FDOA), unbiased conversion measurement Kalman filter (UCMKF), second-order Taylor expansion