Abstract: Aiming at the problems of heavy calculation burden and slow positioning of the Maximum Mikelihood (ML) estimation method for the single-station passive localization via phase difference change rate measurement, this paper proposes a high-precision, low-complexity estimation method using Monte Carlo Importance Sampling (MCIS) technology. According to the Pincus theorem, the approximate global solution of the ML problem is derived. The Importance Sampling (IS) technique is used to construct the importance function that conforms to the Probability Density (PDF) of the Gaussian distribution, which is regarded as the basis for sample selection. The sample set is obtained by inverse transform sampling, and the estimation result of the radiation source position is directly derived by the statistical sample mean. With low sensitivity to the initial estimation error of the target position, the MCIS method is simple and easy to implement with low computational complexity, thereby avoiding the large time consumption of the traditional ML estimation multi-dimensional grid search. Simulation results show that the MCIS algorithm has better positioning accuracy than the Extended Kalman Filter (EKF) and Nonlinear Least Square (NLS) algorithms at the same noise level, and effectively reduces the influence of the initialization estimation error on the positioning accuracy of the iterative algorithm. The influence of the algorithm parameters and different observation conditions on the positioning performance is further discussed and analyzed.

Key words: passive localization, importance sampling, phase difference change rate, maximum likelihood estimation, inverse transform sampling