  Emitter localization is widely applied in the military and civilian _elds. In
this paper, we tackle the problem of position estimation for multiple
stationary emitters using Doppler frequency shifts and angles by moving
receivers. The computational load for the exhaustive maximum likelihood (ML)
direct position determination (DPD) search is insu_erable. Based on the Pincus'
theorem and importance sampling (IS) concept, we propose a novel non-iterative
ML DPD method. The proposed method transforms the original multidimensional
grid search into random variables generation with multiple low-dimensional
pseudo-probability density functions (PDF), and the circular mean is used for
superior position estimation performance. The computational complexity of the
proposed method is modest, and the o_-grid problem that most existing DPD
techniques face is signi_cantly alleviated. Moreover, it can be implemented in
parallel separately. Simulation results demonstrate that the proposed ML DPD
estimator can achieve better estimation accuracy than state-of-the-art DPD
techniques. With a reasonable parameter choice, the estimation performance of
the proposed technique is very close to the Cram_er-Rao lower bound (CRLB),
even in the adverse conditions of low signal-to-noise ratios (SNR) levels.
